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Hellixia is the name of BayesiaLab's subject matter assistant powered by Generative AI. Hellixia offers a wide range of functions to help you characterize a given problem domain:
Identify relevant dimensions of a problem domain
Extract dimensions from a text
Generate embeddings for learning a semantic network
Generate meaningful descriptions for classes of nodes
Provide tools for causal analysis
Translate names and comments of nodes into different languages
Generate images to be associated with nodes
BayesiaLab integrates functionality provided by OpenAI's ChatGPT, a machine learning-based service for compiling human knowledge obtained from the Internet. However, Bayesia* and its affiliates are not affiliated with OpenAI.
Bayesia* makes no representations or warranties of any kind, express or implied, about the completeness, accuracy, reliability, suitability, or availability of the ChatGPT feature. Therefore, any reliance on such information is strictly at your own risk.
In no event will Bayesia* be liable for any loss or damage, including indirect or consequential loss or damage, arising out of, or in connection with, the use of ChatGPT through BayesiaLab.
Please note that the responses generated by ChatGPT are created by a machine-learning model and do not reflect the opinions or policies of Bayesia*.
ChatGPT may sometimes produce inappropriate or offensive content. While OpenAI states that mechanisms exist in ChatGPT to reduce such occurrences, Bayesia* has no control over the delivery of such content and cannot prevent such instances.
*References to "Bayesia" include Bayesia S.A.S. and its affiliates Bayesia USA, LLC, and Bayesia Singapore Pte. Ltd.
Semantic Text Analysis closely mirrors the process of identifying the dimensions of a particular subject (see Dimension Elicitor).
To illustrate Semantic Text Analysis, we selected Dr. Martin Luther King's famous speech, I Have a Dream:
To start the process, we open a new graph and add a single node.
By default, the name of the new node is N1. However, we can change the name to a more descriptive title, e.g., "I Have a Dream."
This node will host the content we wish to analyze.
We now need to enter the speech as a Node Comment.
From the Node Contextual Menu, select Edit
, then select the Comment
tab.
Now paste the speech into the text field.
Note that the Node Comment can accommodate any text length, whereas the Node Name and the Node Long Name are limited.
This icon indicates that a Node Comment is associated with this node.
As the first formal step in the Semantic Text Analysis, we need to use the Dimension Elicitor again.
Select the node of interest, which is I Have a Dream.
Select Main Menu > Hellixia > Dimension Elicitor
.
The Dimension Elicitor window opens up, in which we need to specify several settings:
Question Settings
Keyword: We select Causes, Achievements, and Objectives from the list of Keywords.
Groups: With Groups, we can bundle several keywords to be easily retrieved later when the analysis needs to be repeated. We name the group of our four keywords "Civil Rights."
Responses per Keyword specifies the maximum number of items to be retrieved per Keyword.
Exclude Duplicates automatically removes duplicates from the list of results. This is helpful as the query can produce identical Dimensions in the context of different Keywords.
Completion Model: From the drop-down menu, the following models are available:
GPT_35_TURBO
GPT_35_TURBO_16K
GPT_4
Context
Knowledge File: This text file allows you to specify a broader context for a query. For example, you might embed chunks of documents related to your domain of study into a dataset. Then, you can identify and use the chunks with embeddings closest to that of your query to construct your knowledge file.
General Context: Checking the box and entering a heading provides relevant context. In our example, we use the title "Civil Rights Movement."
Subject of the Query
Checkboxes for Node Name, Node Long Name, and Node Comment are available.
In this example, however, the relevant subject is only stored in the Node Comment, i.e., the entire speech, I Have a Dream.
Options
By checking Create a Class per Keyword, BayesiaLab assigns all newly-discovered dimensions to new BayesiaLab Classes.
Submit Query
Clicking the Submit Query button starts the research.
Upon completing the research, the table at the bottom of the window lists all discovered dimensions and provides a corresponding comment.
The checkboxes at the end of each row allow you to select whether or not to keep the found Dimensions and add them to the Graph Panel. This allows you to override the default selection of all Dimensions.
Click OK to add the dimensions to the Graph Panel.
The Dimensions are now shown as nodes on the Graph Panel.
Furthermore, if you select the option Create a Class per Keyword, the Dimension nodes are grouped based on their associated Keyword. Additionally, a Note is added to visually group each set of nodes that corresponds to a particular Keyword/Dimension.
Semantic Variable Clustering groups nodes based on the semantics of their Node Names.
For this example, we use a list of 49 positive character traits.
All character traits are represented by nodes in an unconnected Bayesian network.
The nodes are named after character traits; no other information is available, e.g., in the Node Long Names or the Node Comments.
Select all nodes you wish to cluster.
To start the Semantic Variable Clustering, select Main Menu > Hellixia > Semantic Variable Clustering
.
In the Semantic Variable Clustering window, you can specify the following item:
Your Completion Model, which depends on your OpenAI subscription
The Context that may apply to the nodes to be clustered
The Maximum Number of Clusters allows you to limit how many clusters are generated.
Clicking OK initiates Hellixia's communication with ChatGPT.
Upon completing the task, BayesiaLab presents the Semantic Variable Clustering Report in a new window.
With the Comment Generator, Hellixia retrieves Dimension Names and the related Comments from ChatGPT and adds them automatically to the Node Comment.
Create a node representing the subject of interest, e.g., "Judea Pearl."
Move your pointer to the desired location to place your new node on the Graph Panel.
Give the node a meaningful name representing the subject to be studied, i.e., "Judea Pearl."
You can also add a Node Long Name and a Node Comment to provide more information.
Select the newly-created node, and then select Main Menu > Hellixia > Comment Generator
, which brings up the Comment Generator window.
There is a range of settings you need to specify in the Comment Generator window:
Under Question Settings,
Specify the Keyword from the dropdown menu.
If needed, stipulate the maximum number of responses per Keyword.
Select the Completion Model from the dropdown menu, e.g., GPT_35 or GPT_4.
Under Context,
Open a Knowledge File, if available.
Provide a General Context for the query. In our example, use "Artificial Intelligence."
Under Main Subject of the Query, select all fields that contain relevant information for the query, i.e., Node Name, Node Long Name, and Node Comment. Check all that apply. Both the Node Long Names and Node Comments are optional properties. If they're selected but not defined for a given node, Hellixia will use the Node Name by default.
Click Submit Query, and Hellixia retrieves the responses from ChatGPT and lists them in a table at the bottom of the Comment Generator window.
The Subject Node column displays the main subject of the query.
The Keyword column lists the keyword used for the Dimension retrieved in that row.
The Index column assigns an index to each Dimension retrieved for a Keyword.
The Comment column further describes the Dimension retrieved.
The Keep column indicates which Keyword/Dimensions row to keep.
Under Output Settings, specify what part of the results table will be added to the Node Comment.
Checking Dimension Name and Comment and Concatenate Output to Current Comment, you will obtain a Comment like this, which you can see in the Node Editor.
In machine learning and Natural Language Processing (NLP), embedding is a mathematical representation of a token, word, phrase, sentence, or any other linguistic unit with a continuous high-dimensional vector. Word embeddings, in particular, are widely used representations that capture the semantic and syntactic properties of words.
The embeddings used by Hellixia have 1,536 dimensions and allow capturing the semantics of the linguistic units defined by the nodes (names, long names, comments).
To demonstrate the workflow for generating embeddings, we start with a set of 54 nodes representing a selection of influential 19th and 20th-century painters.
Go to Main Menu > Hellixia > Embedding Generator
.
Select one or more Input Types from the Hellixia Embedding Generator Window, i.e., Node Name, Node Long Name, and Node Comment. In the example, only Node Names are defined, so that is the only Input Type you need to select.
Click OK.
Each node now has 1,536 observations, which is indicated by the Tooltip associated with the database icon.
A semantic network is a graphical representation of knowledge or concepts organized in a network-like structure. It is a form of knowledge representation that depicts how different concepts or entities are related to each other through meaningful connections.
In a semantic network, concepts are represented as nodes, and their relationships are depicted as labeled links or arcs. These links indicate the connections or associations between the concepts, such as hierarchical, associative, or causal relationships.
With the embeddings now stored as observations, we can machine-learn a semantic network.
For this purpose, we use one of BayesiaLab's Unsupervised Learning algorithms.
The Maximum Weight Spanning Tree (MWST) is the best choice in this context. The algorithm is quick and renders an easily interpretable network.
After the learning is completed, the resulting network appears in the following screenshot:
We can apply one of BayesiaLab's layout algorithms to interpret this graph more easily.
For instance, select Main Menu > View > Layout > Symmetric Layout
.
The resulting graph is shown outside the BayesiaLab window so that its structure can be viewed and interpreted more easily.
To this day, no reliable methods exist to find causal relationships in data. Given a statistical association between two variables, it is impossible, based on data alone, to establish which variable is the cause and which is the effect.
As a result, acquiring additional external information, such as human expert knowledge or the temporal order of the variables, remains necessary to determine the causal direction in bivariate relationships.
With ChatGPT, it is now possible to let BayesiaLab tap into external domain knowledge. BayesiaLab's Hellixia can ask ChatGPT about the causal relationship between two nodes.
Select two nodes of interest, e.g., Smoking and Lung Cancer.
Select Main Menu > Hellixia > Causality Search
In the Causality Search Window:
Specify the Completion Model.
Provide any applicable context to the Context field.
Check which fields contain the subjects under study, e.g., Node Name, Node Long Name, and Node Comment.
Click OK to launch the search.
If ChatGPT believes a causal relationship exists, BayesiaLab adds a corresponding arc.
On the Toolbar, click on the Node icon , and place a new node on the Graph Panel.
After entering the speech and closing the Node Editor, the Information icon appears next to the node name:
An Information icon is attached to each node. This means that the Comments generated by the Dimension Elicitor are stored as Node Comments.
These newly-created clusters are now represented as Classes, indicated by the Classes icon .
Hellixia's Comment Generator is similar to the .
In the case of the , Hellixia creates new nodes.
Select Toolbar > Node Creation Mode
In Modeling Mode , select the nodes on the Graph Panel for which you want to generate embeddings. In our example, we select all 54 nodes.
Upon retrieving the embeddings, the Main Window shows the database icon in the bottom right corner. This indicates that the embeddings are now attached as a dataset.
By default, the observations associated with each node are discretized into quintiles, which you can see by switching into Validation Mode and bringing up any of the Monitors.
From the Modeling Mode , select Main Menu > Learning > Unsupervised Structural Learning > Maximum Spanning Tree
.
Furthermore, BayesiaLab adds an Arc Comment with any contextual information ChatGPT provides. The Arc Comment icon indicates that such a comment was added.
Clicking the Show Arc Comment button in the Toolbar displays the comment.
To manage groups of nodes, BayesiaLab offers Classes.
Nodes can be added to Classes manually or automatically. For instance, the Variable Clustering function can assign nodes to new Classes representing latent factors. By default, newly-created Classes have generic names, such as [Factor_0], which carries no meaning.
Finding suitable descriptions for Classes can be time-consuming.
The Class Description function can assist you in finding meaningful summaries of a Class of nodes.
With the Hellixia Class Description Generator, we can quickly find a useful description for a subset of nodes we select.
In our example, we have a large number of nodes from an auto buyer satisfaction survey.
We are interested in a subset of nodes related to the quality perception of the vehicle interior, i.e.:
Interior Colors
Quality of Interior Materials
Interior Trim & Finish
Quality of Seat Materials
Select these nodes node of interest.
Then select Main Menu > Hellixia > Class Description
.
Specify a Context, if applicable.
Indicate by ticking the checkboxes where the subject matter is stored, i.e., Node Name, Node Long Name, or Node Comment. Check all that apply.
Clicking OK starts generating the Class Description.
The chime confirms when the process is complete.
Opening the Class Editor shows the Class Description that was generated.
Select Graph Contextual Menu > Edit Classes
The Description column shows the newly-generated Class Description.
BayesiaLab's Clustering function produces new Factors and associated Classes.
So, having a dozen or more new Classes is quite common in this context.
By default, the newly-generated Classes have generic and non-informative names, like [Factor_0], [Factor_1], etc.
Given that the Factors and Classes are meant to represent meaningful concepts, naming them is important but can be tedious.
In the following example, 57 Factors (and Classes) were created from 240 manifest nodes. Each manifest node measures the degree of agreement or disagreement with statements in a personality test, such as, "I get angry easily" or "I remain calm under pressure."
These original statements are included as Node Comments with every node.
Clicking Export produces a so-called Structural Prior Dictionary, which is a text file containing all arc attributes, i.e.,
Start and End of arc
Structural Prior for each arc
Arc Comment, which, in this context, contains the Explanation for the causal directions as obtained from ChatGPT.
We can now use this Structural Prior Dictionary as an Arc Dictionary and replace the original, machine-learned arcs with the ChatGPT-informed causal arcs.
First, select Graph Panel Contextual Menu > Delete All Arcs
to remove all existing arcs.
Then, select Main Menu > Data > Associate Dictionary > Arc >Arcs
.
The network now features the causal arc directions as obtained from ChatGPT.
With the final arc directions now in place, we should arrange the nodes into a more intuitive layout, i.e., positioning parent nodes above child nodes.
Select Main Menu > View > Layout > Genetic Grid Layout > Top-Down Repartition
.
The network now displays the correct causal order of nodes and arcs.
In Workflow 1, we exported a Structural Prior Dictionary, including the Causal Structural Priors, and then imported this dictionary as an Arc Dictionary to create a causal network with these priors.
In this Workflow 2, we will immediately utilize the Causal Structural Priors to machine-learning a new network without the export/import step.
However, these new Causal Structural Priors have not been used for updating the arc directions in the network.
Select Main Menu > Learning > Unsupervised Structural Learning > Taboo
.
Like Arc Constraints, Structural Priors, Temporal Indices, and Filtered States, Causal Structural Priors impose constraints on learning. As a result, EQ-based algorithms are not available under those conditions.
This newly learned network now reflects the causal order obtained from ChatGPT.
With the final arc directions in place, we should arrange the nodes into a more intuitive layout, i.e., positioning parent nodes above child nodes.
Select Main Menu > View > Layout > Genetic Grid Layout > Top-Down Repartition
.
With the Causality Analysis function, Hellixia allows you to retrieve domain knowledge from ChatGPT about a potential causal relationship between two nodes.
The Causal Structural Priors function extends this concept to more than two nodes.
We illustrate the Causal Structural Priors workflow with the well-known "Visit Asia" example from the domain of lung diseases.
We have a synthetic dataset from this domain, which has already been imported into BayesiaLab.
So, our starting point is an unconnected network, as shown in the following screenshot.
For instance, the node Smoking has an associated Node Comment that says, "The patient is a regular smoker."
Our objective is to find the causal relationships between risk factors, conditions, symptoms, and diagnostic imaging.
However, we know that machine learning alone cannot discover the true causal structure of this domain.
We begin with machine learning the associations between all nodes anyway and use the Unsupervised EQ learning algorithm for that purpose.
This newly-learned Bayesian network features directed arcs, but they can clearly not be interpreted as causal, e.g., Smoking could not possibly be a cause of Age.
Applying the Genetic Grid layout highlights the implausibility of the arc directions.
Select Main Menu > View > Layout > Genetic Grid Layout > Top-Down Repartition
.
In the past, we would have had to use any available domain knowledge from experts to correct the arc directions.
With Hellixia, however, we can tap into the domain knowledge available via ChatGPT.
So, select all arcs and then select Main Menu > Hellixia > Causal Structural Priors
.
In the Causal Structural Priors window, you need to specify a number of items:
Under Completion Model, choose a model for which you have a subscription, e.g., GPT_35 or GPT_4.
You can specify a General Context of the problem domain. In this example, "Lung Diseases" would be appropriate.
Under Subject of the Query, check all fields that contain information regarding the subject matter. We have information in the Node Name and the Node Comment in the example.
Clicking OK starts the search for causal relationships via ChatGPT. The progress bar at the bottom of the Graph Panel shows the search status.
A chime marks the completion of the search.
This table displays the causal arc directions obtained from ChatGPT in the three left columns.
The reason for the arc orientation is provided in the Explanation column.
Clicking Preview opens a window showing a simplified view of the causal arc directions proposed by ChatGPT.
Now, there are two ways to proceed, as illustrated in the following workflows 1 and 2.
Welcome to the vibrant section of our website dedicated to showcasing Hellixia's semantic network examples, where analysis takes on a new dimension. This part of our site is a hub for curious minds eager to explore the complex interconnections within various domains such as philosophy, literature, cinema, song lyrics, and more.
With the help of Hellixia, we unravel the intricate relationships between ideas, themes, characters, and authors. From examining the moral quandaries in philosophical works like Machiavelli's "The Prince" or Hobbes' "Leviathan" to uncovering the essence of Shakespeare's "Hamlet" and Flaubert's "Madame Bovary," our analyses reach new depths.
But our exploration doesn't stop at books. We venture into the world of cinema, dissecting masterpieces like "Apocalypse Now," and dive into the poignant lyrics of songs by artists such as Nick Cave. Through Hellixia's power, we bring to life semantic networks that vividly illustrate the multifaceted connections and underlying themes in these works.
Whether you're a lover of classic literature, a cinema enthusiast, or a philosopher at heart, this section invites you to explore, learn, and engage with content in a way that transcends traditional analysis. Join us in this exciting journey where technology and creativity intersect, providing unique insights and fostering a deeper understanding of the world around us.
In addition to utilizing ChatGPT, BayesiaLab's Hellixia subject matter assistant also employs DALL-E.
DALL-E is a variant of the GPT model designed to generate images from textual descriptions.
This functionality is useful for creating small images that visualize what the node represents.
To use the Image Generator, select the nodes for which you want an image produced.
Select Main Menu > Hellixia > Image Generator
.
In the Image Generator window, specify the fields that contain the subjects, i.e., the textual descriptions of the images to be generated. Check all that apply.
Under Context, you can state the overall domain of the image subjects, if applicable.
Note that the algorithm keeps searching for a better layout until you stop the process by clicking the red buttonto the left of the Progress Bar.
Clicking the Show Arc Comment button in the Toolbar displays the comments on the arcs. The Arc Comments show the explanations for the causal directions retrieved from ChatGPT.
So, our starting point is the machine-learned network, for which Hellixia has already obtained the Causal Structural Priors. The Structural Prior icon indicates that Structural Priors are associated with the network.
Note that the algorithm keeps searching for a better layout until you stop the process by clicking the red buttonto the left of the Progress Bar.
In addition to the descriptive and self-explanatory node names, Comments are associated with each node, as indicated by the information icon .
Note that the algorithm keeps searching for a better layout until you stop the process by clicking the red buttonto the left of the Progress Bar.
Furthermore, the Structural Priors icon appears in the bottom-right corner of the Graph Panel.
To view the Structural Priors obtained from ChatGPT, you can click on the Structural Priors icon or select Graph Panel Contextual Menu > Edit Structural Priors
.
The final column, Check, indicates whether the causal direction matches the current orientation or not .
The Hellixia Node Translator is powered by ChatGPT and DeepL.
It allows you to easily translate the Node Names, the State Names, and any Node Comments into another language.
We use an unconnected network featuring 240 statements that are all related to personality and character traits, e.g., "I get angry easily", or "I smile a lot".
These statements are contained in the Node Names.
The Node Names are in English, and we want to translate them into German.
Select all nodes to be translated.
Then, select Main Menu > Hellixia > Node Translator.
In the Node Translator window, you can pick the target language from the dropdown menu.
You can also specify the Translator Model, e.g., GPT-3.5, GPT-4, or DeepL.
Finally, you need to check what Node Properties should be translated, e.g., Node Names, State Names, or Node Comments. Check all that apply.
Clicking OK starts the translation process.
Once the process has concluded, all node names appear in German.
The third episode of our Philosophical Minute post is about the famous philosophical statement by René Descartes, Cogito Ergo Sum, "I think, therefore I am." This statement is at the core of Western philosophy and is the starting point of Descartes' philosophical methodology, the foundational element of his metaphysics.
Descartes sought a fundamental element that could be beyond any doubt as a basis for all knowledge. He posited that the very act of doubting one's own existence served as proof of the reality of one's own mind. In essence, if one is questioning, then one must exist to be able to do so.
Considering that all the same thoughts that we have while awake can also come to us when we sleep, without any of them being true at that time, I resolved to pretend that all the things that had ever entered my mind were no more true than the illusions of my dreams.
But immediately afterwards, I noticed that while I wanted to think that everything was false, it was necessary that I, who was thinking, be something; and realizing that this truth, I think, therefore I am, was so firm and so certain that even the most extravagant suppositions of skeptics were not capable of shaking it, I judged that I could accept it without hesitation as the first principle of the philosophy I was seeking.
Start by creating a new node. Label this node "Descartes".
Input the chosen excerpt of text into the comment section of the "Descartes" node.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Examine the dimensions or keywords that Hellixia has identified. Any dimensions that appear irrelevant or redundant should be removed from your analysis.
Use the Embedding Generator on all remaining nodes. This tool will quantify the semantics associated with the names and comments of each node.
Set the "Descartes" node as your Target Node.
Run the Naive Learning algorithm.
Update the visual style of all nodes to appear as "Badges". This will allow the comments within each node to be displayed.
Switch to Validation Mode.
Run an Arc Force analysis.
Use the Radial Layout while you are still within the Arc Force analysis tool. This will arrange the nodes in a clockwise fashion based on the strength of their relationships with the target node.
Show the Arc Comments to visualize information regarding the strength of the relationships between the nodes.
Start by copying the node "Descartes." Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Arguments, Contents, Matters, Milestones, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy."
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Descartes" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; note that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Michel de Montaigne's "Essais" is a large collection (3 books) of many short subjective treatments of various topics published in 1580. Montaigne's stated design in writing, publishing, and revising the "Essais" over the period from approximately 1570 to 1592 was to record "some traits of [his] character and of [his] humours." The "Essais" were seen as an important work that established the essay as a recognized genre in literature. This work can be qualified as introspective philosophy.
Montaigne's "Essais" is not just a foundational work in the history of ideas; it's also a unique insight into the mind of one of the most curious and open thinkers in Western history. His observations on society, culture, and humanity are as relevant today as in the 16th century.
We are launching our 'Philosophical Minute' series with an excerpt from Book 2, 'Apology of Raimond de Sebonde'. We believe this passage serves as the perfect introduction to the series, as it refers to the profound wisdom of Socrates.
The wisest man who ever lived, when asked what he knew, replied that he knew that he knew nothing. He confirmed what is said, that the greatest part of what we know is the least of what we do not know: that is to say, even what we think we know is a small part of our ignorance.
Create a new node: Start by creating a new node and label it as "Montaigne". This node will serve as a container for the text you want to analyze.
Enter the excerpt: Input the selected text into the "Montaigne" node as a comment.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Review the dimensions: Examine the dimensions or keywords returned by Hellixia. Remove any dimensions that seem redundant or irrelevant to your analysis.
Use the Embedding Generator on all remaining nodes. This tool captures and quantifies the semantics associated with the names and comments of each node.
Set the target node: Set "Montaigne" as the Target Node. The subsequent analyses and operations will focus on this node.
Run the Naive Learning algorithm.
Change node styles: Alter the style of all nodes to "Badges". This style will display the comment within each node.
Switch to Validation Mode.
Run the Arc Force analysis.
Apply the Radial Layout: While still in the Arc Force analysis tool, run the Radial Layout. This layout arranges the nodes in a clockwise manner according to the strength of their relationships with the target node.
Show the Arc Comments: These comments will provide information about the strength of the relationships between nodes.
Create a new node titled "Montaigne" that will contain the text we want to analyze.
Enter the excerpt as a comment within the "Montaigne" node.
Use the Dimension Elicitor with the General Context set to "Philosophy" and the keywords (Dimensions, Ideas, Themes, and Theses) to analyze the comment within your node. Review the dimensions that Hellixia returns, and remove any that appear to be redundant or irrelevant.
Apply the Embedding Generator to all remaining nodes, capturing the semantics related to their names and comments.
Exclude the "Montaigne" node.
Use the Maximum Weight Spanning Tree algorithm to create a semantic network that describes the analyzed text.
Change all node styles to Badges so that the comment within each node is displayed.
Apply the Dynamic Grid Layout to arrange the nodes.
Switch to Validation Mode.
Since the graph we're creating doesn't represent causal relationships, select the Skeleton View to remove any arc orientations.
Switch back to Modeling Mode.
Exclude the "Montaigne" node.
Change all node styles to the Discs format.
Enter Validation Mode.
Use the Symmetric Layout.
Analyze the Node Force.
Run Variable Clustering.
Open the Class Editor and utilize the Class Description Generator to assign meaningful names to the three factors you're dealing with.
Save these descriptions using the Export Descriptions feature.
Switch back to Modeling Mode.
Execute Multiple Clustering to create latent variables.
Run Taboo, enabling the option Delete Unfixed Arcs, to create a hierarchical network.
Rename the latent variables you've just created by using the previously exported descriptions as a dictionary for naming the node names.
Switch to Validation Mode.
Utilize the Node Force function.
In this second installment, we delve into a profound examination of yet another passage from Montaigne's work, 'Essais', the 'Liars' (Book I).
This is a formidable challenge for Hellixia, given that it relies on a translation from Old French.
When they disguise and change, when they are often put back on the same story, it is difficult for them not to make mistakes, because the thing as it is, having lodged itself first in memory and having been imprinted there by way of knowledge and science, it is difficult for it not to be represented in the imagination by dislodging the falsehood, which cannot have as firm and steady a foothold, and for the circumstances of the first learning not to cause the memory of the added, false or bastardized pieces to be lost. In what they invent completely, because there is no contrary impression that contradicts their falsehood, they seem to have all the less to fear to make mistakes. However, this fiction, because it is a vain and ungraspable body, readily escapes memory if it is not well secured. If, like truth, lies had only one face, we would be in a better position, for we would take the opposite of what the liar said as certain. But the reverse of truth has a hundred thousand faces and an indefinite field. The Pythagoreans posit that good is certain and finite, evil infinite and uncertain. A thousand roads deviate from the goal, only one leads to it.
This post is also linked to a discussion we had at Marcello Di Bello's presentation, "Cross-Examination with Bayesian Networks" (BayesiaLab Conference, 2022).
Create a new node: Start by creating a new node and label it as "Montaigne". This node will serve as a container for the text you want to analyze.
Enter the excerpt: Input the selected text into the "Montaigne" node as a comment.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Review the dimensions: Examine the dimensions or keywords returned by Hellixia. Remove any dimensions that seem redundant or irrelevant to your analysis.
Use the Embedding Generator on all remaining nodes. This tool captures and quantifies the semantics associated with the names and comments of each node.
Set the target node: Set "Montaigne" as the Target Node. The subsequent analyses and operations will focus on this node.
Run the Naive Learning algorithm.
Change node styles: Alter the style of all nodes to "Badges". This style will display the comment within each node.
Switch to Validation Mode.
Run the Arc Force analysis.
Apply the Radial Layout: While still in the Arc Force analysis tool, run the Radial Layout. This layout arranges the nodes in a clockwise manner according to the strength of their relationships with the target node.
Show the Arc Comments: These comments will provide information about the strength of the relationships between nodes.
Copy the "Montaigne" node: Begin by copying the node titled "Montaigne".
Paste the node into a new graph: Create a new graph and paste the copied "Montaigne" node into it.
Run the Dimension Elicitor using the following keywords to guide the analysis of the node: Contents, Ideas, Milestones, Rules, Themes, Theses, and the General Context set to "Philosophy".
Review the returned dimensions: Examine the dimensions provided by Hellixia. Remove any dimensions that appear redundant or irrelevant to your analysis.
Exclude the "Montaigne" node.
Use the Embedding Generator on all remaining nodes. This will help capture the semantic associations of their names and comments.
Create a semantic network: Use the Maximum Weight Spanning Tree algorithm to form a semantic network from the analyzed text.
Change node styles to "Badges". This style will allow the comment within each node to be shown.
Apply the Dynamic Grid Layout: Use this layout option to organize the nodes on your graph. Note that this layout algorithm is not deterministic, meaning it doesn't always produce the same results given the same input. It randomly favors vertical, horizontal, or mixed orientations. Run this layout multiple times until you find a layout that best suits your preferences.
Switch to Validation Mode.
Select Skeleton View: Since the network you're generating does not represent causal relationships, choose the Skeleton View. This will remove the arc orientations, leaving only connections between nodes without indicating a direction.
Switch back to Modeling Mode.
Change node styles to Discs.
Symmetric Layout.
Enter Validation Mode.
Analyze Node Force.
Run Variable Clustering: This will identify and group similar variables based on their semantics.
Open the Class Editor.
Within the Class Editor, activate the Class Description Generator. Use it to create meaningful names for the factors you're working with.
Save the descriptions you've just created using the Export Descriptions feature.
Switch back to Modeling Mode.
Execute Multiple Clustering to create latent variables.
Next, execute the structural learning algorithm Taboo. Make sure to enable the option "Delete Unfixed Arcs." This should result in the creation of a hierarchical network.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation Mode.
Use Node Force.
Welcome to our dedicated section, where we leverage Hellixia, BayesiaLab's new subject matter assistant, to explore the realm of philosophical essays. Here, we unpack the thoughts and arguments contained within works such as Niccolò Machiavelli's "The Prince," Thomas Hobbes' "Leviathan," and John Locke's "Two Treatises of Government." Through our analyses, we aim to construct semantic networks illuminating the complex webs of ideas and ideologies these essays present.
As we journey through each essay, we'll uncover the layers of philosophical discourse, revealing insights that have shaped political and moral thought for centuries. Join us as we navigate the pathways of these seminal philosophical works and gain a fresh understanding of their significance.
Baruch Spinoza's "Ethics" (often referred to as "Ethica" from its Latin title "Ethica, ordine geometrico demonstrata", meaning "Ethics Demonstrated in Geometrical Order") is a philosophical treatise written in the mid-17th century. It is one of the most significant and controversial works of the Enlightenment, and it presents Spinoza's metaphysical, epistemological, moral, and political views.
The structure of "Ethics" is unique: it is laid out like a geometrical treatise, akin to Euclid's "Elements". Starting with definitions and axioms, Spinoza proceeds with propositions, proofs, corollaries, and scholia (notes), aiming to demonstrate his philosophy with mathematical precision.
In this particular semantic analysis, we explore one of the famous quotes from Ethics:
Desire is the very essence of man, insofar as it is conceived as determined to some action by any of its affections.
Start by creating a new node. Label this node "Spinoza".
Input the chosen excerpt of text into the comment section of the "Spinoza" node.
Use the keyword "Keywords" to guide the Dimension Elicitor in analyzing the comment in the "Spinoza" node. Specify the General Context for your analysis as "Philosophy". By setting this context, you are providing direction for the Dimension Elicitor to understand the broader topic of your text. The Dimension Elicitor will then identify and extract relevant dimensions or keywords from the comment.
Examine the dimensions or keywords that Hellixia has identified. Any dimensions that appear irrelevant or redundant should be removed from your analysis.
Use the Embedding Generator on all remaining nodes. This tool will quantify the semantics associated with the names and comments of each node.
Set the "Spinoza" node as your Target Node.
Run the Naive Learning algorithm.
Update the visual style of all nodes to appear as "Badges". This will allow the comments within each node to be displayed.
Switch to Validation Mode.
Run an Arc Force analysis.
Use the Radial Layout while you are still within the Arc Force analysis tool. This will arrange the nodes in a clockwise fashion based on the strength of their relationships with the target node.
Show the Arc Comments to visualize information regarding the strength of the relationships between the nodes.
Start by copying the node "Spinoza". Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Ideas, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, exclude the "Spinoza" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; bear in mind that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Continuing with Baruch Spinoza's Ethics (see Episode 6), we focus on another passage about desire, determinism, and perceived free will in human actions.:
All men are born in ignorance of causes, and a universal appetite of which they are conscious drives them to seek what is useful to them.
A first consequence of this principle is that men believe they are free, because they are conscious of their volitions and desires, and do not think at all about the causes that predispose them to desire and to want.
The result, secondly, is that men always act with an end in mind, namely, their own utility, the natural object of their desire.
The supreme end of man, guided by reason, his supreme desire, this desire by which he strives to regulate all others, is therefore the desire that drives him to adequately understand both himself and all things that fall within his comprehension.
Start by creating a new node. Label this node "Spinoza".
Input the chosen excerpt of text into the comment section of the "Spinoza" node.
Use the keyword "Keywords" to guide the Dimension Elicitor in analyzing the comment in the "Spinoza" node. Specify the General Context for your analysis as "Philosophy". By setting this context, you are providing direction for the Dimension Elicitor to understand the broader topic of your text. The Dimension Elicitor will then identify and extract relevant dimensions or keywords from the comment.
Examine the dimensions or keywords that Hellixia has identified. Any dimensions that appear irrelevant or redundant should be removed from your analysis.
Use the Embedding Generator on all remaining nodes. This tool will quantify the semantics associated with the names and comments of each node.
Set the "Spinoza" node as your Target Node.
Run the Naive Learning algorithm.
Update the visual style of all nodes to appear as "Badges". This will allow the comments within each node to be displayed.
Switch to Validation Mode.
Run an Arc Force analysis.
Use the Radial Layout while you are still within the Arc Force analysis tool. This will arrange the nodes in a clockwise fashion based on the strength of their relationships with the target node.
Show the Arc Comments to visualize information regarding the strength of the relationships between the nodes.
Start by copying the node "Spinoza". Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Arguments, Ideas, Matters, Milestones, Motifs, Rules, Themes, and the General Context set to "Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, exclude the "Spinoza" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; bear in mind that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Blaise Pascal's "Pensées" (which translates to "Thoughts" in English) is a collection of fragments on theology and philosophy. Pascal, a French mathematician, physicist, and religious philosopher, began writing "Pensées" as a defense of the Christian religion, but he died before he could complete the work. The fragments he left behind were posthumously assembled and published in 1670.
This Philosophical Minute centers around a passage from Pensées, which delves into the human propensity to neglect the present moment, habitually yearning for the future, or dwelling on the past.
We never care about the present. We anticipate the future as too slow to come, as if to hasten its course; or we recall the past to stop it as too quick: so careless, we wander in times that are not ours, and do not think of the only one that belongs to us; and so vain, we think of those that are nothing anymore, and let slip without reflection the only one that remains.
It is because the present, usually, hurts us. We hide it from our sight, because it afflicts us; and if it is pleasant to us, we regret seeing it slip away.
The present is never our end: the past and the present are our means; the only future is our end. Thus we never live, but we hope to live; and, always preparing to be happy, it is inevitable that we never are.
Create a new node: Start by generating a new node named "Blaise Pascal - Pensées". This node will hold the text that you plan to analyze.
Insert the text: Add the selected excerpt into the comment section of the "Blaise Pascal - Pensées" node.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Assess the extracted dimensions: Evaluate the keywords or dimensions identified by Hellixia and eliminate any that are redundant or irrelevant.
Use the Embedding Generator for all remaining nodes. This tool will distill the semantics of the names and comments of each node into a quantifiable form.
Set "Blaise Pascal - Pensées" as the Target Node.
Run the Naive Learning algorithm.
Change the style of all nodes to "Badges". This style will display the comment embedded within each node.
Switch to Validation Mode.
Perform an Arc Force analysis.
While within the Arc Force analysis tool, run the Radial Layout. This will arrange the nodes in a clockwise pattern in relation to their connection strength with the target node.
Show the Arc Comments, which will provide information about the strength of the relationships between nodes.
Start by making a copy of the node named "Blaise Pascal - Pensées".
Open a new graph and paste the copied "Blaise Pascal - Pensées" node.
Use the following keywords to guide the Dimension Elicitor in its analysis of the node: Arguments, Matters, Milestones, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy".
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Blaise Pascal - Pensées" node.
Use the Embedding Generator on all remaining nodes.
Run the Maximum Weight Spanning Tree algorithm to create a semantic network based on the text analysis.
Change the style of all nodes to "Badges". This will display the comment within each node.
Run the Dynamic Grid Layout to organize the nodes on your graph. Note that this algorithm's output is not deterministic; it may favor vertical, horizontal, or mixed orientations. Execute this layout multiple times until you find the most suitable arrangement.
Switch to Validation Mode.
As the graph you are building does not represent causal relationships, opt for the Skeleton View. This will remove all arc directions, leaving only the node connections without any specified direction.
Switch back to Modeling Mode.
Change all node styles to Discs.
Use the Symmetric Layout to organize your nodes in the graph.
Go to Validation Mode.
Conduct a Node Force analysis to evaluate the strength of associations in your graph.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor.
Run Class Description Generator: Use this function to generate descriptive names for your identified factors. This helps to make the output more understandable and interpretable.
Save these descriptions by using the Export Descriptions function.
Switch back to Modeling Mode.
Run Multiple Clustering.
Run the Taboo algorithm: Use this structural learning algorithm to learn a hierarchical network. Make sure to enable the "Delete Unfixed Arcs" option to remove unnecessary connections and streamline your model.
Use the descriptions you exported earlier as a dictionary to rename the latent variables you've just created. This helps in making your model more understandable and keeps the nodes' names consistent with their semantic meaning.
Switch to Validation Mode.
Apply Node Force.
In this fifth episode, we delve into another passage from Blaise Pascal's Pensées. This particular segment sheds light on the compromise required to uphold societal harmony, a state considered the highest form of good.
Without doubt, the equality of goods is just; but, unable to make it force to obey justice, we have made it just to obey force; unable to strengthen justice, force was justified, so that the just and the strong might be together, and peace might be, which is the sovereign good.
Create a new node: Start by generating a new node named "Blaise Pascal - Pensées". This node will hold the text that you plan to analyze.
Insert the text: Add the selected excerpt into the comment section of the "Blaise Pascal - Pensées" node.
Run the Dimension Elicitor, set the General Context to "Philosophy", and input "Keywords" as the keyword for the analysis of the node comment.
Assess the extracted dimensions: Evaluate the keywords or dimensions identified by Hellixia and eliminate any that are redundant or irrelevant.
Use the Embedding Generator for all remaining nodes. This tool will distill the semantics of the names and comments of each node into a quantifiable form.
Set "Blaise Pascal - Pensées" as the Target Node.
Run the Naive Learning algorithm.
Change the style of all nodes to "Badges". This style will display the comment embedded within each node.
Switch to Validation Mode.
Perform an Arc Force analysis.
While within the Arc Force analysis tool, run the Radial Layout. This will arrange the nodes in a clockwise pattern in relation to their connection strength with the target node.
Show the Arc Comments, which will provide information about the strength of the relationships between nodes.
Start by making a copy of the node named "Blaise Pascal - Pensées".
Open a new graph and paste the copied "Blaise Pascal - Pensées" node.
Use the following keywords to guide the Dimension Elicitor in its analysis of the node: Arguments, Contents, Ideas, Matters, Milestones, Motifs, Rules, Themes, Theses, Topics, and the General Context set to "Philosophy".
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Blaise Pascal - Pensées" node.
Use the Embedding Generator on all remaining nodes.
Run the Maximum Weight Spanning Tree algorithm to create a semantic network based on the text analysis.
Change the style of all nodes to "Badges". This will display the comment within each node.
Run the Dynamic Grid Layout to organize the nodes on your graph. Note that this algorithm's output is not deterministic; it may favor vertical, horizontal, or mixed orientations. Execute this layout multiple times until you find the most suitable arrangement.
Switch to Validation Mode.
As the graph you are building does not represent causal relationships, opt for the Skeleton View. This will remove all arc directions, leaving only the node connections without any specified direction.
Switch back to Modeling Mode.
Change all node styles to Discs.
Use the Symmetric Layout to organize your nodes in the graph.
Go to Validation Mode.
Conduct a Node Force analysis to evaluate the strength of associations in your graph.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor.
Run Class Description Generator: Use this function to generate descriptive names for your identified factors. This helps to make the output more understandable and interpretable.
Save these descriptions by using the Export Descriptions function.
Switch back to Modeling Mode.
Run Multiple Clustering.
Run the Taboo algorithm: Use this structural learning algorithm to learn a hierarchical network. Make sure to enable the "Delete Unfixed Arcs" option to remove unnecessary connections and streamline your model.
Use the descriptions you exported earlier as a dictionary to rename the latent variables you've just created. This helps in making your model more understandable and keeps the nodes' names consistent with their semantic meaning.
Switch to Validation Mode.
Apply Node Force.
Welcome to the eighth installment of the Philosophical Minute, where we continue our exploration of the works of Baruch Spinoza. Today's focus is a captivating foray into Spinoza's reflections on desire, and its profound influence on our perceptions of good and evil:
We consider good the thing that we desire; and consequently, we call the thing that inspires us with aversion, bad; so that everyone judges according to their passions what is good or bad, what is better or worse, what is most excellent or most contemptible.
Spinoza, in his meticulous examination, sheds light on the intrinsic nature of desire and its pivotal role in shaping human behavior and ethics. How does what we desire dictate our moral compass? Why do we perceive certain desires as virtuous and others as vice? Spinoza's insights into these questions offer a deep dive into the undercurrents of human psychology and the constructs of morality.
Node Creation: Start by generating a new node. Name it "Spinoza".
Text Inclusion: Insert your chosen text excerpt into the comment section of this "Spinoza" node.
Dimension Elicitation: Use the Dimension Elicitor with the keyword "Keywords" to analyze the comment within the "Spinoza" node. Define the General Context as "Philosophy". This context directs the elicitor to frame the analysis within the broader realm of philosophical discourse.
Dimension Review: Evaluate the dimensions or keywords identified by Hellixia. Remove any that seem redundant or not pertinent to your objective.
Semantic Quantification: Run the Embedding Generator for all the nodes that are still in play. This process translates the semantic elements of each node's name and comments into quantifiable metrics.
Target Node Designation: Designate "Spinoza" as your primary or target node.
Learning Algorithm: Launch the Naive Learning algorithm.
Visualization: Alter the visual representation of every node to the "Badges" style. It ensures that the comments associated with each node are directly visible.
Validation: Transition your workspace to the Validation Mode.
Arc Analysis: Run the Arc Force analysis.
Graph Layout: While still in the Arc Force analysis tool, run the Radial Layout. This method organizes nodes in a circle around your target node, positioning them based on the strength of their connection to the target.
Arc Visualization: Activate the Arc Comments. This feature superimposes a visualization layer on your network, displaying information about the arcs' strengths.
Start by copying the node "Spinoza". Then, create a new graph and paste the node.
Utilize the Dimension Elicitor with the subsequent keywords: Arguments, Ideas, Matters, Milestones, Motifs, Rules, Themes, and the General Context set to "Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, exclude the "Spinoza" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network from the excerpt.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; bear in mind that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Switch back to Modeling Mode and change the visual representation of each node to the "Discs" style. The disc style offers a clean and straightforward visual, which might be easier to interpret in some contexts compared to the badge style.
Use the Symmetric Layout tool.
Switch to Validation Mode and run the Node Force analysis.
Carry out Variable Clustering: This action will group similar variables together based on their semantic connections.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Switch back to Modeling Mode and run Multiple Clustering to produce latent variables.
Launch the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Step with us into the realms of power, strategy, and human nature as we set our sights on Niccolò Machiavelli's The Prince. Crafted in the crucible of Renaissance Florence, this timeless piece of literature stands as one of the most impactful texts in political philosophy, its influence reaching far beyond its era.
Machiavelli's frank, pragmatic exploration of power and statecraft provides a view of leadership that is as intriguing as it is controversial, and understanding his complex narrative requires a nuanced approach. To achieve this, we enlist the capabilities of Hellixia, BayesiaLab's subject matter assistant.
Using Hellixia's ability to generate intricate semantic networks, we can delve deep into the narrative threads of The Prince, illuminating the interconnected concepts, themes, and motifs that form the foundation of Machiavelli's groundbreaking treatise.
From the cunning strategies of political maneuvering to the paradoxical virtues of a successful leader, we'll explore the sophisticated landscape of The Prince, powered by the detailed semantic analysis provided by Hellixia. So, come and join us on this captivating journey as we uncover the layers of Machiavelli's enduring masterpiece.
Start by creating the node "The Prince".
Use the Dimension Elicitor, employing a broad array of keywords like "Characteristics", "Contributions", "Motivations", "Influencers", and many more, to conduct an exhaustive analysis of the book (see the keywords that are listed in the Class Editor below). We also set the General Context to "Nicolas Machiavel Political Philosophy".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "The Prince" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Delve into the intricate world of John Locke's "Two Treatises of Government" in this dedicated section. Using the power of Hellixia, we aim to dissect this seminal work, which stands as a cornerstone of modern political philosophy. The text, rooted in the theories of natural rights and the social contract, has played a pivotal role in shaping democratic governance and individual liberties. Through our in-depth analysis, we will construct semantic networks that elucidate Locke's arguments, laying bare the foundational principles of his thoughts on society, governance, and the very nature of human rights. Join us on this enlightening journey as we navigate the depths of "Two Treatises," unraveling its philosophical intricacies and enduring relevance.
Start by creating the node "Two Treatises of Government, by John Locke".
Use the Dimension Elicitor, employing a broad array of keywords like "Achievements", "Considerations", "Concepts", and many more, to conduct an exhaustive analysis of the essay (see the keywords that are listed in the Class Editor below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Two Treatises of Government, by John Locke" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Embarking on an exploration of one of the most influential works in the realm of political philosophy, we turn our attention to Thomas Hobbes' Leviathan. Penned in a time of civil strife, Leviathan serves as a cornerstone of Western political thought, offering insights into the nature of social contract, sovereignty, and the legitimacy of political power.
Hobbes' arguments and reasoning, profound yet intricate, necessitate a thoughtful and systematic approach to understanding. That is where Hellixia, BayesiaLab's subject matter assistant, comes into play. With the power to construct detailed semantic networks, Hellixia provides us with a uniquely comprehensive way to interpret and examine the depth of Leviathan.
Utilizing these semantic networks, we will delve into the complex themes and ideas that Hobbes presents, mapping out the interconnections and dissecting the concepts that lie at the heart of Leviathan. From the notions of the state of nature and the social contract to the role and extent of sovereignty, our journey through this foundational text, powered by Hellixia's semantic analysis, promises a fresh perspective and new insights into Hobbes' grand political treatise.
Start by creating the node "Leviathan".
Use the Dimension Elicitor, employing a broad array of keywords like "Points", "Considerations", "Approaches", "Concepts", and many more, to conduct an exhaustive analysis of the book (see the keywords that are listed in the Class Editor below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Leviathan" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Step into a realm where two of the Enlightenment's most profound thinkers, Thomas Hobbes and John Locke, are set side by side for scrutiny. This section is dedicated to a comparative analysis of these philosophical giants using the insights provided by Hellixia. While both philosophers tackled the nature of the social contract, governance, and human nature, their conclusions often diverged, leading to rich philosophical debates that resonate today. With the aid of semantic networks, we'll untangle the intricate threads of their arguments, highlighting areas of agreement and divergence. This exploration promises a study of their philosophies and a deeper understanding of the broader political and ethical landscape they helped shape. Join us in this captivating journey as we traverse the intricate terrains of Hobbesian and Lockean thought.
Start by creating the node "Thomas Hobbes and John Locke".
Use the Dimension Elicitor with a broad array of keywords like "Perspectives, Rules, Divergences, Ideas, Topics, Similarities and Differences", and set the General Context to "Political Philosophy."
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Thomas Hobbes and John Locke" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Welcome to our comprehensive exploration of Montesquieu's seminal work, "The Spirit of the Laws." Through the lens of Hellixia, we will embark on an intellectual journey to dissect and understand this monumental text, which remains a cornerstone in the realms of political science and philosophy.
In this section, we will conduct a detailed holistic analysis, delving deep into the complex layers that constitute this influential work. Focusing on various aspects like Concepts, Values, Impacts, and Perspectives, we aim to forge a rich, multidimensional exploration of Montesquieu's political theory. This analysis explains in depth Montesquieu's views on systems of governance, law, and the underlying principles that drive societies.
Join us as we traverse the intricate pathways of "The Spirit of the Laws", illuminating the timeless wisdom encapsulated within its pages and unraveling the broader implications and influences of Montesquieu's revolutionary thoughts on the modern world.
Start by creating the node "The Spirit of the Laws, by Montesquieu."
Use the Dimension Elicitor with this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Themes, Theses, Topics, and Values.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "The Spirit of the Laws, by Montesquieu" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Welcome to our section where we utilize the power of Hellixia to explore the fascinating world of literature. Here, we go beyond conventional textual analyses to create semantic networks, unraveling the rich layers of classics such as 'Hamlet' by William Shakespeare, 'Madame Bovary' by Gustave Flaubert, 'A Tale of Two Cities' by Charles Dickens, and 'Middlemarch' by George Eliot. But our exploration doesn't stop at individual works. We also delve into the relationships between authors from diverse styles - from magic realism and gothic fiction to surrealism and science fiction, and beyond. This innovative approach illuminates the subtle interconnections within and across genres.
Let's embark on this literary journey together, weaving semantic networks that capture the unique essence of literary works, authors, and genres, and provide a refreshing perspective on the magnificent tapestry of literature.
Embark on a literary journey with us as we use Hellixia to uncover the rich interconnections among hundreds of authors, spanning a variety of literary styles such as magic realism, gothic fiction, surrealism, and science fiction. By mapping these intricate relationships, our semantic network becomes a personalized guide, helping you discover your next potential favorite read. This network is not just a visual tool, it's your passport to uncharted literary territories, ready to guide your reading adventure.
Start by creating the node "Magic Realism".
Utilize the Dimension Elicitor with "Competitors" as the guiding keyword, setting the General Context to "Literature Style", to discover other literary styles.
Inspect the dimensions Hellixia generates and discard any that appear irrelevant or extraneous to your analysis.
Select all nodes.
Run the Dimension Elicitor again using "Members" as the keyword and "Influential Writers" as the General Context. This process aims to discover influential writers for each style, focusing on Node Comments as the Main Subject of the Query. Set the Responses per Keyword parameter to 20 to get a wide range of results.
Inspect the resulting dimensions from Hellixia and remove any that appear irrelevant or superfluous.
Repeat the last 2 steps with the Node Names and Comments as Main Subject of the Query. This will enable the discovery of additional writers.
Use the Maximum Weight Spanning Tree algorithm to create a semantic network.
Change node styles to Badges to display each node's comment.
Apply the Dynamic Grid Layout for positioning the nodes on your graph. This algorithm is not deterministic and favors vertical, horizontal, or mixed orientations randomly. Running this layout multiple times might be necessary until you achieve an arrangement that suits your preferences.
Switch to Validation Mode and activate Skeleton View. As your network does not represent causal relations, the Skeleton View will only show the connections between nodes without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Optional: Delete all Arcs. This can be helpful for achieving a cleaner graph layout.
Use the Distance Mapping algorithm based on Mutual Information. This algorithm creates a 2D layout where the nodes' distances are proportional to the semantic proximity between the nodes (considering both names and comments).
Welcome to a deep dive into the depths of "A Tale of Two Cities," Charles Dickens' renowned novel that weaves a tapestry of intertwined lives against the backdrop of the French Revolution. With the help of Hellixia, we will create a detailed semantic network that exposes the complex relationships and themes embedded within this literary masterpiece. From its iconic characters and their motivations to the social and political currents driving the narrative, we'll explore the intricate layers that make this novel a timeless classic. Brace yourself for a journey through love, sacrifice, and redemption as we unravel Dickens' narrative in a way you've never seen before.
Start by creating the node "A Tale of Two Cities".
Use the Dimension Elicitor, employing a broad array of keywords like "Agents", "Aspects", "Components", "Milestones", and many more, to conduct an exhaustive analysis of the book (see the exhaustive list of keywords below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "A Tale of Two Cities" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Prepare to delve into the richly complex world of William Shakespeare's Hamlet, one of the most influential works in English literature. With its iconic characters and timeless themes of power, revenge, morality, and madness, Hamlet continues to captivate audiences centuries after its creation.
To navigate the intricacies of this monumental work, we will create and explore semantic networks, providing a unique lens through which to view and understand Hamlet.
Through these semantic networks, we'll uncover the deep interconnections between the play's characters, themes, and motifs, illuminating the layered narrative and providing fresh insights into this enduring classic. Join us on this enlightening journey as we explore Hamlet in a way you've never seen before, brought to life through the power of Hellixia's semantic analysis.
Start by creating the node "Hamlet".
Use the Dimension Elicitor, employing a broad array of keywords like "Developments", "Ideas", "Perspectives", "Milestones", and many more, to conduct an exhaustive analysis of the play (see the keywords that are listed in the Class Editor below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Hamlet" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
In the vast tapestry of Shakespearean tragedies, "King Lear" stands out as a potent tale of familial strife, ambition, and the relentless quest for power. As we journey into this masterwork, we find ourselves amidst the tumultuous relationships of a king with his children, set against the backdrop of a kingdom in disarray.
Having ventured into the intricate worlds of "Hamlet" and "Macbeth", we now shift our focus to this powerful narrative. Our exploration is structured in two parts: we begin with a detailed narrative analysis, diving deep into plot intricacies and character dynamics. Following this, we transition to a holistic analysis, capturing overarching themes, motives, and the very essence that makes "King Lear" a cornerstone of literary greatness.
With the precision of Hellixia guiding our analysis, join us in this enlightening expedition as we endeavor to unveil the complexities and profundities that Shakespeare so masterfully wove into the fabric of "King Lear".
Navigating "King Lear", our narrative analysis dissects the play's pivotal events and character dynamics. We'll unravel the tale of a father, his daughters, and a kingdom in turmoil, shedding light on Shakespeare's intricate storytelling.
Start by creating the node "King Lear, by Shakespeare."
Use the Dimension Elicitor, employing a broad array of keywords: Agents, Contexts, Developments, Entities, Events, Highlights, Keywords, Locations, Milestones, Motifs, Progressions, and Relationships.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "King Lear, by Shakespeare" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors. Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Stepping beyond the immediate narrative, our holistic examination delves into the deeper themes, sentiments, and philosophical underpinnings of "King Lear." This lens allows us to grasp the timeless essence and profound messages that Shakespeare interwove within the play's fabric.
Follow the workflow outlined in the Narrative Analysis section, but use this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Theses, and Values.
Immerse yourself in George Eliot's "Middlemarch," a literary masterpiece that profoundly looks into 19th-century provincial life in England. Leveraging the capabilities of Hellixia, our journey into this classic will be navigated through semantic networks, dividing our exploration into two distinct stages:
Narrative Analysis: By examining the plot intricacies, character dynamics, and the socio-personal currents influencing them, we'll draw deeper connections within the narrative.
Holistic Analysis: Stepping back from the immediate narrative, Hellixia will guide us through a broader examination of the novel. Tapping into diverse categories such as Achievements, Emotions, Themes, and Values, we aim to capture the multifaceted essence of "Middlemarch."
Join us in this exploration, where we aim to unravel the nuances and complexities of "Middlemarch" that continue to resonate with readers across generations.
From the unfolding Events to pivotal Milestones and distinct Locations to underlying Motifs, we'll spotlight the interwoven Relationships among the novel's Entities. Guided by essential keywords like Context, Developments, and Progressions, this section seeks to unveil the narrative depth and intricacies of Eliot's masterpiece.
Start by creating the node "Middlemarch."
Use the Dimension Elicitor, employing the keywords "Context, Developments, Entities, Events, Keywords, Locations, Milestones, Motifs, Progressions, and Relationships," to conduct an exhaustive narrative analysis of the book. Set the General Context to "George Eliot novel".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Middlemarch" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and change the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Transitioning from the narrative details, our next phase delves into the broader essence of "Middlemarch." Here, we venture beyond the story to understand its Achievements, Emotions, Themes, and Values, capturing the multifaceted heart of Eliot's work. This comprehensive exploration offers a panoramic view of the novel's enduring impact and significance.
Follow the workflow outlined in the Narrative Analysis section, but use this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Themes, Theses, and Values.
"Madame Bovary" is a novel written by the French author Gustave Flaubert, published in 1857. It is one of the most influential literary works of the 19th century and is widely regarded as a seminal work of realism in literature. Flaubert's meticulous attention to detail and his pursuit of the "mot juste" (the exact right word) have made the novel a benchmark in the development of the modern novel.
Flaubert's portrayal of Emma Bovary is complex and multi-dimensional. While she can be seen as self-centered and even morally corrupt, she is also a victim of her environment, upbringing, and limited means of escaping her circumstances.
Semantic networks produced by Hellixia reveal the relationship between the characters and the structure of themes with unprecedented clarity.
Start by creating the node "Madame Bovary".
Use the Dimension Elicitor, employing a broad array of keywords like "Agents", "Aspects", "Components", "Milestones", and many more, to conduct an exhaustive analysis of the book (see the keywords that are listed in the Class Editor below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Madame Bovary" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and change the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Embark with us on a journey into James Joyce's "Ulysses," a literary masterpiece revered for its complexity and depth. Leveraging Hellixia, we will navigate the intricate labyrinth of themes, symbols, and linguistic innovations present in the text. From exploring the psychological depths of its characters to interpreting its myriad of allusions, we will construct a comprehensive semantic network that illuminates the intricate facets of "Ulysses." Prepare for a compelling expedition into the heart of Joyce's modernist vision, a textual exploration that unravels the compelling richness of this universally admired work.
Start by creating the node "Ulysses"
Use the Dimension Elicitor, employing a broad array of keywords like "Characteristics", "Emotions", "Features", "Strengths, "Traits," and "Weaknesses" to conduct an exhaustive analysis of the book. Set the General Context to "James Joyce."
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Ulysse" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Welcome to our in-depth analysis of "The Demon" by Hubert Selby Jr. In this concise yet comprehensive section, we use Hellixia to facilitate a two-part exploration of this riveting novel.
First, we embark on a narrative analysis, dissecting the plot and characters to reveal the underlying themes that Selby skillfully interweaves throughout the story. This part offers a vivid glimpse into Selby's dark and immersive world.
Next, we transition to a holistic analysis, where we zoom out to evaluate the novel's broader philosophical and societal undertones. This segment intends to illuminate the novel's intricate interplay of themes, values, and impacts, showcasing its rich complexity and literary significance.
Join us for this enriching journey that offers a fresh and insightful perspective on "The Demon".
In this first segment, we focus on the narrative intricacies of "The Demon". Through Hellixia's lens, we will dissect the vibrant characters and the entwined plot that makes Selby's novel an evocative journey.
Start by creating the node "The Demon."
Use the Dimension Elicitor, employing a broad array of keywords: Agents, Contexts, Developments, Entities, Events, Highlights, Keywords, Locations, Milestones, Motifs, Progressions, and Relationships. Set also the General Context to: "Hubert Selby Novel"
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "The Demon" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments. Please note that "The Demon" is not as widely recognized, and GPT might hallucinate, i.e., occasionally generate responses that align with other more prominent works by Selby, such as "Requiem for a Dream".
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors. Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Moving forward, we transition to a more expansive view in our holistic analysis. Utilizing Hellixia, we aim to delve deeper, exploring the broader themes, societal influences, and underlying philosophies encapsulated in "The Demon".
Follow the workflow outlined in the Narrative Analysis section, but use this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Themes, Theses, Topics, and Values.
Welcome to our film analysis section, where we use Hellixia's capabilities to delve into the intricate narratives of iconic movies like "The Good, The Bad, and The Ugly" and "Apocalypse Now." With Hellixia's assistance, we'll generate semantic networks that capture these films' complex character relationships, thematic depth, and contextual subtleties. From the moral and psychological complexities of warfare depicted in "Apocalypse Now" to the multi-layered exploration of good and evil in "The Good, The Bad, and The Ugly," our analyses will offer a fresh perspective on these cinematic masterpieces. This section is a cinephile's dream, providing an engaging blend of art and technology to deepen our understanding and appreciation of film.
Welcome to a comprehensive analysis of "The Good, The Bad, and The Ugly," a quintessential spaghetti western directed by the legendary Sergio Leone. With the power of Hellixia, we will create a detailed semantic network, offering an in-depth exploration into this cinematic masterwork. We will dissect its iconic characters, intricate plot lines, dramatic settings, and the moral dilemmas they embody. This film's subtle commentaries on good, evil, and the gray areas in between will be laid bare through our network. Prepare for a fascinating journey as we unravel the intricate layers of "The Good, The Bad, and The Ugly," a film that forever changed the landscape of western cinema.
Start by creating the node "The Good, the Bad and the Ugly".
Use the Dimension Elicitor, employing a broad array of keywords like "Achievements", "Characteristics", "Components", "Milestones", and many more, to conduct an exhaustive analysis of the book (see the exhaustive list of keywords below). Set the General Context to "Sergio Leone Movie".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "The Good, the Bad and the Ugly" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Welcome to an in-depth analysis of "Apocalypse Now," Francis Ford Coppola's seminal film that probes into the heart of darkness represented by the Vietnam War. Utilizing Hellixia, we will generate a sophisticated semantic network to illuminate the complex themes, characters, and cinematic techniques of this iconic film. From its profound critique of war and colonialism to its exploration of human nature and morality, we'll dissect the multi-layered narrative that defines this cinematic masterpiece. Strap in for an intellectual journey as we delve into the chaotic world of "Apocalypse Now" and shine a light on its profound commentary on the human condition.
Start by creating the node "Apocalypse Now".
Use the Dimension Elicitor, employing a broad array of keywords like "Achievements", "Characteristics", "Components", "Milestones", and many more, to conduct an exhaustive analysis of the book (see the exhaustive list of keywords below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Apocalypse Now" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Welcome to our exploration of Sergio Leone's epic masterpiece, "Once Upon a Time in America." Spanning decades, this cinematic tour de force weaves a complex tale of friendship, ambition, betrayal, and redemption against the backdrop of organized crime in 20th-century America.
Leone's storytelling prowess, coupled with a haunting score by Ennio Morricone and remarkable performances by a stellar cast, including Robert De Niro and James Woods, make this film an unforgettable journey through time and human emotion.
From the gritty streets of New York's Lower East Side to the lavish elegance of 1960s' Manhattan, "Once Upon a Time in America" unfolds its narrative with a richness and complexity rarely seen in cinema. The film's non-linear structure, exquisite cinematography, and deeply layered themes make it an object of fascination and study.
Join us as we delve into this magnum opus, unraveling its intricate narrative threads and uncovering the symbolism, motifs, and philosophical undertones that elevate this movie to the status of timeless art. Whether you're revisiting this classic or discovering it for the first time, our analysis promises to provide new insights into a film that continues to captivate audiences worldwide.
Start by creating the node "Once Upon a Time in America".
Use the Dimension Elicitor, employing a broad array of keywords like "Achievements", "Characteristics", "Components", "Milestones", and many more, to conduct an exhaustive analysis of the book (see the exhaustive list of keywords below). Set the General Context to "Sergio Leone Movie".
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Once Upon a Time in America" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
In the realm of advanced data analysis and knowledge modeling, understanding the underpinnings of causality is crucial. Hellixia, at the forefront of this analytical revolution, offers a set of functions dedicated to causality, enabling the generation of Causal Semantic Networks (CSN) and Causal Bayesian Networks (CBN). This section is aimed at engineers and researchers seeking to unravel the complexity of cause-and-effect relationships in their field.
Join us as we plunge into the lyrical depths of "Last Great American Whale", a song by the iconic musician Lou Reed. Known for his distinctive storytelling and unique blend of rock, this track from his 1989 album, 'New York', stands as a testament to Reed's keen observation of American society and culture.
In "Last Great American Whale", Reed weaves a tale that resonates with environmental and social commentary, a narrative that's as poignant today as it was when first penned. To navigate through this multifaceted piece of music, we'll be enlisting the aid of Hellixia, BayesiaLab's subject matter assistant.
Harnessing Hellixia's ability to create intricate semantic networks, we aim to dissect the themes, motifs, and narratives hidden within Reed's lyrics. This song, ripe with symbolism and metaphor, offers a rich landscape for such analysis.
From the overarching narratives of environmentalism and social critique to the individual threads of American culture, Hellixia will guide us through the complex lyrical world that Reed has created. So come, immerse yourself in the rhythm and the words, as we unravel the enigma of Lou Reed's 'Last Great American Whale'.
Start by creating the node "Last Great American Whale".
Use the Dimension Elicitor, employing a broad array of keywords like "Developments", "Influencers", "Events", "Entities", and many more, to conduct an exhaustive analysis of the song lyrics (see the keywords that are listed in the Class Editor below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Last Great American Whale" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Welcome to our section dedicated to the profound world of song lyrics, where we harness the capabilities of Hellixia to dissect and interpret musical narratives. Venturing beyond mere words, we craft semantic networks that spotlight the underlying stories and sentiments of iconic tracks like "Mercy Seat," "Red Right Hand," "Last Great American Whale," and "Jungleland." We aim to unravel the richness of these compositions, gleaning insights into their essence and cultural resonance. Dive deep with us as we illuminate the intricate nuances of these songs, offering a fresh, interconnected perspective on their lyrical artistry.
Welcome to our in-depth analysis of "The Mercy Seat," an iconic song by Nick Cave. Through this exploration, we will delve into the intricate narratives and powerful emotions embedded within the song. Using Hellixia, we will construct a semantic network that reveals the song's complex themes and the relationships among them, shedding light on the profound depths of Cave's storytelling. Join us as we journey into the haunting world of "The Mercy Seat."
Start by creating the node "Τhe Mercy Seat".
Use the Dimension Elicitor with a broad array of keywords like "Achievements," "Characteristics," "Ideas," and "Impacts" (see the exhaustive list below), and set the General Context to "Nick Cave Song." By doing so, you're informing the tool to approach the analysis with the perspective to a song by Nick Cave.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "The Mercy Seat" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Let's delve into the very fabric of "Red Right Hand," examining its lyrical landscape to uncover the embedded stories, motifs, and emotions they evoke.
Start by creating the node "Lyrics of The Red Right Hand, by Nick Cave & the Bad Seeds."
Input the lyrics into the comment section of the node:
Use the Dimension Elicitor, employing the keywords "Agents, Keywords, Events, Relationships, Developments, Contexts, Highlights, Milestones, Entities, Progressions, Motifs, and Locations" to conduct an exhaustive narrative analysis of the lyrics.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Lyrics of The Red Right Hand, by Nick Cave & the Bad Seeds" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and change the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Moving beyond the narrative, we'll now capture the broader essence of "Red Right Hand," exploring its overarching themes, sentiments, and the cultural resonances embedded within.
Follow the workflow outlined in the Narrative Analysis section, but use this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Themes, Theses, and Values.
Step into the enigmatic realm of Nick Cave & The Bad Seeds with their masterful song, "." Renowned for its rich imagery and profound thematic undertones, this song offers a narrative tapestry begging to be unraveled. Leveraging Hellixia, our exploration will commence with a narrative analysis of the lyrics, delving deep into the song's storytelling elements. Following this, we'll transition into a holistic examination, piecing together the broader themes and emotional resonances that Cave artfully embeds. Join us as we navigate this iconic track's poetic and musical depths.
Join us as we delve into a detailed examination of the New Deal, an essential historical period shaped by the repercussions of the Great Depression. Triggered by our reading of John Steinbeck's poignant "The Grapes of Wrath," we will harness the power of Hellixia to create a causal semantic network. This network will depict the policies enacted during the New Deal and explore their cause-and-effect relationships. Through this analysis, we aim to shed light on the complex interplay between economic conditions, policy decisions, and societal outcomes during this transformative era in American history.
Create a node named "New Deal".
Use the following keywords to guide the Dimension Elicitor's node analysis: Characteristics, Causes, Elements, Keywords, Features, Years, Dimensions, Definitions, Traits, Outcomes, Factors, Consequences, Aims, Descriptions, and Goals.
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "New Deal" node.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Utilize Hellxia's Causal Structural Priors to evaluate whether the correlations highlighted by the maximum spanning tree indeed signify causal relationships.
Inspect the Structural Priors Explanations suggested by Hellixia. Any priors that are irrelevant should be removed.
Run the Taboo Learning algorithm with the remaining Structural Priors. These priors will reduce the cost of adding arcs that embody these causal relations.
Use Hellxia's Causal Structural Priors again to examine if the highlighted correlations from the maximum spanning tree suggest causal relationships.
Repeat the above three steps as necessary until the model is satisfactory.
Inspect the final set of Structural Priors Explanations and remove any irrelevant priors.
Export the Structural Priors.
Delete all arcs.
Use the saved Structural Priors as an arc dictionary. This will generate a causal network based on these priors.
Utilize the Structural Priors as an arc comment dictionary to store the descriptions of the causal relationships.
Apply the Genetic Grid Layout algorithm to neatly arrange the nodes on your graph, reflecting the causal directionality.
The screenshot below displays the explanations associated with the Structural Priors. These explanations detail the causal relationships and the logical connections inferred by Hellixia's analysis between the different nodes in the network. They provide valuable insights into the underlying structure and dynamics of the system being studied.
The blue icon in the 'Check' column signifies that an arc in the network currently represents the corresponding structural prior. If there's a red icon, it indicates that the arc is reversed. If there's no icon at all, it denotes the arc is absent from the network. The only red icon in the example below reflects that Hellixia identified a causal explanation in both directions.
In the rich tapestry of "Jungleland", Springsteen paints a picture of urban struggle and young love, masterfully set against the backdrop of a gritty cityscape. His intricate lyrics tell a tale that's profoundly human and deeply emotive.
To guide us through the labyrinth of Springsteen's poetic narrative, we'll be utilizing Hellixia, BayesiaLab's subject matter assistant. Harnessing the power of Hellixia's semantic network generation, we will delve into the depths of Springsteen's lyrics, dissecting the themes, metaphors, and underlying emotions that make "Jungleland" a celebrated piece of musical storytelling.
From the hustle of the city streets to the poignant silent reverence in the face of loss, Hellixia will enable us to explore the intricate interplay of love, struggle, and resilience in "Jungleland". So join us as we navigate the urban landscape of Springsteen's imagination, diving into the heart of his narrative genius.
Start by creating the node "Jungleland".
Use the Dimension Elicitor, employing a broad array of keywords like "Milestones", "Agents", "Connections", "Forces", and many more, to conduct an exhaustive analysis of the song lyrics (see the keywords that are listed in the Class Editor below).
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Jungleland" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Prepare to embark on an explorative journey through "," a sonic masterpiece by none other than the legendary Bruce Springsteen. An epic closure to his breakthrough album 'Born to Run', released in 1975, "Jungleland" is a symphony of vivid storytelling, resounding saxophone solos, and the raw intensity that characterizes Springsteen's work.
Welcome to our Causal Bayesian Networks section, where we leverage Hellixia as a Subject Matter Assistant for constructing Causal Bayesian Networks. These networks feature directional arcs that convey causality. In contrast to Causal Semantic Networks, which primarily offer qualitative insights by highlighting semantic causal relationships between variables, Causal Bayesian Networks offer a dual approach, encompassing both qualitative and quantitative aspects. They serve not only to improve our understanding of a domain, but also to enable probabilistic and causal inference.
Atopic Dermatitis, commonly known as eczema, manifests as red, itchy, and occasionally painful rashes, affecting both children and adults to varying degrees.
This section examines the many facets of atopic dermatitis, where genetic, environmental, and immunological factors converge to influence its development and progression. To better understand this complex disease, we use Hellixia to generate causal Bayesian networks, which provide a structured framework for deciphering cause-and-effect relationships.
But first, we'll start with a semantic analysis of the domain to get an overview of the main concepts, variables, and relationships in the field of atopic dermatitis.
We create the node "Atopic Dermatitis" and then go through our usual workflow for creating a semantic network and then a hierarchical semantic network (see previous sections, e.g., Hamlet) to perceive the semantic landscape surrounding atopic dermatitis and lay the foundations for a deeper understanding of its underlying dynamics.
We finally select the factors only (i.e., we focus on the higher level of this hierarchical network), and use the Hellixia Report Analyzer to generate a concise summary of the Relationship Analysis Report.
Having gained an overall understanding of the domain through semantic networks, we now move on to the construction of Causal Bayesian Networks using Hellixia's new capabilities that will be released in BayesiaLab 11.2.
We start by creating a node called "Atopic Dermatitis Mechanism", then select the Causal Network Generator feature.
After one or two minutes (the prompt is indeed quite complex), we obtain a fully specified Causal Bayesian Network (graph and probabilities). This network is characterized by causally oriented arcs, each accompanied by a concise explanation of the causal relationship and an estimate of the causal effect, scaled between -100 (shown in red) and 100 (shown in blue). To translate these causal effects into conditional probability tables, we use a new BayesiaLab formula, DualNoisyOr(), specially designed to integrate positive and negative effects between Boolean variables.
Naturally, the networks generated by Hellixia MUST undergo rigorous evaluation by Subject-Matter Experts. This verification is crucial not only from a qualitative point of view to ensure that the network accurately represents real causal relationships but also from a quantitative point of view to confirm the relevance of the suggested causal effects.
Let's delve further into this domain by exploring the underlying causes of "Microbial Infection." To do this, we select the respective node in the network and proceed to the Causal Network Generator.
Displayed below is the generated causal network, showcasing the expanded view with detailed aspects of Microbial Infection. The yellow nodes are common to both the original and expanded networks, the grey nodes represent the original network nodes only, and the red nodes indicate the newly added dimensions specific to microbial infection.
We finally use the Hellixia Report Analyzer to generate a concise summary of the (Causal) Relationship Analysis Report.
We will now adopt a different workflow to construct a Causal Network for Atopic Dermatitis. We start by using Hellixia's Dimension Elicitor to identify relevant dimensions. With these nodes generated, we diverge from our usual practice of generating embeddings for semantic networks. Instead, we utilize Hellixia's new Causal Relationships Finder feature to automatically create a Causal Network based on our set of selected nodes.
We select a range of keywords to guide the Dimension Elicitor process in Hellixia, encompassing various aspects of the domain under study. These keywords include 'Accelerators,' 'Catalyzers,' 'Causes,' 'Drivers,' 'Mechanisms,' 'Consequences,' 'Symptoms,' 'Inhibitors,' 'Moderators,' 'Preventers,' and 'Treatments.'
We run the Causal Relationships Finder on the nodes elicited for the Atopic Dermatitis Mechanism. This tool examines potential causal connections among these nodes and, if required, generates latent variables to enhance the network's explanatory power.
Similar to the Causal Network Generator, the tool does more than identify causal links; it also quantifies the causal effects, which are represented on a scale ranging from -100 (indicated in red) to 100 (indicated in blue).
We conclude this section by utilizing the Hellixia Report Analyzer, which efficiently generates a concise summary of the (causal) Relationship Analysis Report for this latest network.
Welcome to our specialized section on creating Causal Semantic Networks. This segment is dedicated to showcasing the process and benefits of constructing networks that represent the semantic relationships between different factors and set the causal orientations that drive those relationships. Through various case studies and demonstrations, we will illustrate how Hellixia, our subject matter assistant, aids in identifying and defining these causations. From historical events to scientific phenomena, these causal semantic networks will provide a rich, contextual understanding of complex systems. Let's embark on this journey of exploration and insight, seeking to make the invisible visible and the complex comprehensible.
In the complex and dynamic field of air transport, it is crucial for airlines to understand and mitigate flight delays. With the advent of sophisticated analytical tools like Hellixia, we now have the opportunity to delve deeper into the causal factors behind these delays. This article explores the innovative application of Hellixia in the creation of Causal Bayesian Networks (CBN), a method that transcends traditional data analysis to uncover the root causes of flight delays.
Using Hellixia for this purpose represents a significant advance in the field of causal analysis. By building causal Bayesian networks, we can map the complex web of factors contributing to delays, from weather conditions to logistical challenges.
In the following sections, we'll look at how Hellixia facilitates the construction of these causal networks, and the insights they provide into the management and prevention of flight delays.
First, we will perform a semantic analysis of the domain to obtain an overview of the key concepts and variables within the aircraft delay domain.
For our analysis of "Delays in scheduled flight departures", we start by building a semantic network, followed by a hierarchical semantic network, similar to our previous workflows (for example, as demonstrated with Hamlet). This process is essential for mapping the semantic landscape surrounding flight delays, providing a solid foundation for understanding the underlying dynamics of this issue.
We begin our analysis by creating a node entitled "Delays in scheduled flight departures" and proceed to use Hellixia's Dimension Elicitor, using two distinct groups of keywords: 'Ancestors' and 'Descendants'. This approach allows us to explore in depth the factors leading to and resulting from flight delays.
We carefully examine the dimensions provided by Hellixia, removing any that seem extraneous or irrelevant to our analysis. Next, we exclude 'Delays in scheduled flight departures' and run the Embedding Generator on the remaining nodes. This step is crucial to understanding the semantic relationships linked to their names and comments.
We have two large sets of nodes: one representing "Ancestors" (42 nodes) and the other "Descendants" (69 nodes). Our approach is to learn a separate network for each group. To do this, we define specific constraints that prohibit relationships between nodes that do not belong to the same class.
We then run the Maximum Weight Spanning Tree algorithm to find the most significant semantic relationships between nodes.
To improve visibility, we change the node styles to Badges, clearly displaying the comment associated with node. Next, we run the Dynamic Grid Layout to position the nodes on the graph. It's important to note that this algorithm is not deterministic, resulting in random orientations - vertical, horizontal or mixed. As a result, we may have to apply this layout several times to get a configuration that matches your preferences.
Next, we switch to Validation Mode and opt for the Skeleton View. In this context, since our network doesn't represent causal relationships, this view is particularly useful as it retains only the connections between nodes, omitting direction indicators.
Next, we run Variable Clustering. This step categorizes variables that are similar, grouping them based on the semantic relationships identified between them.
We can now proceed with the creation of two hierarchical semantic networks.
Opening Class Editor: We begin by accessing the Class Editor and then running the Class Description Generator. This generates descriptive names for the factors we're examining.
Exporting Descriptions: Next, we use the Export Descriptions function to save the newly created factor descriptions.
Returning to Modeling Mode: We then switch back to Modeling Mode and conduct Multiple Clustering to create latent variables.
Running the Structural Learning Algorithm (Taboo): We run the Taboo algorithm for structural learning, ensuring that the Delete Unfixed Arcs option is selected.
Renaming Latent Variables with Exported Descriptions: We utilize the descriptions we previously exported as a Dictionary to rename the latent variables, adding clarity to our model.
Switching to Validation Mode and Running Node Force: Finally, we go back to Validation Mode and run the Node Force analysis, which helps us understand the dynamics and strength of the connections within our network.
Having established a global understanding of the domain via semantic networks, we're now ready to move forward with the construction of causal Bayesian networks, taking advantage of the latest capabilities introduced in Hellixia as part of BayesiaLab version 11.2.
We initiate the process by creating a node named Delays in Scheduled Flight Departures and then proceed to use the Causal Network Generator feature.
After one or two minutes, due to the complexity of the prompt, we manage to generate a small but fully specified causal Bayesian network (graph and probabilities). This network features directed arcs to signify causal relationships, with each arc accompanied by a succinct explanation of its causal link and an estimate of the effect, scaled from -100 (shown in red) to 100 (shown in blue).
To differentiate nodes by depth using different colors, we first run the Edit Class function. Next, we select Generate a Predefined Class - Depth. Next, we select the four depth classes that have been created and apply the Colors - Associate Random Colors with Classes function to assign distinct colors to each class.
Nodes marked with an icon representing a function are parameterized using BayesiaLab's new DualNoisyOr() formula. This formula integrates both positive and negative interactions between Boolean variables (the causal effects returned by Hellixia).
By selecting the Create Corresponding Structural Priors option in the Causal Network Generator wizard, we now have access to Structural Priors. The value of each prior is derived from the absolute value of the causal effect returned by Hellixia. In addition, the explanation provided for each prior corresponds to the description of its causal relationship. These structural priors can then be used later for network learning when relevant data becomes available.
To finalize this first causal network, we employ the Hellixia Image Generator to create unique icons for each node, based on the comment.
Let's move on to the creation of a more complex causal network by setting Complexity to High.
The next crucial step is an in-depth examination of this automatically generated network. For example, we observe that Fueling Delays is identified as a direct cause. Interestingly, Aircraft Turnaround Time is also identified as a direct cause. This leads us to speculate that Fueling Delays could be a direct cause of Aircraft Turnaround Time, which would have an indirect effect on flight delays.
To verify this hypothesis, we select the two nodes, Fueling Delays and Aircraft Turnaround Time, and apply the Hellixia Pairwise Causal Link feature. This will help us ascertain the nature of the causal relationship between these variables.
Hellixia validates the existence of this causal relationship and accordingly updates the conditional probability distribution of Aircraft Turnaround Time. This update incorporates a DualNoisyOr() function with a coefficient of 0.75, reflecting the quantified impact of Fueling Delays on Aircraft Turnaround Time.
Following this update, our next step involves removing the direct link from Fueling Delays to Delays in Scheduled Flight Departures. Subsequently, we need to adjust the DualNoisyOr() formula to accurately reflect this change in the network's structure.
Driven by curiosity to delve deeper, we select the relevant node to explore the causes of causes. For this, we once again make use of the Causal Network Generator, but on Fueling Delays.
Upon reviewing the newly added nodes and relationships, we identified that three relationships were incorrectly marked as negative, contrary to the descriptions in their respective link comments. To rectify this, we change the color of these links to accurately reflect their positive nature and accordingly update the DualNoisyOr() formula of Operational Efficiency.
To conclude our analysis, we're going to build a final causal network, this time using the Causal Relationships Finder function. Unlike the Causal Network Generator, which added new nodes for creating the network, this feature works directly with selected nodes. To begin with, we use the Dimension Elicitor tool to identify the 5 main Causes and 5 main Effects associated with Delays in Scheduled Flight Departures.
We proceed by selecting the 10 causes and effects, along with the Delays in Scheduled Flight Departures node. With these nodes selected, we then run the Hellixia Causal Relationships Finder to create the network.
As a result, we obtain the bow-tie network structure below.
This brings us to the end of our article. For further insights, we invite you to view the recorded webinar on this topic, which was conducted in January 2024.
A Causal Knowledge Discovery Case Study in Dermatology
Skin hyperpigmentation is a common condition where patches of skin become darker than the surrounding skin. This conceptual example explores opportunities for developing new treatments and therapies. The starting point of any such endeavor should be a thorough causal understanding of the problem domain.
In this example, we leverage the capabilities of Hellixia, BayesiaLab's new subject matter assistant, to analyze the cause-and-effect interplay related to this skin condition.
Our focus is on constructing a comprehensive causal semantic network that highlights the factors influencing the onset and severity of hyperpigmentation. From genetic predispositions and environmental triggers to lifestyle habits, we search for the connections that are relevant to this condition. This exploration offers insights into the dynamics of skin hyperpigmentation.
Create a node named "Skin Hyperpigmentation with Visible Light."
Use the following keywords to guide the Dimension Elicitor's node analysis: Causes, Effects, Milestones, and Mechanisms, and set the General Context to "Dermatology."
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Skin Hyperpigmentation with Visible Light" node.
Change the style of all nodes to "Badges". This will display the comment within each node.
Given that the keywords 'Causes' and 'Effects' already embody causal semantics, our primary task now is to manually scrutinize the relationships between the nodes generated by the keywords "Mechanisms" and "Milestones". Generating embeddings and using structural learning can be beneficial during this analysis phase.
Manually draw arcs between the nodes to denote a causal relationship.
Select all arcs and utilize Hellixia's Explanation of Causal Arcs. If Hellixia concurs with the proposed causal relationship, it will provide an explanation, which will then be associated with the arc comments.
Run the Genetic Grid Layout: This will arrange the nodes on your graph while considering the causal directions of the connections. It positions the nodes so that the causal flow, as represented by the directed arcs, generally goes from the top of the graph toward the bottom, thereby providing a clear, hierarchical visual representation of the causal relationships.
This section showcases how to use Hellixia to create semantic networks created around a wide array of subjects, including authors, philosophers, dogs, and more.
This section features videos demonstrating how Hellixia can be utilized to construct semantic networks to examine a particular field or text.
In this section, we demonstrate how Hellixia can be utilized to form a semantic network from the 145 keywords provided by the Dimension Elicitor, illustrating the semantic connections between these keywords.
Create Nodes: create a node for each keyword by importing a CSV file where all the keywords are located on the first line, followed by a '0' on the second line and a '1' on the third line for each keyword. This structure will help BayesiaLab interpret these columns as variables.
Generate Embeddings: Once you have created your nodes, select them all and use the Embedding Generator. This tool will capture the semantic meaning associated with the node names.
Learn Semantic Relationships: Use the Maximum Weight Spanning Tree algorithm to learn the semantic relationships between these nodes (variables). This algorithm will create the most significant connections between the nodes, forming a tree structure that maximizes the total weight of the tree.
Automatic Node Positioning: Apply the Symmetric Layout algorithm to the nodes for automatic positioning. This will organize your nodes in a visually clear and understandable way.
Switch to Validation Mode and conduct a Node Force Analysis.
This demonstration of Hellixia illustrates how to generate a semantic network from a well-known quote by Michel de Montaigne, a prominent French Renaissance philosopher, writer, and essayist:
Le plus sage homme qui fut onques, quand on lui demanda ce qu’il savait, répondit qu’il savait qu’il ne savait rien.
Il vérifiait ce qu’on dit, que la plus grande part de ce que nous savons est la moindre de celles que nous ignorons : c’est-à-dire que cela même que nous pensons savoir, c’est une partie, et bien petite, de notre ignorance.
The wisest man who ever lived, when asked what he knew, replied that he knew that he knew nothing.
He confirmed what is said, that the greatest part of what we know is the least of what we do not know: that is to say, even what we think we know is a small part of our ignorance.
Venture with us into the fascinating world of the Labrador Retriever, an incredibly cherished dog breed across the globe. Harnessing the power of Hellixia, we will delve into the various characteristics that define this breed. From its temperament and physical attributes to its historical background and unique quirks, we will construct a detailed semantic network that reveals the intricate aspects of the Labrador Retriever. Join us as we delve into understanding what makes this breed so special and universally adored.
Create a node named "Labrador Retriever".
Use the following keywords to guide the Dimension Elicitor in its node analysis: Advantages, Aims, Behaviors, Characteristics, Competitors, Components, Definitions, Descriptions, Dimensions, Elements, Factors, Features, and Traits.
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Labrador Retriever" node.
Use the Embedding Generator on all remaining nodes.
Run the Maximum Weight Spanning Tree algorithm to create a semantic network.
Change the style of all nodes to "Badges". This will display the comment within each node.
Run the Dynamic Grid Layout to organize the nodes on your graph. Note that this algorithm's output is not deterministic; it may favor vertical, horizontal, or mixed orientations. Execute this layout multiple times until you find the most suitable arrangement.
Switch to Validation Mode.
As the graph you are building does not represent causal relationships, opt for the Skeleton View. This will remove all arc directions, leaving only the node connections without any specified direction.
Switch back to Modeling Mode.
Change all node styles to Discs.
Use the Symmetric Layout to organize your nodes in the graph.
Go to Validation Mode.
Conduct a Node Force analysis to evaluate the strength of associations in your graph.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
Welcome to an engaging exploration of man's best friend, as seen through the lens of semantic networks. In this example, we will use the power of Hellixia to unravel the intricate web of relationships between different dog breeds.
Whether you're a canine enthusiast, a professional breeder, or simply curious about the method, you'll find this demonstration enlightening and entertaining. Let's embark on this journey to better understand the world of dog breeds.
Create a node named "Dog Breeds".
Use the Dimension Elicitor, and enter "Sample" as a single keyword to extract the 10 main breeds.
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Exclude the "Dog Breeds" node.
Select the 10 created nodes.
Open the Dimension Elicitor and enter "Competitors" as the keyword.
Set the General Context to "Dog Breeds". This ensures that the elicitor will only consider elements related to "Dog Breeds" during the analysis.
Adjust the settings of the Dimension Elicitor to extract 10 breeds per node.
Run the Dimension Elicitor with the node name as the Main Subject of the Query.
Review the results. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Repeat the same workflow on the new nodes.
Inspect the dimensions suggested by Hellixia. Any dimensions that are irrelevant or redundant should be removed from your analysis.
Select all remaining nodes on your graph.
Open the Embedding Generator tool: Set the Linguistic Unit to "Node Name" and "Node Comment": The linguistic unit refers to the part of the node that the Embedding Generator will use. In this case, it will analyze both the node names (i.e., the breeds of the dogs) and the node comments (i.e., the descriptions of the breeds).
Run the Embedding Generator.
Run the Maximum Weight Spanning Tree algorithm to create a semantic network.
Change the style of all nodes to "Badges". This will display the comment within each node.
Run the Dynamic Grid Layout to organize the nodes on your graph. Note that this algorithm's output is not deterministic; it may favor vertical, horizontal, or mixed orientations. Execute this layout multiple times until you find the most suitable arrangement.
Switch to Validation Mode.
As the graph you are building does not represent causal relationships, opt for the Skeleton View. This will remove all arc directions, leaving only the node connections without any specified direction.
Switch back to Modeling Mode.
Change all node styles to Discs.
Use the Symmetric Layout to organize your nodes in the graph.
Go to Validation Mode.
Conduct a Node Force analysis to evaluate the strength of associations in your graph.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and apply Node Force.
In this tutorial, we employ Hellixia to explore the concept of "Risk Analysis" in all its facets.
Hellixia retrieves an array of concepts related to risk analysis from ChatGPT and then generates a new node for each concept. Furthermore, Hellixia adds a descriptive comment to each node, as provided by ChatGPT.
Next, Hellixia generates embeddings based on the node names and comments. As a result, each node now features a vector of 1,536 numbers representing its semantic context.
Using BayesiaLab's discretization function, each node's vector is binned into quintiles.
On this basis, an unsupervised learning algorithm, such as Maximum Weight Spanning Tree (MWST), learns a semantic network with the given nodes.
The new Dynamic Grid Layout can now arrange the network into an easily-readable format.
Running the Node Force Analysis, we see the strongest nodes and their connections in the network.
In the context of machine learning and natural language processing (NLP), embedding refers to a mathematical representation of a word, phrase, sentence, or any other linguistic unit in a continuous vector space. Word embeddings, in particular, are widely used representations that capture the semantic and syntactic properties of words.
A semantic network is a graphical representation of knowledge or concepts organized in a network-like structure. It is a form of knowledge representation that depicts how different concepts or entities are related to each other through meaningful connections.
In a semantic network, concepts are represented as nodes, and their relationships are depicted as labeled links or arcs. These links indicate the connections or associations between the concepts, such as hierarchical, associative, or causal relationships.
Your Expert Assistant for Semantic Network Analysis
Unlock the power of semantic network analysis with Hellixia Guide, a specialized assistant designed to enhance your experience with Hellixia. Whether you're a beginner or an advanced user, Hellixia Guide provides detailed insights and workflows for creating, analyzing, and interpreting semantic networks. From node force analysis to constructing hierarchical semantic structures, this guide is your go-to resource for navigating the complexities of semantic networks in Hellixia with ease and precision.
The first step in formulating a new Bayesian network about a problem domain is typically defining the dimensions of that domain. This would also be the first step in the BEKEE workflow (seeBayesia Expert Knowledge Elicitation Environment (BEKEE))
Depending on the familiarity with the field of study, exploring a subject's facets and aspects may require a significant brainstorming effort. The Hellixia Dimension Elicitor assists by querying Large Language Models and proposing a list of dimensions.
To illustrate the Dimension Elicitor, we want to discover the dimensions related to the concept of "Bayesian Belief Networks."
Create a node representing the subject of interest, e.g., "Bayesian Belief Networks."
Move your pointer to the desired location to place your new node on the Graph Panel.
Give the node a meaningful name representing the subject to be studied, i.e., "Bayesian Belief Networks."
You can also add a Long Name and a Node Comment to provide more information.
Select the newly-created node, and then select Main Menu > Hellixia > Dimension Elicitor
, which brings up the Dimension Elicitor Window.
In the Question Settings of the Dimension Elicitor Window, specify the keywords to be investigated. The list offers 145 keywords that Hellixia can use to query ChatGPT.
Select Advantages, Characteristics, Components, Contributions, Dimensions, and Strengths as Keywords to follow our example.
Responses per Keyword specifies the maximum number of items to be retrieved per keyword.
Exclude Duplicates automatically removes duplicates from the list of results. This is helpful as the query can produce identical Dimensions in the context of different Keywords.
Depending on your OpenAI account and available resources, you can select the appropriate Completion Model from the dropdown menu, e.g., GPT-3.5 or GPT-4,
You can provide additional context by submitting a Knowledge File.
This text file allows you to specify a broader context for a query.
For example, you might embed chunks of documents related to your domain of study into a dataset.
Then, you can identify and use the chunks with embeddings closest to that of your query to construct your Knowledge File.
You can also provide a General Context for the query, e.g., "Artificial Intelligence."
The Main Subject of the Query is determined by the selected nodes.
You can use the Node Name, the Node Long Name, or the Node Comments.
Node Longe Names and Node Comments have the advantage that they can include longer text and provide more information for the query.
Both the Node Long Names and Node Comments are optional properties of a node. If they are selected as a Main Subject for the Query but have no content, Hellixia will use the Node Name by default.
Click Submit Query to start the elicitation process.
Once the query is complete, a table at the bottom of the window shows the results.
The Subject Node column displays the Main Subject of the Query.
The Keyword column lists the keyword used for the dimension retrieved in that row.
The Index column assigns an index to each dimension retrieved for a Keyword.
The Comment column further describes the dimension retrieved. This comment will also be used as a Node Comment.
The Keep column indicates which Keyword/Dimensions row to keep. If you checked Exclude Duplicates, only unique Keyword/Dimension combinations will be kept.
However, you can modify the selection by checking and unchecking items in the Keep columns.
All Dimensions are added as nodes to the Graph Panel upon clicking OK.
If you select the option Create a Class per Keyword, the Dimension nodes are grouped by their associated Keyword. Additionally, a Note is added to visually group each set of nodes corresponding to a particular Keyword/Dimension.
To utilize the Hellixia functions, BayesiaLab must connect to the OpenAI or Mistral APIs using a personal API Key.
OpenAI and Mistral are third-party services that can be accessed through BayesiaLab; however, it is not part of the BayesiaLab software. As a result, Bayesia makes no representations.
A subscription fee payable to OpenAI or Mistral may be required to obtain your personal API Key.
Obtain your personal API key from the OpenAI website.
Once you have obtained your API Key, enter it into your locally-installed BayesiaLab software under Main Menu > Windows > Preferences > Tools > Hellixia
If you want to utilize an alternative to OpenAI and Mistral, you can deploy models in your own Microsoft Azure account for example. The process involves creating endpoints.
The URL is structured as follows: https://{your-resource-name}.openai.azure.com/openai/deployments/{deployment-id}/chat/completions?api-version={api-version}
."
In this URL:
{your-resource-name}
should be replaced with the name of your Azure OpenAI resource.
{deployment-id}
should be replaced with the ID of the specific deployment.
{api-version}
should be replaced with the version of the API you're using. This follows the YYYY-MM-DD format.
If you're operating behind a proxy that enforces SSL rewriting or redirection, you might encounter the following error message:
'PKIX path building failed: sun.security.provider.certpath.SunCertPathBuilderException: unable to find valid certification path to requested target.'
If you encounter this issue, it will be necessary to point BayesiaLab towards the truststore, where the approved certificates are kept.
Go to Main Menu > Windows > Preferences > General.
Click on the folder icon to locate and select the BayesiaLab.cfg file.
Navigate to the end of the file, where you'll locate the [JavaOptions] section.
If you're using Windows, you should add the following two lines:
java-options=-Djavax.net.ssl.trustStoreType=Windows-ROOT
java-options=-Djavax.net.ssl.trustStore=NUL
For MacOSX users, instead, add:
java-options=-Djavax.net.ssl.trustStoreType=KeychainStore
java-options=-Djavax.net.ssl.trustStore=/dev/null
After these changes, save the file and then restart BayesiaLab for the updates to take effect.
Select Toolbar > Node Creation Mode
For further information, visit Microsoft's official documentation at
Step into the world of William Shakespeare's "Macbeth," a profound tragedy that navigates the treacherous terrain of ambition, power, and the human psyche. In this section, we'll embark on a comprehensive exploration of this iconic play, divided into two illuminating parts:
1. Narrative Analysis: We'll dissect the plot's complexities, unravel character dynamics, and spotlight key events that shape Macbeth's tragic trajectory.
2. Holistic Analysis: Beyond the surface, we'll step back to capture overarching themes, moral implications, and the timeless resonance that gives "Macbeth" its enduring impact.
Join us on this analytical odyssey as we traverse the profound layers of Shakespeare's masterpiece, using semantic networks to illuminate its essence and offer fresh insights into the complexities of the human condition.
Uncover the plot's intricacies, character dynamics, and pivotal moments in the dedicated narrative analysis of "Macbeth." With the guidance of Hellixia, we'll unravel the story's threads, shedding light on the twists and turns that drive this iconic tragedy.
Start by creating the node "Macbeth, by Shakespeare."
Use the Dimension Elicitor, employing a broad array of keywords: Agents, Contexts, Developments, Entities, Events, Highlights, Keywords, Locations, Milestones, Motifs, Progressions, and Relationships.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Macbeth, by Shakespeare" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on their semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors. Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Transitioning from the narrative, our focus shifts to the broader canvas of "Macbeth." Through Hellixia's lens, we'll delve into overarching themes, explore moral complexities, and unearth the enduring significance beneath the surface.
Follow the workflow outlined in the Narrative Analysis section, but use this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Theses, and Values.
Venture into the haunting narrative of "The Horla," Guy de Maupassant's masterful exploration of sanity's fragile line and the unknown's unsettling embrace. In this section, with Hellixia as our analytical compass, we will journey through two distinct facets of this chilling tale:
Narrative Analysis: We'll dissect the plot intricacies, key events, and character dynamics, laying bare the psychological currents that drive this unsettling story forward.
Holistic Analysis: Beyond the immediate narrative, we'll step back to capture the broader themes, motifs, and overarching sentiments that give "The Horla" its enduring resonance.
Together, let's plunge into the depths of this classic horror story, using semantic networks to illuminate its layers and offer fresh insights into Maupassant's unsettling vision.
In this section, we'll unravel the plot intricacies, key events, and character dynamics that form the backbone of Maupassant's haunting tale. Through the lens of Hellixia, witness the story's unfolding as we navigate its chilling corridors.
Start by creating the node "The Horla, by Guy de Maupassant."
Use the Dimension Elicitor, employing the keywords "Context, Developments, Entities, Events, Keywords, Locations, Milestones, Motifs, Progressions, and Relationships," to conduct an exhaustive narrative analysis of the book.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "The Horla, by Guy de Maupassant" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and change the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Execute Variable Clustering: This operation will categorize analogous variables based on semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors in question. Use the Export Descriptions function, and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Given the size of this network, we can focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
Transitioning from the narrative, we now embark on a holistic exploration of "The Horla." With Hellixia's insights, we'll delve into the deeper themes, emotions, and overarching concepts that permeate Maupassant's masterpiece, capturing its essence beyond just the storyline.
Follow the workflow outlined in the Narrative Analysis section, but use this set of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Themes, Theses, and Values.
Welcome to the Holistic Analysis of Salman Rushdie's "Midnight's Children," facilitated by the advanced tools of Hellixia. In this comprehensive exploration, we traditionally delve into the multifaceted narrative, characters, and themes of Rushdie's iconic work.
Adding a new dimension to our analysis, we will now also utilize the innovative Hellixia Report Analyzer feature. This state-of-the-art tool is adept at providing a useful summary of the novel's domain, focusing on the nuanced analysis of node forces and the strengths of the relationships within the story's network.
By integrating this feature into our holistic analysis, we aim to not only maintain our thorough examination but also enhance it with a succinct and insightful summary, capturing the essence of Rushdie's narrative in a way that complements our deep dive into the text.
Start by creating the node "Midnight's Children, by Salman Rushdie"
Use the Dimension Elicitor, employing a broad array of keywords: Achievements, Characteristics, Components, Concepts, Considerations, Contributions, Domains, Elements, Emotions, Features, Feelings, Forces, Ideas, Impacts, Perspectives, Purposes, Sentiments, Subjects, Theses, and Values.
Inspect the dimensions returned by Hellixia and eliminate any that seem superfluous or unrelated to your analysis. Next, disregard the "Midnight's Children, by Salman Rushdie" node and run the Embedding Generator on all remaining nodes to apprehend the semantic associations of their names and comments.
Use the Maximum Weight Spanning Tree algorithm to generate a semantic network.
Change node styles to Badges to ensure each node's comment is visible. Then, apply the Dynamic Grid Layout to position the nodes on your graph; remember that this algorithm is not deterministic, and its orientation—vertical, horizontal, or mixed—is random. You might need to execute this layout several times to obtain an arrangement that aligns with your taste.
Switch over to Validation Mode and select Skeleton View. Since your network doesn't represent causal relations, Skeleton View will maintain only node connections without indicating a direction.
Return to Modeling Mode and alter the node styles to Discs.
Use the Symmetric Layout and switch to Validation Mode to run a Node Force analysis.
Switch to Validation Mode.
Generate the Relationship Report. This report returns two key pieces of information: the Node Force, which indicates the influence and importance of each node within the network, and the strength of all relationships as described in the network. This provides a comprehensive view of how nodes are interconnected and the significance of these connections.
Run the Report Analyzer: With the Relationship Report in hand, proceed to run the Report Analyzer. This tool is designed to synthesize the data into a narrative form. It interprets the node forces and relationship strengths to create a story that summarizes the main dynamics of the domain. This narrative provides a digestible and insightful summary of the complex relationships and key elements within the network.
Execute Variable Clustering: This operation will categorize analogous variables based on semantic relationships.
Open the Class Editor and run Class Description Generator to generate descriptive names for the factors.
Use the Export Descriptions function and save the newly created descriptions.
Return to Modeling Mode and run Multiple Clustering to generate latent variables.
Run the structural learning algorithm Taboo. Ensure the "Delete Unfixed Arcs" option is enabled.
Use the descriptions you exported earlier as a Dictionary to rename the latent variables you've created.
Switch to Validation and run Node Force.
Focus on the upper level of the hierarchical network. Below is the Node Force analysis on these factors only, i.e., excluding all manifest variables before the analysis.
After our initial exploration using the Report Analyzer on the network of "manifest variables," we are now set to delve deeper. Our next step involves generating a new report, this time concentrating on the hierarchical network – the domain of latent variables.
In this section, we harness the power of Hellixia, crafting a temporal and causal semantic network to delve into the relationships between 25 philosophers across time. With the help of Hellixia Comment Generator, we construct a Temporal Indice Dictionary, enabling us to set temporal constraints.
Begin by creating a node named "Influential Philosophers".
Utilize the Dimension Elicitor with "Samples" as Keyword. Adjust the Responses per Keyword setting to 25 to ensure a broad collection of answers.
Review the dimensions returned by Hellixia, eliminating any that seem redundant or irrelevant to your analysis.
Select all nodes.
Run the Comment Generator with "Years" as the Keyword, setting the Responses per Keyword to 1, and checking the Node Name as the Main Subject of the Query. Set the Output Settings to Dimension Name. This step replaces the existing comments tied to the nodes with the primary date associated with each philosopher.
Review the comments to ensure their accuracy. Modify BC dates to negative dates.
Export the Node Comments as a Dictionary and associate it with Node Temporal Indices. These indices will be automatically used as structural constraints to orient the arcs from past to future.
Select all nodes.
Run the Comment Generator again, this time using "Field" as Keyword and "Philosophy" as General Context. Set Responses per Keyword to 2, set the Node Name as the Main Subject of the Query, and set the Output Settings to Dimension Name. Make sure to check the box for Append Output to Current Comment. This action appends the current comments associated with the nodes with each philosopher's two main fields of study.
Use the Maximum Weight Spanning Tree algorithm to construct the Causal/Temporal Semantic Network.
Select all nodes and change the node styles to Badges, which allows the display of each node's comment.
Run the Genetic Grid Layout algorithm to efficiently organize the nodes on your graph, reflecting the causal/temporal directionality of the connections.