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 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.
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.
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.