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.

Workflow for Creating the Causal/Temporal Semantic Network

  • 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.

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