Dimension Elicitor


  • 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 ChatGPT and proposing a list of dimensions.

Usage & Example

  • 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."

  • Select Toolbar > Node Creation Mode

  • 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.

Workflow Illustration

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