Dimension Elicitor
Context
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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