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Training the Siebel Smart Answer Knowledge Base Model

Training begins by analyzing a collection of message texts each of which are assigned to one or more categories. This collection is called a corpus and contains the necessary data for training the assigned categories that make up the knowledge base. During the analysis the knowledge base populates the concept models for each category in order to generate the knowledge base model.

For Siebel Call Center, train the knowledge base model based on the topics associated with the business objects that will be retrieved and displayed in response to natural language queries from agents.

A corpus must contain examples of all categorized emails, or requests that you expect to receive in your production environment. By default the number of emails or requests in each category must be roughly the same in order to ensure that each category is given equal weight during the categorization process. Otherwise, the number of emails or requests in each category must be in proportion to the number that you expect to receive in your production environment. Categories in the knowledge base will only return scores (confidence-level percentages) after they have gathered sufficient statistical information, which is driven by the number of emails or requests associated with the categories.