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Relevance Ranking
In evaluating all types of queries, IDOL employs complex algorithms based on a combination of Information Theory and Bayesian methods to automatically weight and rank documents by statistical relevance. In doing so, it makes use of theoretic values calculated dynamically for all concepts on indexing, allowing the relevance of content to be evaluated. In practice, relevance can be seen as a measure of the conceptual overlap between the query text and the text within a document. Additionally, IDOL allows administrators to manipulate the relevance of query results in several ways:
Setting up a field process - users modify IDOL's configuration file, raising the percentage relevance of results according to the number of times that terms in specified fields match the terms in the query
Using BIAS - users can employ the BIAS field specifier to dynamically boost the percentage relevance of a query's results according to the numerical proximity of a specified field to a given value
Using Multipliers - users can multiply the weights of individual query terms in order to boost the relevance of results that match these terms accordingly
Key Benefits
Improves user experience by ordering search results in formats that better match expectations better
Relevance can be customized by administrators to suit business needs and employee roles
Properly implemented relevancy ranking can result in productivity gains and cost savings
Further Reference:
Autonomy's Unique Combinatation of Technologies
Autonomy's Unique Combinatation of Technologies| Functionality |
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Conceptual Search | | | Relevance Ranking | | | Directed Navigation |
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