Automated adaptive classification system for bayesian knowledge networks
First Claim
1. A method of classifying a plurality of informational items in an information retrieval system, comprising the steps of:
- detecting an access of a first informational item;
detecting an access of a second informational item;
applying an ensemble of clustering algorithms; and
creating a relationship link between said first informational item and second informational item.
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Abstract
A method and apparatus for use in an information retrieval system is provided, which derives related informational items that have a usage based relationship strength, and which results in an efficient and more accurate dynamic relationship association between informational items. This system comprises the steps and means for, respectively, detecting a selection of at least a first informational item and a second informational item in an information retrieval session. A relationship type is assigned based on characteristic similarities between the first informational item and the second informational item. Additionally, a relationship strength is assigned based on historical frequency of the consecutive selection of the first and second informational items and providing an access to the second informational item upon detection of the first being accessed by a user of the information retrieval system.
Also, in accordance with the principles of the present invention, the extraction of textual database fields; the application of multiple text classification algorithms; the merging of the algorithm results; the encoding of the merged results as a Bayesian-type link; the use of feedback methods to weight, prune and age the relationship link serves to automate and enhance the process of classification in an information retrieval system.
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Citations
25 Claims
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1. A method of classifying a plurality of informational items in an information retrieval system, comprising the steps of:
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detecting an access of a first informational item;
detecting an access of a second informational item;
applying an ensemble of clustering algorithms; and
creating a relationship link between said first informational item and second informational item. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 17, 18, 19, 20, 21, 22)
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13. A Bayesian-type Belief Network wherein the traditional Baysian Belief Network components are modified, the modification comprising:
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a set of random Frequently Asked Questions(FAQ) or Data;
a set of relationships between nodes;
a weight which describes the strength of relationship between each node; and
a network structure which allows cycles and other structures with no limitations.
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14. An apparatus for providing classification of informational items in an information retrieval system comprising:
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means for detecting an access of informational items;
means for applying an ensemble of clustering algorithms; and
means for creating relationship links between said informational items to enhance the effectiveness of said system. - View Dependent Claims (15, 16)
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23. A method for retrieving help information in a system where informational items are not fixedly mapped to one another comprising the steps of:
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determining an efficient path to arrive at a particular help item of interest; and
storing a context in which a help item is sought as well as the path to said help item. - View Dependent Claims (24, 25)
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Specification