Neural network feedback for enhancing text search

• US 20030212663A1
• Filed: 05/08/2002
• Published: 11/13/2003
• Est. Priority Date: 05/08/2002
• Status: Abandoned Application

• Current Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Original Assignee: Hewlett-Packard Development Company, L.P. (HP Inc.)
• Inventors: Leno, Doug, Sheedvash, Sassan
• Application Number:
  US10/141,298
• Publication Number:
  US 20030212663A1
• Time in Patent Office: Days
• US Class Current: 707/3
• CPC Class Codes: G06F 16/3338 Query expansion
• CPC Class Codes: G06F 16/3349 Reuse of stored results of previous queries
• CPC Class Codes: G06F 16/38 Retrieval characterised by using metadata, e.g. metadata not derived from the content or metadata generated manually

Abstract

An Artificial Neural Network (ANN) based search method and system for enhancing and assisting the task of specifying the required information in the query by combining the user'"'"'s original query with additional information previously provided by the expert users. That is, the ANN based search system utilizes the expert community feedback in predicting the relevance of particular documents and dynamically builds statistical associations between the queries and known solutions, i.e., relevant documents, identified by the expert users.

Claims

1. An Artificial Neural Network (ANN) based method for searching documents in a knowledge store, comprising the steps of:

searching the knowledge store for documents relevant to a user query;

determining whether said user query relates to one or more previously processed expert query;

modifying said user query with relevance feedback to provide a modified query if it is determined that said user query relates to one of said expert queries; and

searching the knowledge store for documents relevant to said modified query to provide relevant documents.

2. The method of claim 1 wherein the step of determining determines said user query is related to one of said expert queries if a relatedness measure exceeds a predetermined threshold.

3. The method of claim 1 further comprising:

step of determining statistical associations between said user query and said relevant documents.

4. The method of claim 3 further comprising:

step of displaying said relevant documents in order of its relevancy based on at least one of the following;

said statistical associations and said relevance feedback.

5. The method of claim 3 further comprising:

step of clustering said knowledge store based on at least one of the following;

said statistical associations and said relevancy feedback.

6. A method for searching documents in a knowledge store, comprising the steps of:

providing an Artificial Neural Network (ANN) system for enhancing user'"'"'s search for documents in the knowledge store;

training the ANN system using expert queries to supplement user queries;

determining whether a user query relates to one or more previously processed expert query;

modifying said user query with relevance feedback to provide a modified query if it is determined that said user query relates to one said expert queries; and

searching the knowledge store for documents relevant to said modified query to provide relevant documents.

7. The method of claim 6 wherein the step of training comprises:

searching the knowledge store for documents relevant to an expert query from a domain-specific expert;

marking one or more of said relevant documents as being relevant if it is determined that a document is relevant to said expert query by said expert; and

analyzing text of said marked document to determine relevance feedback.

8. The method of claim 7 wherein said relevance feedback represents terms and concepts that are statistically relevant to said expert query.

9. An artificial neural network (ANN) system for searching documents in a knowledge store, comprising:

a search engine for searching the knowledge store for documents relevant to a user query; and

an ANN decision system for determining whether said user query relates to one or more previously processed expert query, and modifying said user query with relevance feedback to provide a modified query if it is determined that said user query relates to one of said expert queries; and

wherein said search engine is operable to search the knowledge store for documents relevant to said modified query to provide relevant documents.

10. The ANN system of claim 9 wherein said ANN decision system is operable to determine said user query is related to one of said expert queries if the relatedness measure exceeds a predetermined threshold.

11. The ANN system of claim 9 wherein said ANN decision system is operable determine statistical associations between said user query and said relevant documents.

12. The ANN system of claim 11 further comprising:

a display device for displaying said relevant documents in order of its relevancy based on at least one of the following;

said statistical associations and said relevance feedback.

13. The ANN system of claim 11 wherein said ANN decision system is operable to cluster said knowledge store based on at least one of the following:

said statistical associations and said relevancy feedback.