Data processing: database and file management or data structures – Database design – Data structure types
Reexamination Certificate
2002-05-10
2008-10-14
Fleurantin, Jean B (Department: 2162)
Data processing: database and file management or data structures
Database design
Data structure types
C707S793000, C707S793000
Reexamination Certificate
active
07437349
ABSTRACT:
A method, system and computer program for adaptively processing a query search. An expanding operation is utilized to expand the query into sub-queries, wherein at least one of the sub-queries is expanded probabilistically. A retrieving operation retrieves the results of the sub-queries, and a merging operation is used to merge the sub-query results into a search result. An adapting operation is configured to modify the search such that the relevance of the search result is increased when the search is repeated.
REFERENCES:
patent: 5404507 (1995-04-01), Bohm et al.
patent: 5640553 (1997-06-01), Schultz
patent: 5737734 (1998-04-01), Schultz
patent: 6470333 (2002-10-01), Baclawski
patent: 6581055 (2003-06-01), Ziauddin et al.
patent: 6728706 (2004-04-01), Aggarwal et al.
patent: 6766320 (2004-07-01), Wang et al.
patent: 7152064 (2006-12-01), Bourdoncle et al.
Ellen M. Voorhees, Query Expansion using Lexical-Semantic Relations, Jul. 3-6, 1994; Springer-Verlag—Berlin, Germany, 62-69.
Maria Luisa Barja et al., Informia: a mediator for integrated access to heterogeneous information sources, 1998, 1-8.
Maeda, Y., The optimal algorithm for query refinement in information retrieval, Oct. 12-15, 1999, IEEE, 522-526.
Jae-Hyun Lin et al., Query expansion for intelligent information retrieval on Internet, Dec. 10-13, 1997, IEEE, 656-662.
A.F. Smeaton and C.J. van Rijsbergen, The Retrieval Effects of Query Expansion on a Feedback Document Retrieval System, The Computer Journal, vol. 26, No. 3 (1983), pp. 239-246.
F. Cuna Ekmekcioglu et al., Effectiveness of Query Expansion in Ranked-Output Document Retrieval Systems, Journal of Information Science, 18 (1992), pp. 139-147.
Edward Chang and Beitao Li, MEGA—The Maximizing Expected Generalization Algorithm for Learning Complex Query Concepts, University of California, Santa Barbara, pp. 1-39.
Milind R. Naphade and Thomas S. Huang, A Probabilistic Framework for Semantic Video Indexing, Filtering and Retrieval, University of Illinois at Urbana-Champaign, pp. 1-26.
Norbert Fuhr, Probabilistic Models in Information Retrieval, Mar. 4, 1992, pp. 1-21.
Milind R. Naphade et al., Learning to Annotate Video Databases, Pervasive Media Management Group, IBM T J Watson Research Center, Hawthorne, NY 10532, USA.
Gerard Salton and Chris Buckley, Improving Retrieval Performance by Relevance Feedback, Journal of the American Society for Information Science, 41(4):288-297, 1990.
Apostol Natsev et al., WALRUS: A Similarity Retrieval Algorithm for Image Databases.
Gregory Grefenstette, Use of Syntactic Context to Produce Term Association Lists for Text Retrieval, 15th Ann Int'l SIGIR (1992), pp. 89-96.
Yonggang Qiu and H. P. Frei, Concept Based Query Expansion, pp. 1-11.
Carolyn J. Crouch and Bokyung Yang, Experiments in Automatic Statistical Thesaurus Construction, 15th Ann Int'l SIGIR (1992), pp. 77-88.
Eugene Agichtein et al., Learning Search Engine Specific Query Transformations for Question Answering, WWW10 (May 1-5, 2001).
Basu Sankar
Naphade Milind R.
Smith John R.
Fleurantin Jean B
International Business Machines - Corporation
Tuchman Ido
LandOfFree
Adaptive probabilistic query expansion does not yet have a rating. At this time, there are no reviews or comments for this patent.
If you have personal experience with Adaptive probabilistic query expansion, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Adaptive probabilistic query expansion will most certainly appreciate the feedback.
Profile ID: LFUS-PAI-O-3996761