Verifying relevance between keywords and web site contents

Data processing: database and file management or data structures – Database design – Data structure types

Reexamination Certificate

Rate now

  [ 0.00 ] – not rated yet Voters 0   Comments 0

Details

C707S793000, C707S793000

Reexamination Certificate

active

10826162

ABSTRACT:
Systems and methods for verifying relevance between terms and Web site contents are described. In one aspect, site contents from a bid URL are retrieved. Expanded term(s) semantically and/or contextually related to bid term(s) are calculated. Content similarity and expanded similarity measurements are calculated from respective combinations of the bid term(s), the site contents, and the expanded terms. Category similarity measurements between the expanded terms and the site contents are determined in view of a trained similarity classifier. The trained similarity classifier having been trained from mined web site content associated with directory data. A confidence value providing an objective measure of relevance between the bid term(s) and the site contents is determined from the content, expanded, and category similarity measurements evaluating the multiple similarity scores in view of a trained relevance classifier model.

REFERENCES:
patent: 5297042 (1994-03-01), Morita
patent: 5418948 (1995-05-01), Turtle
patent: 5488725 (1996-01-01), Turtle et al.
patent: 5694592 (1997-12-01), Driscoll
patent: 5812134 (1998-09-01), Pooser et al.
patent: 5845278 (1998-12-01), Kirsch et al.
patent: 5987460 (1999-11-01), Niwa et al.
patent: 6003027 (1999-12-01), Prager
patent: 6112202 (2000-08-01), Kleinberg
patent: 6167398 (2000-12-01), Wyard et al.
patent: 6169986 (2001-01-01), Bowman et al.
patent: 6470307 (2002-10-01), Turney
patent: 6556983 (2003-04-01), Altschuler et al.
patent: 6560600 (2003-05-01), Broder
patent: 6615209 (2003-09-01), Gomes et al.
patent: 6711585 (2004-03-01), Copperman et al.
patent: 6742003 (2004-05-01), Heckerman et al.
patent: 6760714 (2004-07-01), Caid et al.
patent: 6892193 (2005-05-01), Bolle et al.
patent: 2002/0015366 (2002-02-01), Sawano
patent: 2002/0178153 (2002-11-01), Nishioka et al.
patent: 2003/0046389 (2003-03-01), Thieme
patent: 2003/0065632 (2003-04-01), Hubey
patent: 2003/0110181 (2003-06-01), Schuetze et al.
patent: 2003/0208482 (2003-11-01), Kim et al.
patent: 2003/0226100 (2003-12-01), Farahat et al.
patent: 2003/0233370 (2003-12-01), Barabas et al.
patent: 2004/0010331 (2004-01-01), Terada et al.
patent: 2005/0015366 (2005-01-01), Carrasco et al.
patent: 2005/0097188 (2005-05-01), Fish
patent: 2005/0216443 (2005-09-01), Morton et al.
patent: 10029644 (2002-01-01), None
patent: 1320042 (2003-06-01), None
patent: 0809197 (2004-02-01), None
patent: WO97/49048 (1997-12-01), None
patent: WO99/48028 (1999-09-01), None
Sharon McDonald et al., Evaluating a Content Based Image Retrieval System, 2001, ACM, 232-240.
Jespersen et al., Evaluating the Markov Assumption for Web Usage Mining, Nov. 7-8, 2003, ACM, 82-89.
D. Jimenez et al., The influence of sementics in IR using LSI and K-means clustering techniques, 2003, ACM, 279-284.
Chakrabarti S.“Data Mining for Hypertext: A tutorial survey” SIGKDD Explorations vol. 1 issue 2, Jan. 2000, 11 pages.
Cohn et al.; “The Missing Link—A Probabilistic Model of Document Content and Hypertext Connectivity” Proceedings of Neural Information Processing Systems, 2001, 7 pages.
Dhillon et al.; “Efficient Clustering of Very Large Document Collections” Data Mining,for Scientific and Engineering Applications, Chapter 1, Kluwer Academic Publishers, 2001, pp. 1-25.
Liu et al.; “Clustering Through Decision Tree Construction” 9th International Conference on Information and Knowledge Management, 2000, 10 pages.
Kleinberg J.M.; “Authoritative Sources in a Hyperlinked Environment ” Proceedings of the ACM-SIAM Symposium on Discrete Algorithms, 1998, 34 pages.
Heer et al.; “Identification of Web User Traffic Composistion using Multi-Model Clustering and Information Scent” 1st SIAM ICDM Workshop on Web Mining. Chicago 2001. 13 pages.
Gibson et al.; “Inferring Web Communities from Link Topology” Proceedings of the 9th ACM Conference on Hypertext and Hypermedia, 1998, 17 pages.
Neville et al; “Iterative Classification in Relational Date” Proceedings AAAI-2000 Workshop on Learning Statistical Models form Relational Data, AAAI Press 2000, pp. 42-49.
Steinbach et al.; “A Comparison of Document Clustering Techniques” 6th ACM SIGKDD World Text Mining Conference Boston, 2000, 2 pages.
Su et al.; “Correlation-based Document Clustering using Web Logs” Proceedings of the 34th Hawaii International Conference on System Sciences, 2001, 7 pages.
Taskar et al.; “Probabilistic Classification and Clustering in Relational Data” Proceedings of the 34th Hawaii International Conference on System Sciences. 2001. 7 pages.
Unger et al.; “Clustering Methods for Collaborative Filtering” In Workshop on Recommendation System at the 15th National Conference on Artifical Intelligence, 1998, 16 pages.
Wen et al.; “Query Clustering Using User Logs” ACM Transactions on Information Systems vol. 20 No. 1; Jan. 2002, pp. 59-81.
Zeng et al; “A Unified Framework for Clustering Heterogeneous Web Objects” Proceedings of the 3rd international Conference of Web Information System Engineering, Singapore 2002, 10 pages.
“Open Directory Project” http://dmoz.org/ Netscape 1998-2004 1 page.
Berkhin P.; “Survey of Clustering Data Mining Techniques” Accrue Software Inc.; 2002 pp. 1-56.
Yang et al.; “A Comparative Study on Feature Selection in Text Categorization” Proceedings of the Fourteenth International Conference on Machine Learning, Morgan Kaufmann Publishers Inc., San Francisco 1997, pp. 412-420.
Slattery S. et al.; “Combining Statistical and Relational Methods for Learning in Hypertext Domains” Lecture Notes in Computer Science, 1998, pp. 38-52.
Kim et al, “A Comparison of Collocation-Based Similarity Measures in Query Expansion”, Information Processing & Management, Elsevier Science Ltd, vol. 35, No. 1, Jan. 1999 pp. 19-30.
Qiu et al, “Concept Based Query Expansion”, SIGIR Forum, Association for Computing Machinery, New York, Jun. 27, 1993, pp. 160-169.
Raghavan et al, “On the Reuser of Past Optimal Queries”, SIGIR '95, Proceedings of the 18th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Jul. 9, 1995, pp. 344-350.
Beeferman D. & Berger A. “Agglomerative clustering of a search engine query log” Proceedings of the ACM SIGKDD. International cnference on Knowledge Discovery and Data Mining, ACM, US, 2000, pp. 407-416, XP002339640.
Srivastava et al. “Discovery and Applications of Usage Patterns from Web Data” Sigkdd Explorations, Jan. 2000 (2000-20001), pp. 12-23, XPoo2241892 *the whole document*.
Attardi, G. et al.: “Automatic web Page Categorization by Link and context Analysis” Proceedings of THIA, European Symposium on Intelligence, 1999. *the whole document*.
Brin S et al: “The anatomy of a large-scale hypertextual web search engine” Computer Networks and ISDN Systems, North Holland Publishing. Amsterdem, NL, vol. 30, No. 1-7, Apr. 1998, pp. 107-117.
Harmandas, V. et al. “Image Retrieval by Hypertext Links” Association For Computing Machinery. Proceedings of the 20th Annual INternational ACM-SIFIR Conference on Research and Development in INformation Retrieval. Philadelphia, PA, Jul. 27-31, 1997, pp. 296-303.
Smith, J.R. et al.: “An Image and Video Search Engine for the World-Wide Web” Storage and retrieval for image and video databases 5. San Jose, Feb. 13-14, 1997, Proceedings of SPIE, Bellingham, SpIE, US, vol. 3022, pp. 84-95.
Westerveld, T. et al: “Retriving Web Pages using Content, Links, URLs and Anchors” Test Retrieval Conference. Proceedings, XX, XX, Nov. 13, 2001,pp. 663-672.
Boyan et al., “A Machine Learning Architecture for Optimizing Web Search Engines”, AAAI Workshop on Internet-Based Information Systems, 1996, pp. 1-8, Retrieved from the Internet http://www.cs.cornell.edu/People/tj/publicatio

LandOfFree

Say what you really think

Search LandOfFree.com for the USA inventors and patents. Rate them and share your experience with other people.

Rating

Verifying relevance between keywords and web site contents does not yet have a rating. At this time, there are no reviews or comments for this patent.

If you have personal experience with Verifying relevance between keywords and web site contents, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Verifying relevance between keywords and web site contents will most certainly appreciate the feedback.

Rate now

     

Profile ID: LFUS-PAI-O-3852176

  Search
All data on this website is collected from public sources. Our data reflects the most accurate information available at the time of publication.