Result merging methods in distributed information retrieval with overlapping databases

Information Retrieval

Volumn 10, Issue 3, 2007, Pages 297-319

  Wu, Shengli ^a   McClean, Sally ^a  

^a UNIVERSITY OF ULSTER   (United Kingdom)

Author keywords

Distributed information retrieval; Overlapping databases; Result merging

Indexed keywords

EID: 34250217770     PISSN: 13864564     EISSN: 15737659     Source Type: Journal    
DOI: 10.1007/s10791-007-9023-y     Document Type: Article

References (36)

1. Aslam, J. A., & Montague, M. (2003). Models for metasearch. In: Proceedings of the 24th Annual International ACM SIGIR Conference (pp. 24-37). ACM, Gaithersburg, MD.
2. Baeza-Yates, R., & Ribeiro-Neto, B. (1999). Modern information retrieval. Reading, MA: ACM and AddisonWesley.
3. Callan, J. (2000). Distributed information retrieval. Advances in Information Retrieval (pp. 127-150), Kluwer Academic Publishers.
4. Callan, J. K., Lu, Z., & Croft, W. (1995). Searching distributed collections with inference networks. In Proceedings of the 18th Annual International ACM SIGIR Conference (pp. 21-28). ACM, Seattle, WA.
5. Calvé, A. L., & Savoy, J. (2000). Database merging strategy based on logistic regression. Information Processing and Management, 36(3), 341-359.
6. Chowdhury, A., Frider, O., Grossman, D., & McCabe, C. (2002). Collection statistics for fast duplicate document detection. ACM Transactions on Information Systems, 20(2), 171-191.
7. Fox, E. A., & Shaw, J. (1994). Combination of multiple searches. In The Second Text REtrieval Conference (TREC-2) (pp. 243-252). National Institute of Standards and Technology, USA, Gaitherburg, MD.
8. Fuhr, N. ( 1999). A decision-theoretic approach to database selection in networked IR. ACM Transactions on Information Systems, 17(3), 229-249.
9. Gauch, S., Wang, G., & Gomez, M. (1996). Prolusion: Intelligent fusion from multiple, distributed search engines. Journal of Universal Computer Science, 2(9), 637-649.
10. Gravano, L., García-Molina, H., & Tomasic, A. (1999). Gloss: Text-source discovery over the Internet. ACM Transactions on Database Systems, 24(2), 229-264.
11. Hawking, D., & Robertson, S. (2003). On collection size and retrieval effectiveness. Information Retrieval, 9(0,99-105.
12. Hawking, D., & Thistlewaite, P. (1999). Methods for information server selection. ACM Transactions on Information Systems, 17(1), 40-76.
13. Hull, D. A., Pedersen, J. O., & Schüze, H. (1996). Method combination for document filtering. In Proceedings of the 19th Annual International ACM SIGIR Conference (pp. 279-287). ACM, Zurich, Switzerland.
14. Lawrence, S., & Giles, C. L. (1998a). Context and page analysis for improved Web search. IEEE Internet Computing, 2(4), 38-46.
15. Lawrence, S., & Giles, C. L. (1998b). Inquiris, the NECI search engine. Computer Networks (Proceedings of WWW 7), 30(1-1), 95-105.
16. Lawrence, S., & Giles, C. L. (2000). Accessibility of information on the Web. Intelligence, 11. (1), 32-39.
17. Lee, J. H. (1997). Analysis of multiple evidence combination. In Proceedings of the 20th Annual International ACM SIGIR Conference (pp. 267-275). ACM, Philadelphia, PA.
18. Lemur, retrieved on October 25, 2005 from http://www.lemurproject.org/
19. Lucene, retrieved on October 25, 2005 from http://lucene.apache.org/java/ docs/index.html
20. Manmatha, R., Rath, T., & Feng, F. (2001). Modeling score distributions for combining the outputs of search engines. In Proceedings of the 24th Annual International ACM SIGIR Conference (pp. 267-275). ACM, New Orleans, LA.
21. Meng, W., Yu, C., & Liu, K. (2002). Building efficient and effective metasearch engines. ACM Computing Surveys, 34(1), 48-89.
22. Montague, M. (2002). Metasearch: Data fusion for document retrieval (Technical Report TRC2002-424). Hanover, NH: Department of Computer Science, Dartmouth College.
23. Montague, M., & Aslam, J. A. (2002). Condorcet fusion for improved retrieval. In Proceedings of ACM CIKM Conference (pp. 538-548). ACM, McLean, VA.
24. Oztekin, B., Karypis, G., & Kumar, V. (2002). Expert agreement and content based reranking in a meta search environment using Mearf. In Proceedings of the llth International World Wide Web Conference (pp. 333-344). ACM, Honolulu, HI.
25. Rasolofo, Y., Hawking, D., & Savoy, J. (2003). Result merging strategies for a current news metasearcher. Information Processing and Management, 39(4), 581-609.
26. Searchengineshowdown, retrieved on October 25, 2005 from http: //www.searchengineshowdown.com/ stats/overlap, shtml
27. Selberg, E., & Etzioni, O. ( 1995). Multi-service search and comparison using the MetaCrawler. In Proceedings of the 4th International World Wide Web Conference. Darmstadt, Germany.
28. Selberg, E., & Etzioni, O. (1997). The MetaCrawler architecture for resource aggregation on the Web. IEEE Expert, 12(1), 11-14.
29. Selberg, E., & Etzioni, O. (2000). On the instability of web search engines. In Proceedings of RIAO conference (pp. 223-235). Paris, France.
30. Si, L., & Callan, J. (2002). Using sampled data and regression to merge search engine results. In Proceedings of the 25th Annual International ACM SIGIR Conference (pp. 19-26). ACM, Tampere, Finland.
31. Vogt, C. C., & Cottrell, G. A. (1999). Fusion via a linear combination of scores. Information Retrieval, 1 (3), 151-173.
32. Voorhees, E.M., Gupta, N.K., & Johnson-Laird, B. (1995). Learning collection fusion strategies. In Proceedings of the 18th Annual International ACM SIGIR Conference (pp. 172-179). ACM, Seattle, WA.
33. Voorhees, E. M., & Harman, D. K. (Eds.). (1996). In Proceedings of the 5th Text Retrieval Conference. National Institute of Standards and Technology, USA, Gaithersburg, MD.
34. Wu, S., & Crestani, F. (2003). Distributed information retrieval: A multi-objective resource selection approach. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 11(Suppl.), 83-100.
35. Wu, S., & Crestani, F. (2004). Shadow document methods of results merging. In Proceedings of the 19th ACM Symposium on Applied Computing (pp. 1067-1072). ACM, Nicosia, Cyprus.
36. Yuwono, B., & Lee, D. (1997). Server ranking for distributed test retrieval systems on the Internet. In Proceedings of the Fifth international Conference on Database Systems for Advanced Application (pp. 41-50). World Scientific, Melbourne, Australia.