Maximal termsets as a query structuring mechanism

International Conference on Information and Knowledge Management, Proceedings

Volumn , Issue , 2005, Pages 287-288

  Pôssas, Bruno ^a,b   Ribeiro Neto, Berthier ^a,b   Ziviani, Nivio ^a   Meira Jr , Wagner ^a  

^a FEDERAL UNIVERSITY OF MINAS GERAIS   (Brazil)

^b Google Inc   (Brazil)

Author keywords

Association rule mining; Automatic query structuring; Information retrieval models; Weighting index term co occurrences

Indexed keywords

DATA MINING; INFORMATION RETRIEVAL; INFORMATION TECHNOLOGY; MATHEMATICAL MODELS; PROBLEM SOLVING; QUERY LANGUAGES; SEARCH ENGINES; WEBSITES; WORLD WIDE WEB;

ASSOCIATION RULE MINING; AUTOMATIC QUERY STRUCTURING; INFORMATION RETRIEVAL MODELS; WEIGHTING INDEX TERM CO-OCCURRENCES;

DATA STRUCTURES;

EID: 33745779693     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1099554.1099630     Document Type: Conference Paper

References (5)

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3. B. Pôssas, N. Ziviani, B. Ribeiro-Neto, and W. Meira Jr. Set-based vector model: An efficient approach for correlation-based ranking. ACM Transactions on Information Systems. To appear.
4. M. E. Smith. Aspects of the P-Norm Model of Information Retrieval: Syntatic Query Generation, Efficiency and Theoretical Properties. PhD thesis, Computer Science Department, Cornell University, 1990.
5. M. Srikanth and R. Srihari. Exploiting syntactic structure of queries in a language modeling approach to ir. In CIKM'03: Proceedings of the twelfth international conference on Information and knowledge management, pages 476-483. ACM Press, 2003.