A comprehensive OWA-based framework for result merging in metasearch

Authors:
Elizabeth D. Diaz;Arijit De;Vijay Raghavan
Affiliations:
Center of Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;Center of Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA;Center of Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

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Abstract

When a query is passed to multiple search engines, each search engine returns a ranked list of documents. The problem of result merging is to fuse these ranked lists such that optimal performance is achieved as a result of the combination. In this paper, our primary contribution is a result merging method, based on fuzzy set theory that adapts the quantifier-guided, Ordered Weighted Averaging (OWA) operators introduced by Yager. The proposed framework is more comprehensive than the existing OWA operator based method, as our investigation evaluates alternative heuristics for missing documents (those existing exist in some, but not all, ranked lists) in order to place such documents into the ranked lists before merging. It shows that the effectiveness of the merging process is improved over the based strategy known as Borda-fuse.