A Fuzzy Search Engine Weighted Approach to Result Merging for Metasearch

Authors:
Arijit De;Elizabeth D. Diaz;Vijay Raghavan
Affiliations:
Center of Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504,;University of Texas of Permian Basin, Odessa, TX 79762,;Center of Advanced Computer Studies, University of Louisiana at Lafayette, Lafayette, LA 70504,
Venue:
RSFDGrC '07 Proceedings of the 11th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Computing
Year:
2009

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Abstract

Each search engine queried by a metasearch engine returns results in the form of a result list of documents. The key issue is to combine these lists to achieve the best performance. The salient contribution of this paper is a result merging model that applies Yager's fuzzy aggregation Ordered Weight Average, OWA, operator in combination with the concept of importance guided aggregation to extend the OWA-based result merging model proposed by Diaz. Our result merging model, IGOWA, (Importance Guided OWA) improves upon the OWA model proposed by Diaz so as to allow weights to be applied to search engine result lists. To support our model we also explore a scheme for computing search engine weights. We call the weights obtained from our scheme Query-System Weights and we compare this with the scheme for computing search engine weights proposed by Aslam and Montague. We refer to Aslam's scheme as System Weights.