METAXPLORER: AN INTELLIGENT AND ADAPTABLE METASEARCH ENGINE USING A NOVEL OWA OPERATOR

Abstract

World Wide Web has become the main place for searching information on any topic. This makes searching a key activity and thus, search engines the most widely used tools on the Web. However, as the Web continues to expand, the portion of the Web covered by each search engine is decreasing constantly. Metasearch engines address this issue by combining the results of multiple search engines and thereby increasing the search effectiveness. This research work proposes a new model for metasearch, MetaXplorer, which is both intelligent and adaptable. This research work also proposes a novel Ordered Weighted Averaging operator named Intelligent OWA operator, which is capable of handling the dynamic nature of decision making environment. The proposed Intelligent OWA operator is used for result aggregation in MetaXplorer, along with Fuzzy Analytical Hierarchy Process. Furthermore, MetaXplorer analyses the documents returned by individual search engines instead of considering their ranks in search engine result lists alone in the aggregation process, and thus is intelligent. Subjective evaluation of MetaXplorer is provided by comparing it with previously proposed models. This research work also performs the performance evaluation of MetaXplorer in terms of precision. The precision values for MetaXplorer are compared with three existing metasearch engines on the Web. The results indicate that MetaXplorer performs better than the existing metasearch engines and has several features which were not present in the previous models.