Summary
CatS is a meta-search engine that utilizes text classification techniques to improve the presentation of search results. After posting a query, the user is offered an opportunity to refine the results by browsing through a category tree derived from the dmoz Open Directory topic hierarchy. This paper describes some key aspects of the system (including HTML parsing, classification and displaying of results), outlines the text categorization experiments performed in order to choose the right parameters for classification, and puts the system into the context of related work on (meta-)search engines. The approach of using a separate category tree represents an extension of the standard relevance list, and provides a way to refine the search on need, offering the user a non-imposing, but potentially powerful tool for locating needed information quickly and efficiently. The current implementation of CatS may be considered a baseline, on top of which many enhancements are possible.
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Radovanović, M., Ivanović, M. (2006). CatS: A Classification-Powered Meta-Search Engine. In: Last, M., Szczepaniak, P.S., Volkovich, Z., Kandel, A. (eds) Advances in Web Intelligence and Data Mining. Studies in Computational Intelligence, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33880-2_20
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DOI: https://doi.org/10.1007/3-540-33880-2_20
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