Abstract
Organizing query results into clusters facilitates quick navigation through search results and helps users to specify their search intentions. Most meta-search engines group documents based on short fragments of source text called snippets. Such a model of data representation in many cases shows to be insufficient to reflect semantic correlation between documents. In this paper, we discuss a framework of document description extension which utilizes domain knowledge and semantic similarity. Our idea is based on application of Tolerance Rough Set Model, semantic information extracted from source text and domain ontology to approximate concepts associated with documents and to enrich the vector representation.
The authors are supported by the grant N N516 077837 from the Ministry of Science and Higher Education of the Republic of Poland and by the National Centre for Research and Development (NCBiR) under Grant No. SP/I/1/77065/10 by the strategic scientific research and experimental development program: “Interdisciplinary System for Interactive Scientific and Scientific-Technical Information”.
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Nguyen, S.H., Świeboda, W., Jaśkiewicz, G. (2012). Extended Document Representation for Search Result Clustering. In: Bembenik, R., Skonieczny, L., Rybiński, H., Niezgodka, M. (eds) Intelligent Tools for Building a Scientific Information Platform. Studies in Computational Intelligence, vol 390. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24809-2_6
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