Abstract
This paper is a continuation of the research on designing and developing a dialog-based semantic search engine for SONCA system which is a part of the SYNAT project. In previous papers we proposed some extensions of Tolerance Rough Set Model (TRSM), which can be used to improve the search result clustering algorithms. In this paper, we investigate the problem of quality analysis of presented solutions. We propose some semantic evaluation measures to estimate the quality of the proposed search clustering methods. We illustrate the proposed evaluation method on the base of the Medical Subject Headings (MeSH) thesaurus and compare different clustering techniques over the commonly accessed biomedical research articles from PubMed Central (PMC) portal. The experimental results are showing the advantages of our new clustering methods which are implemented in SONCA system.
The authors are partially 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”. The research of the first author has been partially supported by the statutory research grant founded by Polish-Japanese Institute of Information Technology.
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Nguyen, S.H., Świeboda, W., Jaśkiewicz, G. (2013). Semantic Evaluation of Search Result Clustering Methods. In: Bembenik, R., Skonieczny, L., Rybinski, H., Kryszkiewicz, M., Niezgodka, M. (eds) Intelligent Tools for Building a Scientific Information Platform. Studies in Computational Intelligence, vol 467. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35647-6_24
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