Abstract
A case study of semantic clustering of scientific articles related to Rough Sets is presented. The proposed method groups the documents on the basis of their content and with assistance of DBpedia knowledge base. The text corpus is first treated with Natural Language Processing tools in order to produce vector representations of the content and then matched against a collection of concepts retrieved from DBpedia. As a result, a new representation is constructed that better reflects the semantics of the texts. With this new representation, the documents are hierarchically clustered in order to form partition of papers that share semantic relatedness. The steps in textual data preparation, utilization of DBpedia and clustering are explained and illustrated with experimental results. Assessment of clustering quality by human experts and by comparison to traditional approach is presented.
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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Szczuka, M., Janusz, A., Herba, K. (2012). Semantic Clustering of Scientific Articles with Use of DBpedia Knowledge Base. 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_5
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DOI: https://doi.org/10.1007/978-3-642-24809-2_5
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