Abstract
This paper summarizes our recent research on semantic clustering of scientific articles. We present a case study which was focused on analysis of papers related to the Rough Sets theory. The proposed method groups the documents on the basis of their content, with an assistance of the DBpedia knowledge base. The text corpus is first processed using Natural Language Processing tools in order to produce vector representations of the content. In the second step the articles are matched against a collection of concepts retrieved from DBpedia. As a result, a new representation that better reflects the semantics of the texts, is constructed. With this new representation the documents are hierarchically clustered in order to form a partitioning of papers into semantically related groups. The steps in textual data preparation, the utilization of DBpedia and the employed clustering methods are explained and illustrated with experimental results. A quality of the resulting clustering is then discussed. It is assessed using feedback form human experts combined with typical cluster quality measures. These results are then discussed in the context of a larger framework that aims to facilitate search and information extraction from large textual repositories.
This work was supported by the grant N N516 077837 from the Ministry of Science and Higher Education of the Republic of Poland, the Polish National Science Centre grant 2011/01/B/ST6/03867 and by the Polish National Centre for Research and Development (NCBiR) under Grant No. SP/I/1/77065/10 in frame of 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. (2013). Semantic Clustering of Scientific Articles Using Explicit Semantic Analysis. In: Peters, J.F., Skowron, A., Ramanna, S., Suraj, Z., Wang, X. (eds) Transactions on Rough Sets XVI. Lecture Notes in Computer Science, vol 7736. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36505-8_6
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DOI: https://doi.org/10.1007/978-3-642-36505-8_6
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