Text mining scientific papers: a survey on FCA-Based information retrieval research

  • Authors:

  • Jonas Poelmans;Dmitry I. Ignatov;Stijn Viaene;Guido Dedene;Sergei O. Kuznetsov

  • Affiliations:

  • Faculty of Business and Economics, K.U. Leuven, Leuven, Belgium,National Research University Higher School of Economics (HSE), Moscow, Russia;National Research University Higher School of Economics (HSE), Moscow, Russia;Faculty of Business and Economics, K.U. Leuven, Leuven, Belgium,Vlerick Leuven Gent Management School, Leuven, Belgium;Faculty of Business and Economics, K.U. Leuven, Leuven, Belgium,Universiteit van Amsterdam Business School, Amsterdam, The Netherlands;National Research University Higher School of Economics (HSE), Moscow, Russia

  • Venue:
  • ICDM'12 Proceedings of the 12th Industrial conference on Advances in Data Mining: applications and theoretical aspects
  • Year:
  • 2012

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Abstract

Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.