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

Authors
  • J. Poelmans
  • D.I. Ignatov
  • S. Viaene
  • G. Dedene
  • S.O. Kuznetsov
Publication date 2012
Host editors
  • P. Perner
Book title Advances in Data Mining : Applications and Theoretical Aspects
Book subtitle 12th Industrial Conference, ICDM 2012, Berlin, Germany, July 13-20 2012: proceedings
ISBN
  • 9783642314872
ISBN (electronic)
  • 9783642314889
Series Lecture Notes in Computer Science
Event 12th Industrial Conference on Data Mining
Pages (from-to) 273-287
Publisher Heidelberg: Springer
Organisations
  • Faculty of Economics and Business (FEB) - Amsterdam Business School Research Institute (ABS-RI)
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.
Document type Conference contribution
Language English
Published at https://doi.org/10.1007/978-3-642-31488-9_22
Permalink to this page
Back