Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research
J. Poelmans, D. Ignatov, S. Viaene, G. Dedene, and S. Kuznetsov. Advances in Data Mining. Applications and Theoretical Aspects, volume 7377 of Lecture Notes in Computer Science, Springer Berlin Heidelberg, (2012)
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.
Description
Text Mining Scientific Papers: A Survey on FCA-Based Information Retrieval Research - Springer
