Fuzzy Formal Concept Analysis Approach for Information Retrieval Cherukuri Aswani Kumar, Subramanian Chandra Mouliswaran, Pandey Amriteya and S.R. Arun Abstract Recently Formal Concept Analysis (FCA), a mathematical framework based on partial ordering relations has become popular for knowledge representa- tion and reasoning. Further this framework is extended as Fuzzy FCA, Rough FCA, etc. to deal with practical applications. There are investigations in the literature applying FCA for Information Retrieval (IR) applications. The objective of this paper is to apply Fuzzy FCA approach for IR. While adopting Fuzzy FCA, we follow a fast algorithm to generate the fuzzy concepts rather than classical algo- rithms that are based on residuated methods. Further we follow an approach that retrieves the relevant documents even during absence of exact match of the keywords. Keywords FCA ⋅ Fuzzy FCA ⋅ Fuzzy context ⋅ Fuzzy concept ⋅ Fuzzy concept lattice ⋅ Information retrieval 1 Introduction At present, information retrieval (IR) systems play an important role in various online search applications. Starting from traditional application such as digital library search and web search to recent popular applications such as social search and recommender system, IR systems have a great significance. The interest on IR systems has evolved numerous IR models since 1960 such as Boolean models [1, 2], vector space models [3–6], probabilistic model [7], graph theory model [8] etc. The major challenge of these IR systems is to retrieve the relevant and precise information or documents as per the user needs modeled through keyword search or C. Aswani Kumar ( ✉ ) ⋅ S. Chandra Mouliswaran ⋅ P. Amriteya ⋅ S.R. Arun School of Information Technology and Engineering, VIT University, Vellore, India e-mail: cherukuri@acm.org S. Chandra Mouliswaran e-mail: scmwaran@gmail.com © Springer International Publishing Switzerland 2015 V. Ravi et al. (eds.), Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015), Advances in Intelligent Systems and Computing 415, DOI 10.1007/978-3-319-27212-2_20 255