Abstract
Recently Formal Concept Analysis (FCA), a mathematical framework based on partial ordering relations has become popular for knowledge representation 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 algorithms 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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Waller, W.G., Kraft, D.H.: A mathematical model of a weighted Boolean retrieval system. Inf. Process. Manage. 15(5), 235–245 (1979)
Cerulo, L., Canfora, G.: A taxonomy of information retrieval models and tools. CIT. J. Comput. Inf. Technol. 12(3), 175–194 (2004)
Codocedo, V., Napoli, A.: Formal concept analysis and information retrieval–a survey. In: Formal Concept Analysis, pp. 61–77. Springer International Publishing (2015)
Manning, C.D., Raghavan, P., Schütze, H.: Introduction to Information Retrieval, vol. 1, p. 496. Cambridge University Press, Cambridge (2008)
Dominich, S.: The Modern Algebra of Information Retrieval, pp. 74–93. Springer, Heidelberg (2008)
Baeza-Yates, R., &Ribeiro-Neto, B.: Modern Information Retrieval (vol. 463). ACM press, New York (1999)
Wong, S.K.M., Yao, Y.Y.: On modeling information retrieval with probabilistic inference. ACM Trans. Inf. Syst. (TOIS) 13(1), 38–68 (1995)
Flake, G.W., Lawrence, S., Giles, C.L., Coetzee, F.M.: Self-organization and identification of web communities. Computer 35(3), 66–70 (2002)
Subtil, P., Mouaddib, N., Foucaut, O.: A fuzzy information retrieval and management system and its applications. In: Proceedings of the 1996 ACM Symposium on Applied Computing, pp.537–541. ACM (1996)
Oussalah, M., Khan, S., Nefti, S.: Personalized information retrieval system in the framework of fuzzy logic. Expert Syst. Appl. 35(1), 423–433 (2008)
Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer Science & Business Media (2012)
Mooers, C.N.: A Mathematical Theory of Language Symbols in Retrieval. Zator (1958)
Kumar, C.A., Srinivas, S.: A note on the effect of term weighting on selecting intrinsic dimensionality of data. Cybern. Inf. Technol. 9(1), 5–12 (2009)
Kumar, C.A.: Fuzzy clustering-based formal concept analysis for association rules mining. Appl. Artif. Intell. 26(3), 274–301 (2012)
Priss, U.: Lattice-based information retrieval. Knowl. Organ. 27(3), 132–142 (2000)
Kollewe, W., Sander, C., Schmiede, R., Wille, R.: TOSCANA als Instrument der bibliothekarischenSacherschließung. Aufbau und ErschließungbegrifflicherDatenbanken, pp. 95–114. BIS-Verlag, Oldenburg (2000)
Carpineto, C., Romano, G.: Using concept lattices for text retrieval and mining. In: Formal Concept Analysis, pp. 161–179. Springer, Berlin (2005)
Aswani Kumar, C., Radvansky, M., Annapurna, J.: Analysis of a vector space model, latent semantic indexing and formal concept analysis for information retrieval. Cybern. Inf. Technol. 12(1), 34–48 (2012)
Muthukrishnan, A.K.: Information Retrieval Using Concept Lattices (M.S. dissertation, University of Cincinnati) (2006)
Kumar, C.A., Ishwarya, M. S., Loo, C.K.: Formal concept analysis approach to cognitive functionalities of bidirectional associative memory. Biol. Inspir. Cogn. Archit. 12, 20–33 (2015)
Krajci, S.: Cluster based efficient generation of fuzzy concepts. Neural Netw. World 13(5), 521–530 (2003)
Butka, P., Pócsová, J., Pócs, J.: A proposal of the information retrieval system based on the generalized one-sided concept lattices. In: Applied Computational Intelligence in Engineering and Information Technology, pp. 59–70. Springer, Berlin, Heidelberg (2012)
Martin, T., Majidian, A.: Finding fuzzy concepts for creative knowledge discovery. Int. J. Intell. Syst. 28(1), 93–114 (2013)
Kumar, C.A., Srinivas, S.: Concept lattice reduction using fuzzy K-Means clustering. Expert Syst. Appl. 37(3), 2696–2704 (2010)
Aswani Kumar, C., Srinivas, S.: Mining associations in health care data using formal concept analysis and singular value decomposition. J. Biol. Syst. 18(04), 787–807 (2010)
Formica, A.: Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis. Knowl. Based Syst. 26, 40–47 (2012)
Quan, T.T., Hui, S.C., Cao, T.H.: A Fuzzy FCA-based approach to conceptual clustering for automatic generation of concept hierarchy on uncertainty data. In: CLA, pp. 1–12 (2004)
Letsche, T.A., Berry, M.W.: Large-scale information retrieval with latent semantic indexing. Inf. Sci. 100(1), 105–137 (1997)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval (vol. 463). ACM press, New York (1999)
Barbut, M., Monjardet, B.: Ordreet classification algèbre et combinatoirs. Hachette (1970)
Carpineto, C., Romano, G.: A lattice conceptual clustering system and its application to browsing retrieval. Mach. Learn. 24(2), 95–122 (1996)
Carpineto, C., Mizzaro, S., Romano, G., Snidero, M.: Mobile information retrieval with search results clustering: prototypes and evaluations. J. Am. Soc. Inform. Sci. Technol. 60(5), 877–895 (2009)
Carpineto, C., Romano, G.: Galois: an order-theoretic approach to conceptual clustering. In: Proceedings of ICML, vol. 293, pp. 33–40 (1993)
Spoerri, A.: InfoCrystal: integrating exact and partial matching approaches through visualization. In: RIAO 94: recherched’informationassistée par ordinateur. Conférence, pp. 687–696 (1994)
Valverde-Albacete, F.J., Peláez-Moreno, C.: Systems versus methods: an analysis of the affordances of FOrmal concept analysis for information retrieval⋆. In: FCAIR 2012 Formal Concept Analysis Meets Information Retrieval Workshop co-located with the 35th European Conference on Information Retrieval (ECIR 2013), (p. 113). Moscow, Russia (2013)
Godin, R., Missaoui, R., April, A.: Experimental comparison of navigation in a Galois lattice with conventional information retrieval methods. Int. J. Man Mach. Stud. 38(5), 747–767 (1993)
Alam, M., & Napoli, A.: Defining views with formal concept analysis for understanding SPARQL query results. In: Proceedings of the Eleventh International Conference on Concept Lattices and Their Applications (2014)
Singh, P.K., &Aswani Kumar, Ch.: Bipolar fuzzy graph representation of concept lattice. Inf. Sci. 288, 437–448 (2014)
Singh, P.K., Aswani Kumar, C., Li, J.: Knowledge representation using interval valued fuzzy formal concept lattice. Soft Comput. (2015)
Acknowledgments
One of the Authors, Ch. A.K sincerely acknowledges the research grant under cognitive science research initiative schemeSR/CSRI/118/2014 of DST, Govt. of India.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Aswani Kumar, C., Chandra Mouliswaran, S., Amriteya, P., Arun, S.R. (2015). Fuzzy Formal Concept Analysis Approach for Information Retrieval. In: Ravi, V., Panigrahi, B., Das, S., Suganthan, P. (eds) Proceedings of the Fifth International Conference on Fuzzy and Neuro Computing (FANCCO - 2015). Advances in Intelligent Systems and Computing, vol 415. Springer, Cham. https://doi.org/10.1007/978-3-319-27212-2_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-27212-2_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-27211-5
Online ISBN: 978-3-319-27212-2
eBook Packages: Computer ScienceComputer Science (R0)