Many-Valued Concept Lattices for Conceptual Clustering and Information Retrieval

  • Authors:
  • Nizar Messai;Marie-Dominique Devignes;Amedeo Napoli;Malika Smail-Tabbone

  • Affiliations:
  • LORIA-INRIA Nancy Grand Est, 615, rue du Jardin Botanique 54600 Villers-lès-Nancy, FRANCE, email: messai@loria.fr;LORIA-INRIA Nancy Grand Est, 615, rue du Jardin Botanique 54600 Villers-lès-Nancy, FRANCE, email: devignes@loria.fr;LORIA-INRIA Nancy Grand Est, 615, rue du Jardin Botanique 54600 Villers-lès-Nancy, FRANCE, email: napoli@loria.fr;LORIA-INRIA Nancy Grand Est, 615, rue du Jardin Botanique 54600 Villers-lès-Nancy, FRANCE, email: malika@loria.fr

  • Venue:
  • Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
  • Year:
  • 2008

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Abstract

In this paper we present an extension of the Galois connection to deal with many-valued formal contexts. We define a many-valued Galois connection with respect to similarity between attribute values in a many-valued context. Then, we define many-valued formal concepts and many-valued concept lattices. Depending on a similarity threshold, many-valued concept lattices may have different levels of precision. This feature makes them very useful for multilevel conceptual clustering. Many-valued concept lattices are also used in a new lattice-based information retrieval approach for efficiently answering complex queries.