Object-fuzzy concept network: An enrichment of ontologies in semantic information retrieval

  • Authors:

  • Silvia Calegari;Elie Sanchez

  • Affiliations:

  • Dipartimento Di Informatica, Sistemistica e Comunicazione, Università di Milano – Bicocca, Viale Sarca 336-14, 20126 Milano Italia;LIF, Biomathematiques et Informatique Medicale, Faculte de Medecine (Universite Aix-Marseille II), 27 Bd Jean Moulin, 13385 Marseille Cedex5 France

  • Venue:
  • Journal of the American Society for Information Science and Technology
  • Year:
  • 2008

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Abstract

This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases. © 2008 Wiley Periodicals, Inc.