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

Silvia Calegari and Elie Sanchez

Journal of the American Society for Information Science and Technology, 2008, vol. 59, issue 13, 2171-2185

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.

Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:59:y:2008:i:13:p:2171-2185

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