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Fuzzy Ontology Models Based on Fuzzy Linguistic Variable for Knowledge Management and Information Retrieval

  • Jun Zhai
  • Yiduo Liang
  • Jiatao Jiang
  • Yi Yu
Part of the IFIP – The International Federation for Information Processing book series (IFIPAICT, volume 288)

Abstract

Ontology is the basis of sharing and reusing knowledge on the Semantic Web, and ontology-based semantic retrieval is a hotspot of current research. Fuzzy ontology is an extension of domain ontology for solving the uncertainty problems. To represent fuzzy knowledge more effectively, this paper presents a new series of fuzzy ontology models that consists of fuzzy domain ontology and fuzzy linguistic variable ontologies, considering semantic relationships of concepts, including set relation, order relation, equivalence relation and semantic association relation etc. The process to construct linguistic variables ontology is discussed. Using ontology and RDFS, the knowledge model for product information is created. To achieve semantic retrieval, the semantic query expansion in SeRQL is constructed by semantic relations between fuzzy concepts. The application shows that these models can overcome the localization of other fuzzy ontology models, and this research facilitates the fuzzy knowledge sharing and semantic retrieval on the Semantic Web.

Keywords

Membership Function Information Retrieval Resource Description Framework Semantic Relation Linguistic Variable 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© IFIP International Federation for Information Processing 2008

Authors and Affiliations

  • Jun Zhai
    • 1
  • Yiduo Liang
    • 1
  • Jiatao Jiang
    • 1
  • Yi Yu
    • 1
  1. 1.School of Economics and ManagementDalian Maritime UniversityDalianChina

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