Automatic fuzzy semantic web ontology learning from fuzzy object-oriented database model

  • Authors:

  • Fu Zhang;Z. M. Ma;Gaofeng Fan;Xing Wang

  • Affiliations:

  • College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China;College of Information Science & Engineering, Northeastern University, Shenyang, China

  • Venue:
  • DEXA'10 Proceedings of the 21st international conference on Database and expert systems applications: Part I
  • Year:
  • 2010

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Abstract

How to construct Web ontologies that meet applications' needs has become a key technology to enable the Semantic Web. Manual development of ontologies remains a cumbersome and time-consuming task. In real-world applications, however, information is often vague or ambiguous. Thus, developing approaches and tools for constructing fuzzy ontologies by extracting domain knowledge from huge amounts of existing fuzzy databases can facilitate fuzzy ontology development. In this paper, we propose a formal approach and an automated tool for constructing fuzzy ontologies from fuzzy Object-Oriented database (FOOD) models. Firstly, we introduce the fuzzy ontology, which consists of the fuzzy ontology structure and instances. Then, the FOOD models are investigated, and we propose a kind of formal definition of FOOD models. On this basis, we develop a formal approach that can translate the FOOD model and its corresponding database instances into the fuzzy ontology structure and the fuzzy ontology instances, respectively. Furthermore, following the proposed approach, we implement an automated learning tool, which can automatically construct fuzzy ontologies from FOOD models. Case studies show that the approach is feasible and the automated learning tool is efficient.