Extracting knowledge from fuzzy relational databases with description logic

  • Authors:

  • Z. M. Ma;Fu Zhang;Li Yan;Jingwei Cheng

  • Affiliations:

  • (Correspd. Tel./Fax: +86 24 83681582/ E-mail: mazongmin@ise.neu.edu.cn) College of Information Science and Engineering, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China;School of Software, Northeastern University, Shenyang, China;College of Information Science and Engineering, Northeastern University, Shenyang, China

  • Venue:
  • Integrated Computer-Aided Engineering
  • Year:
  • 2011

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Abstract

In recent years, how to extract useful information and knowledge from fuzzy relational databases has received much attention. Based on the high expressive power and effective reasoning service of Description Logics (DLs), this paper proposes a DL approach for automatically extracting knowledge from fuzzy relational databases (FRDB). To represent the extracted knowledge, a fuzzy DL called f-ALCNI is introduced after considering the characteristics of FRDB. On this basis, we propose an approach which can extract the f-ALCNI knowledge base from the FRDB, i.e., which can transform the FRDB (including schema and data information) into the f-ALCNI knowledge base (i.e., TBox and ABox). Furthermore, we design and implement a prototype extraction tool called FRDB2DL. In addition, to further demonstrate how the DLs are useful for improving some database applications, based on the extracted knowledge, we investigate the reasoning problems of FRDB (e.g., consistency, satisfiability, subsumption, equivalence, and redundancy) by means of the reasoning mechanism of f-ALCNI. Case studies show that the proposed approach is feasible and the tool is efficient.