An ontology-based similarity measurement for problem-based case reasoning

Adela Lau, Yue Hong Eric Tsui, Wing Bun Lee

Research output: Journal article publicationJournal articleAcademic researchpeer-review

28 Citations (Scopus)

Abstract

Traditional case-based reasoning uses a table/frame or scenario to represent a case. It assumed that similar input/event results in similar output/event state. However, similar cases may not have similar output/event states since problem solver may have different way to break down the problem. Thus, authors previously proposed problem-based case reasoning to overcome the limitation of the traditional approaches and used clustered ontology to represent the problem spaces of a case. However, synonym problem causes the mismatch of similar sub-problems of historical cases for new case. Thus, this paper proposed ontology-based similarity measurement to retrieve the similar sub-problems that overcomes the synonym problems on case retrieval. The recall and precise of ontology-based similarity measurement were higher than that of the traditional similarity measurement.
Original languageEnglish
Pages (from-to)6574-6579
Number of pages6
JournalExpert Systems with Applications
Volume36
Issue number3 PART 2
DOIs
Publication statusPublished - 1 Apr 2009

Keywords

  • Knowledge retrieval
  • Ontology-based similarity measurement
  • Problem-driven case

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Engineering(all)

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