Personalizing a Concept Similarity Measure in the Description Logic ELH with Preference Profile

Authors

  • Teeradaj Racharak School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University
  • Boontawee Suntisrivaraporn School of Information, Computer, and Communication Technology, Sirindhorn International Institute of Technology, Thammasat University
  • Satoshi Tojo School of Information Science, Japan Advanced Institute of Science and Technology

Keywords:

Concept similarity measure, semantic web ontology, preference profile, description logics

Abstract

Concept similarity measure aims at identifying a degree of commonality of two given concepts and is often regarded as a generalization of the classical reasoning problem of equivalence. That is, any two concepts are equivalent if and only if their similarity degree is one. However, existing measures are often devised based on objective factors, e.g. structural-based measures and interpretation-based measures. When these measures are employed to characterize similar concepts in an ontology, they may lead to unintuitive results. In this work, we introduce a new notion called concept similarity measure under preference profile with a set of formally defined properties in Description Logics. This new notion may be interpreted as measuring the similarity of two concepts under subjective factors (e.g. the agent's preferences and domain-dependent knowledge). We also develop a measure of the proposed notion and show that our measure satisfies all desirable properties. Two algorithmic procedures are introduced for top-down and bottom-up implementation, respectively, and their computational complexities are intensively studied. Finally, the paper discusses the usefulness of the approach to potential use cases.

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Published

2018-07-26

How to Cite

Racharak, T., Suntisrivaraporn, B., & Tojo, S. (2018). Personalizing a Concept Similarity Measure in the Description Logic ELH with Preference Profile. COMPUTING AND INFORMATICS, 37(3), 581–613. Retrieved from http://www.cai.sk/ojs/index.php/cai/article/view/2018_3_581