Journal of KIISE:Databases (한국정보과학회논문지:데이타베이스)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지

Ontology Mapping using Semantic Relationship Set of the WordNet

워드넷의 의미 관계 집합을 이용한 온톨로지 매핑

  • 곽정애 (이화여자대학교 컴퓨터공학과) ;
  • 용환승 (이화여자대학교 컴퓨터공학과)
  • Published : 2009.12.15
Download PDF
⟨ Previous Next ⟩

Abstract

Considerable research in the field of ontology mapping has been done when information sharing and reuse becomes necessary by a variety of ontology development. Ontology mapping method consists of the lexical, structural, instance, and logical inference similarity computing. Lexical similarity computing used in most ontology mapping methods performs an ontology mapping by using the synonym set defined in the WordNet. In this paper, we define the Super Word Set including the hypenym, hyponym, holonym, and meronym set and propose an ontology mapping method using the Super Word Set. The results of experiments show that our method improves the performance by up to 12%, compared with previous ontology mapping method.

다양한 온톨로지 개발로 온톨로지간에 정보공유와 재사용이 필요하게 되면서 온톨로지 매핑에 관련된 연구가 활발이 이루어지고 있다. 온톨로지 매핑 기법으로는 어휘 유사성, 구조 유사성, 인스턴스 유사성, 추론 유사성 검사 기법으로 나누어진다. 이 중 어휘 유사성 검사 기법은 대부분의 온톨로지 매핑 연구에서 사용하는 기법으로써 주로 워드넷에 정의되어 있는 동의어 집합만을 사용한다. 이에 본 연구에서는 워드넷에 정의되어 있는 동의어 집합 외에 상위어, 하위어, 전체어, 부분어 집합의 모든 단어들을 포함한 수퍼워드셋을 정의하고, 이것을 이용한 온톨로지 매핑 기법을 제안한다. 실험 결과에 의하면, 제안된 기법은 기존 온톨로지 매핑 기법보다 평균 12%까지 온톨로지 매칭율을 높인 것을 보여준다.

Keywords

References

  1. N. Choi, I. Song, and H. Han, 'A Survey on Ontology Mapping,' ACM SIGMOD Record, vol.35, no.3, pp.34-41, Sep. 2006 https://doi.org/10.1145/1168092.1168097
  2. A. Doan, P. Domingos, and A. Y. Halevy, 'Leaming to Match the Schemas of Data Sources: A Multistrategy Approach,' Machine Learning, vol.50, no.3, pp.279-301, March 2003 https://doi.org/10.1023/A:1021765902788
  3. D. Beneventano, S. Bergarnaschi, F. Guerra, and M. Vincini, 'Synthesizing an Integrated Ontology,' IEEE Internet Computing, vol.7, no.5, pp.42-51, Sep, October 2003 https://doi.org/10.1109/MIC.2003.1232517
  4. A. Doan, j. Madhavan, R. Dhamankar, P. Domingos, and A. Y. Halevy, 'Learning to match ontologies on the Semantic Web,' The VLDB Journal, vol.12, no.4, pp.303-319, 2003 https://doi.org/10.1007/s00778-003-0104-2
  5. A. Maedche, B. Motik, N. Silva, and R. Volz, 'MAFRA - A MApping FRAmework for Distributed Ontologies in the semantic Web,' Proc. of the Workshop on Knowledge Transformation for the Semantic Web (KTSW 2002), pp.60-68, Lyon, France, 2002 https://doi.org/10.1007/3-540-45810-7_23
  6. J. Li, 'LOM: A Lexicon-based Ontology Mapping Tool,' Proc. of the Performance Metrics for Intelligent Systems Workshop (PerMIS. '04), 2004
  7. D. Aumueller, H. - H. Do, S. Massmann, and E. Rahm, 'Schema and ontology matching with COMA++,' Proc. of the ACM SIGMOD International Conference on Management of Data, pp.906-908, June 2005 https://doi.org/10.1145/1066157.1066283
  8. J. Tang, J.-Z. Li, B. Liang, X. Huang, Y. Li and K. Wang, 'Using Bayesian decision for ontology mapping,' Journal of Web Semantics, vol.4, no.4, pp.243-262, 2006 https://doi.org/10.1016/j.websem.2006.06.001
  9. O. Udrea, L. Getoor, and R. J. Miller, 'Leveraging data and structure in ontology integration,' Proc. of the ACM SIGMOD International Conference on Management of Data, pp.449-460, 2007 https://doi.org/10.1145/1247480.1247531
  10. WordNet, http://wordnet.princeton.edu/
  11. P. Pantel and D. Lin, 'Discovering Word Senses from Text,' Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.613-6I9, July 2002 https://doi.org/10.1145/775047.775138
  12. H. Komilakis, M. Grigoriadou, K. A. Papanikolaou, and E. Gouli, 'Using WordNet to Support Interactive Concept Map Construction,' Proc. of the IEEE International Conference on Advanced Learning Technologies (ICALT'04), pp.600-604, 2004 https://doi.org/10.1109/ICALT.2004.1357485
  13. G. Antoniou and F. V. Harrnelen, A Semantic Web Primer, p.118, The MIT Press, Massachusetts, 2004