Journal of the Korean Institute of Intelligent Systems (한국지능시스템학회논문지)

Korean Institute of Intelligent Systems (한국지능시스템학회)
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Building Domain Ontology Based on Linguistic Patterns

  • Kim, Kweon-Yang (School of Computer Engineering, Kyungil University) ;
  • Lim, Soo-Yeon (Department of Computer Engineering, Kyungpook National University)
  • Published : 2006.12.25
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Abstract

In this paper, we focus on the building domain ontology from corpus by extracting concepts and properties relationships based on linguistic patterns. The pharmacy field is selected as an experiment domain and we present an algorithm to extract hierarchical structure for terminology based on the noun/suffix patterns of terminology in domain texts. In order to show usefulness of our domain ontology, we compare a typical keyword based retrieval method with an ontology based retrieval mettled which uses related information in an ontology for a related feedback. As a result, our method shows the improvement of precision by 4.97% without losing recall.

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References

  1. Baeza-Yates, R. and Robeiro-Neto, B.: Modern Information Retrieval. ACM Press, New York, NY, USA, 1999
  2. Bettina, B., Andreas, H., Gerd, S.: Towards Semantic Web Mining. International Semantic Web Conference, 2002
  3. Gyeong-Hee Lee, Ju-HO Lee, Myeong- Choi, Gil-Chang Lim, 'Study on Named Entity Recognition in Korean Text,' Proceedings of the 12th Conference on Hangul and Korean Information Processing, pp. 292-299, 2000
  4. Hyo-Shik Shin, Young-Soo Kang, Key-Sun Choi, Man-Suk Song, Computational Approach to Zero Pronoun Resolution in Korean Encyclopedia, Proceedings of the 13th Conference on Hangul and Korean Information Processing, pp. 239-243, 2001
  5. JongHoon Oh, KyungSoon Lee, KeySun Choi, 'Automatic Term Recognition using Domain Similarity and Statistical Methods,' Journal of the Korea Information Science Society, Vol. 29, No. 4, pp. 258-269, 2002
  6. Jung-Oh Park, Do-Sam Hwang, 'A Terminology extraction system,' Proceedings of Korea Information Science Society Spring Conference(2001), Vol 27, No 1, pp. 381-383, 2000
  7. Kang, S. J. and Lee, J.H.: Semi-Automatic Practical Ontology Construction by Using a Thesaurus, Computational Dictionaries, and Large Corpora. ACL 2001 Workshop on Human Language Technology and Knowledge Management, Toulouse, France, 2001
  8. Klavans, J. and Muresan, S., 'DEFINDER: Rule-Based Methods for the Extraction of Medical Terminology and their Associated Definitions from On-line Text,' Proceedings of AMIA Symposium, pp. 201-202, 2000
  9. Michele M., Paola V. and Paolo F., 'Text Mining Techniques to Automatically Enrich a Domain Ontology', Applied Intelligence 18, 322-340, 2003
  10. Missikoff, M., Velardi, P. and Fabriani, P., 'Text Mining Techniques to Automatically Enrich a Domain Ontology,' Applied Intelligence, Vol. 18, pp. 322-340, 2003
  11. S. Y. Lim, Koo, S. O., Song, M. H., Lee, S, J., 'Hub_word based on Ontology Construction for Document Retrieval', IC-AI'03, Las Vegas, USA, 2003
  12. Soo-Yeon Lim, Mu-Hee Song, Sang-Jo Lee, 'Domain-specific Ontology Construction by Terminology Processing,' Journal of the Korea Information Science Society(B), Journal of the Korea Information Science Society Vol. 31, No. 3, pp. 353-360, 2004
  13. Yi-Gyu Hwang, Bo-Hyun Yun, 'HMM-based Korean Named Entity Recognition,' Journal of the Korea Information Procissing Society(B), vol.10, No. 2, pp. 229-236, 2003 https://doi.org/10.3745/KIPSTB.2003.10B.2.229

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