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  • P-ISSN1013-0799
  • E-ISSN2586-2073
  • KCI

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Vol.22 No.2

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Relevance Feedback based on Medicine Ontology for Retrieval Performance Improvement

Journal of the Korean Society for Information Management / Journal of the Korean Society for Information Management, (P)1013-0799; (E)2586-2073
2005, v.22 no.2, pp.41-56
https://doi.org/10.3743/KOSIM.2005.22.2.041

(2005). Relevance Feedback based on Medicine Ontology for Retrieval Performance Improvement. Journal of the Korean Society for Information Management, 22(2), 41-56, https://doi.org/10.3743/KOSIM.2005.22.2.041

Abstract

For the purpose of extending the Web that is able to understand and process information by machine, Semantic Web shared knowledge in the ontology form. For exquisite query processing, this paper proposes a method to use semantic relations in the ontology as relevance feedback information to query expansion. We made experiment on pharmacy domain. And in order to verify the effectiveness of the semantic relation in the ontology, we compared a keyword based document retrieval system that gives weights by using the frequency information compared with an ontology based document retrieval system that uses relevant information existed in the ontology to a relevant feedback. From the evaluation of the retrieval performance, we knew that search engine used the concepts and relations in ontology for improving precision effectively. Also it used them for the basis of the inference for improvement the retrieval performance.

keywords
ontology, semantic relation, document retrieval, query expansion, 온톨로지, 의미관계, 문서검색, 질의 확장

Reference

1.

강승식.. (1998). 형태소 해석기 HAM. , -.

2.

강신재. (2002). 실용적인 온톨로지의 반자동 구축 및 어휘 의미 중의성 해소를 위한 응용. , -.

3.

김홍기. (2002). 월드와이드 웹에서 시멘틱 웹으로. (4), 242-301.

4.

문유진.. (1996). 한국어 명사를 위한 WordNet의 설계와 구현. , 437-445.

5.

박정오. (2000). 전문용어 추출시스템. , 381-383.

6.

신효식. (2002). 텍스트로부터 용어 정의문 자동 추출방법. , 292-299.

7.

오종훈. (2002). 분야간 유사도와 통계기법을 이용한 전문용어의 자동 추출. 정보과학회논문지 : 소프트웨어 및 응용, 29(4), 258-269.

8.

옥철영.. (2004). 한국어 정보처리와 온톨로지 2004 한국어 정보처리 연구회 동계 튜토리얼 자료집. , 81-123.

9.

이재호.. (2003). 시멘틱 웹의 온톨로지 언어. , -.

10.

정도헌.. (2003). 시멘틱 웹을 위한 온톨로지 언어와 구현사례 연구. , 87-109.

11.

최기선.. (2001). KAIST 대용량 코퍼스. [. , -.

12.

Guarino,N.. (1998). Proceedings of FOIS'98. , 3-15.

13.

Kang. (2001). “Semi- Automatic Practical Ontology Construction by Using a Thesaurus ACL 2001 Workshop on Human Language Technology and Knowledge Manage- ment. , 45-52.

14.

and Muresan. (2000). Rule-based Methods for the Extraction of Medical Terminology and their Associated Definitions from On-line Text Proceedings of AMIA Symposium. , 201-202.

15.

Lee. (1995180-188). “Combining Multiple Evidence from Different Properties of Weighting Schemes ACM SIGIR Conference on Research and Development in Infor- mation Retrieval. , -.

16.

(1995). A Large-Scale Inve- stment in Knowledge Infrastructure” Communications of the ACM. , 33-38.

17.

Lim. (2003). “Hub-word based on Ontology Construction for Document Retrieval” Proceedings of IC-AI’03. , 549-552.

18.

Maedche. (2002). Ontology Learning for the Semantic Web. , -.

19.

Velardi. (2003). “Text Mining Techniques to Automa- tically Enrich a Domain Ontology”. , 322-340.

20.

Salton. (1983). Introduction to Modern Information Retrieval. , -.

21.

Volz. (2003). “Pruning-based Identification of Domain Ontologies” Journal of Uni- versal Computer Science. , 520-529.

22.

Vossen. ([cited2004). trimming and fusing WordNet for technical documents” Proceedings of NAACL-2001 Workshop on WordNet and Other Lexical Re- sources. , 208-215.

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