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A Study on Legal Ontology Construction

법령 온톨로지 구축에 관한 연구

  • Jo, Dae Woong (Department of Computer Science and Engineering, Soongsil University) ;
  • Kim, Myung Ho (School of Computer Science and Engineering, Soongsil University)
  • 조대웅 (숭실대학교 대학원 컴퓨터학과) ;
  • 김명호 (숭실대학교 컴퓨터학부)
  • Received : 2014.08.19
  • Accepted : 2014.09.15
  • Published : 2014.11.29

Abstract

In this paper, we propose an OWL DL mapping rules for construction legal ontology based on the analyzed relationship between the structural features and elements of the statute. The mapping rule to be proposed is the method building the structure of the domestic statute, unique attribute of the statute, and reference relation between laws with TBox, and the legal sentence is analyzed, and the pattern type of the sentence is selected. It expresses with ABox. The proposed mapping rule is transformed to the information in which the computer can process the domestic legal document. It is usable for the legal knowledge base.

본 논문에서는 법령 온톨로지 구축을 위해 법령의 구조적 특징과 요소 간의 관계에 대해 분석 정리하고 정리된 내용을 바탕으로 OWL DL 수준의 매핑 규칙을 제안한다. 제안하는 매핑 규칙은 국내 법령 관계의 상-하위 구조, 법령의 고유 속성, 법률 간의 참조 관계와 같은 구조적인 관계의 TBox를 구축하는 방법과 법률 문장을 분석하여 조문 규정 별로 나타나는 문장의 패턴 유형을 선별, ABox로 구축될 수 있는 요소를 표현한다. 제안된 매핑 규칙은 일반 텍스트로 설명되고 있는 국내의 법령을 컴퓨터가 이해 가능한 수준의 정보로 변환 되어 법령 지식 베이스로 활용 가능하다.

Keywords

References

  1. J. H. Kim et al., "Term Mapping Methodology between Everyday Words and Legal Terms for Law Information Search System," Journal of Intelligence and Information Systems, Vol. 18, No. 3, pp. 137-152, Sep. 2012.
  2. In. Ho. Chang, "Developing and Evaluating an Ontology-based Legal Retrieval System," Journal of the Korean Society for Library and Information Science, Vol. 45, No. 2, pp. 345-355, May 2011. https://doi.org/10.4275/KSLIS.2011.45.2.345
  3. Simple Knowledge Organization System Reference, http://www.w3.org/TR/skos-reference
  4. R. Hoekstra et at., "LKIF Core: Principled Ontology Development for the Legal Domain," Proceedings of the 2009 conference on Law, Ontologies and the Semantic web: Channelling the Legal Information Flood, pp. 21-52, Jul. 2009.
  5. A. Gangemi, "Design patterns for legal ontology construction," The Eleventh International Conference on Artificial Intelligence and Law, Proceedings of the Conference, pp. 65-85, Jul. 2007.
  6. Naver encyclopedia of knowledge, http://terms.naver.com/entry.nhn?docId=1100907&cid=40942&categoryId=31721
  7. S. S. Cho, D. W. Jo and M. H. Kim, "The Design and Implementation of The Amendment Statement Automatic Generated System for Attached Tables in Legislation," Journal of the computational structural engineering institute of Korea, Vol. 19, No. 4, pp. 111-122, Apr. 2014. https://doi.org/10.9708/jksci.2014.19.4.111
  8. ACT ON PROMOTION OF THE PROVISION AND USE OF PUBLIC DATA, http://elaw.klri.re.kr/kor_service/lawView.do?hseq=30365&lang=ENG
  9. OWL Web Ontology Language Semantics and Abstract Syntax, http://www.w3.org/TR/owl-semantics/syntax.html
  10. Protege, http://protege.stanford.edu/
  11. STATUTES OF THE REPUBLIC OF KOREA, http://elaw.klri.re.kr/kor_service/main.do
  12. D. W. Jo, J. W. Choi and M. H. Kim, "SPARQL Query Tool for Using OWL Ontology," Journal of The Korea Society of Computer and Information, Vol. 14, No. 11, pp. 21-30, Nov. 2009.

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  3. SPARQL Query Automatic Transformation Method based on Keyword History Ontology for Semantic Information Retrieval vol.22, pp.2, 2014, https://doi.org/10.9708/jksci.2017.22.02.97
  4. Semi-automatic Legal Ontology Construction based on Korean Language Sentence Patterns vol.22, pp.6, 2014, https://doi.org/10.9708/jksci.2017.22.06.069
  5. 연관법령 검색을 위한 워드 임베딩 기반 Law2Vec 모형 연구 vol.18, pp.7, 2014, https://doi.org/10.9728/dcs.2017.18.7.1419
  6. A Study of the Legislation Prediction Model using Artificial Intelligence vol.21, pp.8, 2014, https://doi.org/10.9728/dcs.2020.21.8.1443