Ontology-Based Approach for Legal Provision Retrieval
In this paper, we present an ontology-based approach for legal provision retrieval. The approach aims at assisting the man who knows little about legal knowledge to inquire appropriate provisions. Legal ontology and legal concept probability model are main functional components in our approach. Lega...
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Published in | Shanghai jiao tong da xue xue bao Vol. 17; no. 2; pp. 135 - 140 |
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Main Author | |
Format | Journal Article |
Language | English |
Published |
Heidelberg
Shanghai Jiaotong University Press
01.04.2012
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Subjects | |
Online Access | Get full text |
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Summary: | In this paper, we present an ontology-based approach for legal provision retrieval. The approach aims at assisting the man who knows little about legal knowledge to inquire appropriate provisions. Legal ontology and legal concept probability model are main functional components in our approach. Legal ontology is extracted from Chinese laws by the natural language processing (NLP) techniques. Legal concept probability model is built from corpus, and the model is used to bridge the gap between legal ontology and natural language inquiries. |
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Bibliography: | ontology, law, retrieval, knowledge representation 31-1943/U TANG Qi , WANG Ying-lin, ZHANG Ming-lu (Department of Computer Science and Engineering, Shanghai Jiaotong University, Shanghai 200240, China) In this paper, we present an ontology-based approach for legal provision retrieval. The approach aims at assisting the man who knows little about legal knowledge to inquire appropriate provisions. Legal ontology and legal concept probability model are main functional components in our approach. Legal ontology is extracted from Chinese laws by the natural language processing (NLP) techniques. Legal concept probability model is built from corpus, and the model is used to bridge the gap between legal ontology and natural language inquiries. ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 1007-1172 1995-8188 |
DOI: | 10.1007/s12204-012-1242-8 |