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Ontology-based model of law retrieval system for R&D projects

Authors:

HaeMin JungAuthors Info & Claims

ICEC '16: Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World

Article No.: 26, Pages 1 - 6

https://doi.org/10.1145/2971603.2971629

Published: 17 August 2016 Publication History

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Abstract

Research and development projects have close relationship with laws. In some cases, new technologies resulted from R&D projects can't be used because some statutes restrict them. The reason of this problem is that researchers don't know exactly which laws can affect their R&D projects. To solve the issue, we suggest a model for law retrieval system that can be used by researchers of R&D projects to find related statutes. Input of this model is a query document that describes the main contents of a project. By using ontology, legal terms are extracted from the document and statutes defining them are retrieved as a set of related laws. After this searching process, statutes are provided to researchers with their ranks, which are assigned using relevance scores we developed. By using this model, we can make a system for researchers to search a list of statutes that may affect R&D projects, and finally, they can adjust their project's direction by checking the list, preventing their works from being useless.

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Cited By

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Parashar SMittal NMehta P(2023)CASRank: A ranking algorithm for legal statute retrievalMultimedia Tools and Applications10.1007/s11042-023-15464-083:2(5369-5386)Online publication date: 2-Jun-2023
https://doi.org/10.1007/s11042-023-15464-0
Stoianova OMoskaleva V(2021)Ontologies to Reduce Uncertainty in R&D Project PlanningProceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21)10.1007/978-3-030-87178-9_37(370-379)Online publication date: 16-Sep-2021
https://doi.org/10.1007/978-3-030-87178-9_37
Verma AMorato JJain AArora A(2019)Relevant Subsection Retrieval for Law Domain Question Answer SystemData Visualization and Knowledge Engineering10.1007/978-3-030-25797-2_13(299-319)Online publication date: 10-Aug-2019
https://doi.org/10.1007/978-3-030-25797-2_13
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Index Terms

Ontology-based model of law retrieval system for R&D projects
1. Information systems
  1. Information retrieval
    1. Retrieval models and ranking

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Published In

ICEC '16: Proceedings of the 18th Annual International Conference on Electronic Commerce: e-Commerce in Smart connected World

August 2016

311 pages

ISBN:9781450342223

DOI:10.1145/2971603

Conference Chairs:
Toru Ishida
Kyoto Univ., Japan
,
Norman Sadeh
CMU
,
Jae Kyu Lee
KAIST, Korea
,
Federico Casalegno
MIT
,
Wooju Kim
Yonsei Univ., Korea
,
Program Chairs:
Sohyeong Kim
Stanford Univ.
,
Sung-Byung Yang
Kyung Hee Univ., Korea

Publication rights licensed to ACM. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 August 2016

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Korea Railroad Research Institute

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ICEC '16

Sponsor:

BigBang Angels
Benple
Women's News Inc.
FKII
Korea Internet Corporations Association
KOFST
NIA
HAREX
Haitai
KTO
G-MICE Bureau
IT Daily
Suwon City
ALLWIN
DIPA
Tech M

ICEC '16: International Conference on Electronic Commerce 2016

August 17 - 19, 2016

Suwon, Republic of Korea

Acceptance Rates

ICEC '16 Paper Acceptance Rate 44 of 55 submissions, 80%;

Overall Acceptance Rate 150 of 244 submissions, 61%

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Cited By

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Parashar SMittal NMehta P(2023)CASRank: A ranking algorithm for legal statute retrievalMultimedia Tools and Applications10.1007/s11042-023-15464-083:2(5369-5386)Online publication date: 2-Jun-2023
https://doi.org/10.1007/s11042-023-15464-0
Stoianova OMoskaleva V(2021)Ontologies to Reduce Uncertainty in R&D Project PlanningProceedings of the Fifth International Scientific Conference “Intelligent Information Technologies for Industry” (IITI’21)10.1007/978-3-030-87178-9_37(370-379)Online publication date: 16-Sep-2021
https://doi.org/10.1007/978-3-030-87178-9_37
Verma AMorato JJain AArora A(2019)Relevant Subsection Retrieval for Law Domain Question Answer SystemData Visualization and Knowledge Engineering10.1007/978-3-030-25797-2_13(299-319)Online publication date: 10-Aug-2019
https://doi.org/10.1007/978-3-030-25797-2_13
Li ZZhuang CGe JLi CLei TLing PHe MLuo B(2018)Predicting Statutes Based on Causes of Action and Content of StatutesData Science10.1007/978-981-13-2206-8_39(477-492)Online publication date: 9-Sep-2018
https://doi.org/10.1007/978-981-13-2206-8_39
Li CYe JGe JKong LHu HLuo B(2018)A Novel Convolutional Neural Network for Statutes RecommendationPRICAI 2018: Trends in Artificial Intelligence10.1007/978-3-319-97304-3_65(851-863)Online publication date: 27-Jul-2018
https://doi.org/10.1007/978-3-319-97304-3_65

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