“智慧法院”数据融合分析与集成应用
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摘要
针对"智慧法院"建设中存在的共性问题和实际需求,介绍了"智慧法院"数据融合分析及集成应用示范平台的架构。从司法大数据深度语义学习、基于知识图谱的司法数据融合、司法数据安全防护与隐私保护以及司法数据融合分析的可视化4个方面,探讨了"智慧法院"建设中共性关键技术的研究思路和实现路径。最后,以证据抽取、犯罪行为链构建和法律条文推荐为例,展现了数据融合分析及集成应用示范平台的应用效果。研究成果对实现以法院司法数据为核心的新一代"智慧法院"建设目标具有一定的参考价值。
In view of the common problems and practical needs in the construction of "Intelligent Court", the framework of "Intelligent Court" data fusion analysis and integration application demonstration platform was introduced. The research ideas and implementation paths of common key technologies in the construction of "Intelligent Court" were put forward from four aspects: deep semantic learning of judicial big data, judicial data fusion based on knowledge map, judicial data security protection and privacy protection, and visualization of judicial data fusion analysis. Finally, taking evidence extraction,criminal chain construction and legal provisions recommendation as examples, the application effect of data fusion analysis and integrated application demonstration platform was analyzed. The research results have certain reference value for realizing the goal of building a new generation of "Intelligent Court" with the judicial data of courts as the core.
In view of the common problems and practical needs in the construction of "Intelligent Court", the framework of "Intelligent Court" data fusion analysis and integration application demonstration platform was introduced. The research ideas and implementation paths of common key technologies in the construction of "Intelligent Court" were put forward from four aspects: deep semantic learning of judicial big data, judicial data fusion based on knowledge map, judicial data security protection and privacy protection, and visualization of judicial data fusion analysis. Finally, taking evidence extraction,criminal chain construction and legal provisions recommendation as examples, the application effect of data fusion analysis and integrated application demonstration platform was analyzed. The research results have certain reference value for realizing the goal of building a new generation of "Intelligent Court" with the judicial data of courts as the core.
引文
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[2]陈刚.运用大数据思维和手段提升政府治理能力[J].大数据时代,2017(3):6-13.CHEN G.Using big data thinking and means to improve governance ability[J].Big Data Time,2017(3):6-13.
[3]连玉明.“人在干、云在算”--“数据铁笼”助力政府治理[J].大数据时代,2017(3):50-54.LIAN Y M.“Man is doing things,cloud is calculating”-data cage helps government governance[J].Big Data Time,2017(3):50-54.
[4]鲍旭华,曲晓东,郑新华.大数据驱动的安全协同生态建设[J].大数据,2018,4(3):93-100.BAO X H,QU X D,ZHENG X H.Big data driven security collaborative ecological construction[J].Big Data Research,2018,4(3):93-100.
[5]张平文,鄂维南,袁晓如,等.大数据分析与应用技术创新平台[J].大数据,2018,4(4):86-93.ZHANG P W,E W N,YUAN X R,et al.Big data analysis and application technology innovation platform[J].Big Data Research,2018,4(4):86-93.
[6]卢英华.大数据在智慧城市规划中的应用[J].智能建筑与智慧城市,2019(1):75-76.LU Y H.Application of big data in intelligent urban planning[J].Intelligent Building and Smart City,2019(1):75-76.
[7]和芫.人工智能进法院:对科技应用于司法的思考[J].科技与法律,2018(6):77-88.HE Y.Artificial intelligence entering the court:thoughts on the application of science and technology to justice[J].Science Technology and Law,2018(6):77-88.
[8]WATERMAN D A,PAUL J,PETERSON MA,et al.Expert systems for legal decision making[J].Expert Systems,1986,3(4):212-226.
[9]REMUS D,LEVYF S.Can robots be lawyers?Computer s,law yers,and the practice of law[J].SSRN Electronic Journal,2015.
[10]PAYNE S,KOOPS K.Opening remarks:a potpourri of recent developments affecting the teaching of transactional law and skills[J].Tra nsact ions:T he Ten nessee Journal of Business Law,2016,18(2):333-342.
[11]张保生.人工智能法律系统的法理学思考[J].法学评论,2001(5):11-21.ZHANG B S.Jurisprudential thoughts on artificial intelligence legal system[J].Law Review,2001(5):11-21.
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[13]郝铁川.不可幻想和高估人工智能对法治的影响[N].法制日报,2018-01-03.HAO T C.Can’t overestimate and fantasy the artificial intelligence influences on the rule of law[N].Legal Daily,2018-01-03.
[14]DAMASHEK M.Gauging similarity with n-grams:language-in dependent categorization of text[J].Science,1995,267(5199):843-848.
[15]ZELENKO D,AONE C,RICHARDELLAA.Kernel methods for relation extraction[J].Journal of Machine Learning Research,2003,3(3):1083-1106.
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[25]LIU M,ZHANG D.Pairwise constraint-guided sparse learning for feature selection[J].IEEETransactions on Cybernetics,2017,46(1):298-310.
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[30]G H I N I TA G,Z H AO K,PA PA D I A S D,et al.A reciprocal framework for spatial K-anonymity[J].Information Systems,2010,35(3):299-314.
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