“智慧法院”数据融合分析与集成应用

doi:10.11959/j.issn.2096-0271.2019022

大数据 ›› 2019, Vol. 5 ›› Issue (3): 35-46.doi: 10.11959/j.issn.2096-0271.2019022

“智慧法院”数据融合分析与集成应用

秦永彬^1,²,冯丽¹,陈艳平^1,²,黄瑞章^1,²,刘于雷³,丁红发¹

¹ 贵州大学计算机科学与技术学院，贵州贵阳 550025
² 贵州大学贵州省公共大数据重点实验室，贵州贵阳 550025
³ 多彩贵州网有限责任公司，贵州贵阳 550000

出版日期:2019-05-15 发布日期:2019-05-20
作者简介:秦永彬（1980-），男，博士，贵州大学计算机科学与技术学院教授，主要研究方向为大数据治理与应用、多源数据融合与应用、企业信息化与电子政务。|冯丽（1993-），女，贵州大学计算机科学与技术学院硕士生，主要研究方向为自然语言处理、数据融合分析。|陈艳平（1980-），男，博士，贵州大学计算机科学与技术学院副教授，主要研究方向为数据融合分析、自然语言处理、知识发现。|黄瑞章（1979-），女，博士，贵州大学计算机科学与技术学院副教授，主要研究方向为数据融合分析、文本挖掘、网络挖掘、知识发现。|刘于雷（1978-），男，多彩贵州网有限责任公司互联网大数据研发部负责人，贵州省互联网大数据与传播应用研发中心副主任，主要研究方向为互联网大数据应用、数据融合分析。|丁红发（1988-），男，贵州大学贵州省公共大数据重点实验室博士生，主要研究方向为隐私保护与数据安全、密码算法及应用。
基金资助:
国家自然科学基金资助项目(91746116);国家自然科学基金资助项目(U1836205);贵州省重大应用基础研究基金资助项目(JZ20142001);贵州省科技重大专项计划基金资助项目([2017]3002);贵州省自然科学基金资助项目([2018]1035)

“Intelligent Court” data fusion analysis and integrated application

Yongbin QIN^1,²,Li FENG¹,Yanping CHEN^1,²,Ruizhang HUANG^1,²,Yulei LIU³,Hongfa DING¹

¹ College of Computer Science ＆Technology,Guizhou University,Guiyang 550025,China
² Guizhou Provincial Key Laboratory of Public Big Data,Guizhou University,Guiyang 550025,China
³ Colorful Guizhou Net Company with Limited Liability,Guiyang 550000,China

Online:2019-05-15 Published:2019-05-20
Supported by:
The National Natural Science Foundation of China(91746116);The National Natural Science Foundation of China(U1836205);The Major Applied Basic Research Program of Guizhou Province(JZ20142001);Special Science and Technology Projects of Guizhou Province([2017]3002);The Natural Science Foundation of Guizhou Province([2018]1035)

摘要/Abstract

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。

关键词: 智慧法院, 知识图谱, 数据融合, 融合分析应用

Abstract:

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.

Key words: Intelligent Court, knowledge map, data fusion, application of fusion analysis

中图分类号:

TP391

秦永彬, 冯丽, 陈艳平, 黄瑞章, 刘于雷, 丁红发. “智慧法院”数据融合分析与集成应用[J]. 大数据, 2019, 5(3): 35-46.

Yongbin QIN, Li FENG, Yanping CHEN, Ruizhang HUANG, Yulei LIU, Hongfa DING. “Intelligent Court” data fusion analysis and integrated application[J]. Big Data Research, 2019, 5(3): 35-46.

图/表 5

参考文献 36

[1]	安小米, 郭明军, 洪学海 ,等. 政府大数据治理体系的框架及其实现的有效路径[J]. 大数据, 2019,5(3): 3-12.
	AN X M , GUO M J , HONG X H ,et al. Framework of government big data governance system and effective way of implementation[J]. Big Data Research, 2019,5(3): 3-12.
[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 M A ,et al. Expert systems for legal decision making[J]. Expert Systems, 1986,3(4): 212-226.
[9]	REMUS D , LEVY F S . Can robots be lawyers? Computers,lawyers,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]. Transactions:The Tennessee 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.
[12]	季卫东 . 人工智能时代的司法权之变[J]. 东方法学, 2018(1): 125-133.
	JI W D . Change of judicial power in the age of artificial intelligence[J]. Oriental Law, 2018(1): 125-133.
[13]	郝铁川 . 不可幻想和高估人工智能对法治的影响[J]. 法制日报,2018-01-03.
	HAO T C . Can’t overestimate and fantasy the artificial intelligence influences on the rule of law[J]. Oriental Law,2018-01-03.
[14]	DAMASHEK M . Gauging similarity with n-grams:language-independent categorization of text[J]. Science, 1995,267(5199): 843-848.
[15]	ZELENKO D , AONE C , RICHARDELLA A . Kernel methods for relation extraction[J]. Journal of Machine Learning Research, 2003,3(3): 1083-1106.
[16]	SAHAMI M , HEILMAN T D . A Web-based kernel function for measuring the similarity of short text snippets[C]// The 15th International Conference on World Wide Web,May 23-26,2006,Edinburgh,Scotland. New York:ACM Press, 2006: 377-386.
[17]	吴帅, 潘海珍 . 基于隐马尔可夫模型的中文分词[J]. 现代计算机, 2018(33): 27-30.
	WU S , PAN H Z . Chinese word segmentation based on hidden Markov model[J]. Modern Computer, 2018(33): 27-30.
[18]	Kambhatla , Nanda . Combining lexical,syntactic,and semantic features with maximum entropy models for extracting relations[C]// The 42nd Annual Meeting of the Association for Computational Linguistics,July 21-26,2004,Barcelona,Spain.[S.l.:s.n]. 2004.
[19]	CHEN Y , ZHENG Q , ZHANG W . Omni-word feature and soft constraint for Chinese relation extraction[C]// The 52nd Annual Meeting of the Association for Computational Linguistics,June 22-27,2014,Baltimore,USA.[S.l.:s.n]. 2014: 572-581.
[20]	JACOBS D W , DAUME H , KUMAR A ,et al. Generalized multiview analysis:a discriminative latent space[C]// 2012 IEEE Conference on Computer Vision and Pattern Recognition,June 16-21,2012,Providence,USA. Piscataway:IEEE Press, 2012: 2160-2167.
[21]	ZHOU J T , TSANG I W , PAN S J ,et al. Heterogeneous domain adaptation for multiple classes[C]// The 17th International Conference on Artificial Intelligence and Statistics,April 22-25,2014,Reykjavik,Iceland.[S.l.:s.n]. 2014: 1095-1103.
[22]	ZHU Y , CHEN Y , LU Z ,et al. Heterogeneous transfer learning for image classification[C]// The 25th AAAI Conference on Artificial Intelligence,August 7-11,2011,San Francisco,California. Palo Alto:AAAI Press, 2011: 1304-1309.
[23]	LIU K , WEI S , ZHAO Y ,et al. Accumulated reconstruction error vector (AREV):a semantic representation for cross-media retrieval[J]. Multimedia Tools and Applications, 2015,74(2): 561-576.
[24]	ZHANG X M , LI Z J , CHAO W H . Improving image tags by exploiting web search results[J]. Multimedia Tools and Applications, 2013,62(3): 601-631.
[25]	LIU M , ZHANG D . Pairwise constraint-guided sparse learning for feature selection[J]. IEEE Transactions on Cybernetics, 2017,46(1): 298-310.
[26]	郭增茂 . 知识管理中RS-CBR案例检索研究[D]. 郑州:郑州大学, 2014.
	GUO Z M . Research on RS-CBR case retrieval in knowledge management[D]. Zhengzhou:Zhengzhou University, 2014.
[27]	铁共 . 大数据应用安全挑战与实践[J]. 大数据时代, 2018(4): 43-49,42.
	TIE G . Security challenges and practice of big data applications[J]. Big Data Time, 2018(4): 43-49,42.
[28]	方贤进, 肖亚飞, 杨高明 . 大数据及其隐私保护[J]. 大数据, 2017,3(5): 45-56.
	FANG X J , XIAO Y F , YANG G M . Privacy preserving in the age of big data[J]. Big Data Research, 2017,3(5): 45-56.
[29]	AMRO B , SAYGIN Y , LEVI A ,et al. PA-CTM:privacy aware collaborative traffic monitoring system using autonomous location update mechanism[C]// The 4th ACM SIGSPATIAL International Workshop on Security and Privacy in GIS and LBS,November 1,2011,Chicago,USA. New York:ACM Press, 2011: 1-8.
[30]	GHINITA G , ZHAO K , PAPADIAS D ,et al. A reciprocal framework for spatial K-anonymity[J]. Information Systems, 2010,35(3): 299-314.
[31]	WU S , WANG X , WANG S ,et al. K-anonymity for crowdsourcing database[J]. IEEE Transactions on Knowledge and Data Engineering, 2014,26(9): 2207-2221.
[32]	施惠娟 . 可视化数据挖掘技术的研究与实现[D]. 上海:华东师范大学, 2010.
	SHI H J . Research and implementation of visual data mining technology[D]. Shanghai:East China Normal University, 2010.
[33]	汪加才, 陈奇, 赵杰煜 ,等. VISMiner:一个交互式可视化数据挖掘原型系统[J]. 计算机工程, 2003(1): 17-19.
	WANG J C , CHEN Q , ZHAO J Y ,et al. VISMiner:an interactive visual data mining prototyped system[J]. Computer Engineer, 2003(1): 17-19.
[34]	陈涛, 夏翠娟, 刘炜 ,等. 关联数据的可视化技术研究与实现[J]. 图书情报工作, 2015,59(17): 113-119.
	CHEN T , XIA C J , LIU W ,et al. Research and implementation of visualization technology for linked data[J]. Library and Information Service, 2015,59(17): 113-119.
[35]	袁海, 陈康, 陶彩霞 ,等. 基于中文文本的可视化技术研究[J]. 电信科学, 2014,30(4): 114-122.
	YUAN H , CHEN K , TAO C X ,et al. Research on visualization techniques based on chinese texts[J]. Telecommunications Science, 2014,30(4): 114-122.
[36]	CHEN Y , LIU S , ZHENG Q ,et al. Discovery of rare key phrases[C]// The 15th International Conference on e-Business Engineering(ICEBE),October 12-14,Xi’an,China. Piscataway:IEEE Press, 2018: 127-132.

“智慧法院”数据融合分析与集成应用

“Intelligent Court” data fusion analysis and integrated application

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