LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References
Pages 3300 - 3305
Abstract
We present LawNet-Viz, a web-based tool for the modeling, analysis and visualization of law reference networks extracted from a statute law corpus. LawNet-Viz is designed to support legal research tasks and help legal professionals as well as laymen visually exploring the article connections built upon the explicit law references detected in the article contents. To demonstrate LawNet-Viz, we show its application to the Italian Civil Code (ICC), which exploits a recent BERT-based model fine-tuned on the ICC. LawNet-Viz is a system prototype that is planned for product development.
References
[1]
Tommaso Agnoloni and Ugo Pagallo. 2015. The Case Law of the Italian Constitutional Court, Its Power Laws, and the Web of Scholarly Opinions. In Proc. of the 15th International Conference on Artificial Intelligence and Law. Association for Computing Machinery, 151--155. https://doi.org/10.1145/2746090.2746108
[2]
Mathieu Bastian, Sebastien Heymann, and Mathieu Jacomy. 2009. Gephi: An Open Source Software for Exploring and Manipulating Networks. In Proc. of the Third International Conference on Weblogs and Social Media (ICWSM). The AAAI Press.
[3]
Paul Boniol, George Panagopoulos, Christos Xypolopoulos, Rajaa El Hamdani, David Restrepo Amariles, and Michalis Vazirgiannis. 2020. Performance in the Courtroom: Automated Processing and Visualization of Appeal Court Decisions in France. In Proc. of the Natural Legal Language Processing Workshop 2020 co-located with the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (CEUR Workshop Proceedings, Vol. 2645). CEUR-WS.org, 11--17.
[4]
Ilias Chalkidis, Manos Fergadiotis, Prodromos Malakasiotis, Nikolaos Aletras, and Ion Androutsopoulos. 2020. LEGAL-BERT: The Muppets straight out of Law School. CoRR, Vol. abs/2010.02559 (2020).
[5]
Ademar Crotti Junior, Fabrizio Orlandi, Damien Graux, Murhaf Hossari, Declan O'Sullivan, Christian Hartz, and Christian Dirschl. 2020. Knowledge Graph-Based Legal Search over German Court Cases. In Proc. of the Semantic Web Conference: ESWC 2020 Satellite Events. 293--297. https://doi.org/10.1007/978-3-030-62327-2_44
[6]
Faraz Dadgostari, Mauricio Guim, P. Beling, Michael A. Livermore, and D. Rockmore. 2021. Modeling law search as prediction. Artificial Intelligence and Law, Vol. 29, 1 (2021), 3--34.
[7]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In Proc. of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT). 4171--4186.
[8]
Erwin Filtz. 2017. Building and Processing a Knowledge-Graph for Legal Data. In Proc. of the 16th International Semantic Web Conference. Springer International Publishing, 184--194.
[9]
Erwin Filtz, Sabrina Kirrane, and Axel Polleres. 2021. The linked legal data landscape: linking legal data across different countries. Artificial Intelligence and Law, Vol. 29, 4 (2021), 485--539. https://doi.org/10.1007/s10506-021-09282-8
[10]
James H. Fowler, Timothy R. Johnson, James F. Spriggs, Sangick Jeon, and Paul J. Wahlbeck. 2007. Network Analysis and the Law: Measuring the Legal Importance of Precedents at the U.S. Supreme Court. Political Analysis, Vol. 15, 3 (2007), 324--346. https://doi.org/10.1093/pan/mpm011
[11]
Ademar Crotti Junior, Fabrizio Orlandi, Declan O'Sullivan, Christian Dirschl, and Quentin Reul. 2019. Using Mapping Languages for Building Legal Knowledge Graphs from XML files. In Proc. of the Blockchain enabled Semantic Web Workshop (BlockSW) and Contextualized Knowledge Graphs (CKG) Workshop co-located with the 18th International Semantic Web Conference (CEUR Workshop Proceedings, Vol. 2599). CEUR-WS.org.
[12]
Marios Koniaris, Ioannis Anagnostopoulos, and Yannis Vassiliou. 2017. Network analysis in the legal domain: a complex model for European Union legal sources. Journal of Complex Networks, Vol. 6, 2 (08 2017), 243--268. https://doi.org/10.1093/comnet/cnx029
[13]
Lucio La Cava, Andrea Simeri, and Andrea Tagarelli. 2021. The Italian civil code network analysis, In Proc. of the Relations in the Legal Domain Workshop co-located with the 15th International Conference on Artificial Intelligence and Law. CEUR Workshop Proceedings, Vol. 2896, 3--16.
[14]
Bokwon Lee, Kyu-Min Lee, and Jae-Suk Yang. 2019. Network structure reveals patterns of legal complexity in human society: The case of the Constitutional legal network. PLOS ONE, Vol. 14, 1 (01 2019), 1--15. https://doi.org/10.1371/journal.pone.0209844
[15]
Nicola Lettieri, Antonio Altamura, Armando Faggiano, and Delfina Malandrino. 2016. A computational approach for the experimental study of EU case law: analysis and implementation. Social Network Analysis and Mining, Vol. 6, 1 (2016), 56. https://doi.org/10.1007/s13278-016-0365--6
[16]
Nicola Lettieri, Antonio Altamura, and Delfina Malandrino. 2017. The legal macroscope: Experimenting with visual legal analytics. Information Visualization, Vol. 16, 4 (2017), 332--345. https://doi.org/10.1177/1473871616681374
[17]
Luoqiu Li, Zhen Bi, Hongbin Ye, Shumin Deng, Hui Chen, and Huaixiao Tou. 2021. Text-guided Legal Knowledge Graph Reasoning. In Proc. of the China Conference on Knowledge Graph and Semantic Computing. 27--39.
[18]
Pierre Mazzega, Danièle Bourcier, and Romain Boulet. 2009. The Network of French Legal Codes. In Proc. of the 12th International Conference on Artificial Intelligence and Law. 236--237. https://doi.org/10.1145/1568234.1568271
[19]
Kody Moodley, Pedro V Hernandez-Serrano, Amrapali J Zaveri, Marcel GH Schaper, Michel Dumontier, and Gijs Van Dijck. 2020. The Case for a Linked Data Research Engine for Legal Scholars. European Journal of Risk Regulation, Vol. 11, 1 (2020), 70--93. https://doi.org/10.1017/err.2019.51
[20]
Markus Moser and Mark Strembeck. 2019. An Analysis of Three Legal Citation Networks Derived from Austrian Supreme Court Decisions. In Proc. of the 4th International Conference on Complexity, Future Information Systems and Risk. 85--92. https://doi.org/10.5220/0007749900850092
[21]
Radim Řehrůřrek and Petr Sojka. 2010. Software Framework for Topic Modelling with Large Corpora. In Proc. of the LREC 2010 Workshop on New Challenges for NLP Frameworks. 45--50.
[22]
Neda Sakhaee and Mark C. Wilson. 2021. Information extraction framework to build legislation network. Artificial Intelligence and Law, Vol. 29, 1 (2021), 35--58. https://doi.org/10.1007/s10506-020-09263-3
[23]
Francesco Sovrano, Monica Palmirani, and Fabio Vitali. 2020. Legal knowledge extraction for knowledge graph based question-answering. In Legal Knowledge and Information Systems. IOS Press, 143--153.
[24]
Emilio Sulis, Llio Humphreys, Fabiana Vernero, Ilaria Angela Amantea, Luigi Di Caro, Davide Audrito, and Stefano Montaldo. 2020. Exploring Network Analysis in a Corpus-Based Approach to Legal Texts: A Case Study. In Proc. of the CAiSE for Legal Documents (COUrT) Workshop co-located with the the 33rd International Conference on Advanced Information Systems. 27--38.
[25]
Andrea Tagarelli and Andrea Simeri. 2021. Unsupervised law article mining based on deep pre-trained language representation models with application to the Italian civil code. Artificial Intelligence and Law (2021), 1--57. https://doi.org/10.1007/s10506-021-09301-8
[26]
Ryan Whalen. 2016. Legal networks: The promises and challenges of legal network analysis. Michigan State Law Review (2016), 539.
[27]
Gineke Wiggers and Suzan Verberne. 2019. Citation Metrics for Legal Information Retrieval Systems. In Proc. of the 8th International Workshop on Bibliometric-enhanced Information Retrieval co-located with the 41st European Conference on Information Retrieval (CEUR Workshop Proceedings, Vol. 2345). CEUR-WS.org, 39--50.
[28]
Paul Zhang and Lavanya Koppaka. 2007. Semantics-based legal citation network. In Proc. of the 11th International Conference on Artificial Intelligence and Law. 123--130. https://doi.org/10.1145/1276318.1276342
[29]
Lucia Zheng, Neel Guha, Brandon R. Anderson, Peter Henderson, and Daniel E. Ho. 2021. When does pretraining help?: assessing self-supervised learning for law and the CaseHOLD dataset of 53, 000 legal holdings. In Proc. of the Eighteenth International Conference for Artificial Intelligence and Law (ICAIL), Juliano Maranh a o and Adam Zachary Wyner (Eds.). ACM, 159--168. https://doi.org/10.1145/3462757.3466088
Index Terms
- LawNet-Viz: A Web-based System to Visually Explore Networks of Law Article References
Recommendations
Technology report: Intelligent summoner
Trial Courts all over the world have a common problem concerning how to regulate the number of jurors to summon so that there is a sufficient but not excess supply available for scheduled trials. Many trials end abruptly just before jurors are selected ...
Rediscovering artificial intelligence and law: an inadequate jurisprudence?
Headlines suggesting that Google scientists had developed the first computer programme capable of learning a wide variety of tasks independently, in what has been hailed as a significant step towards ‘true artificial intelligence’1 may or may not ...
Introduction: A Hybrid Regulatory Framework and Technical Architecture for a Human-Centered and Explainable AI
AI Approaches to the Complexity of Legal Systems XI-XIIAbstractThis introduction presents the fifth volume of a series started twelve years ago: the AI Approaches to the Complexity of Legal Systems (AICOL). The introduction revises the recurrently addressed topics of technology, Artificial Intelligence and ...
Comments
Information & Contributors
Information
Published In
July 2022
3569 pages
ISBN:9781450387323
DOI:10.1145/3477495
- General Chairs:
- Enrique Amigo,
- Pablo Castells,
- Julio Gonzalo,
- Program Chairs:
- Ben Carterette,
- J. Shane Culpepper,
- Gabriella Kazai
Copyright © 2022 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 07 July 2022
Check for updates
Author Tags
Qualifiers
- Short-paper
Conference
SIGIR '22
Sponsor:
SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
July 11 - 15, 2022
Madrid, Spain
Acceptance Rates
Overall Acceptance Rate 792 of 3,983 submissions, 20%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 463Total Downloads
- Downloads (Last 12 months)128
- Downloads (Last 6 weeks)20
Reflects downloads up to 29 Jan 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in