Article

Searching and retrieving legal literature through automated semantic indexing

Authors:

E. Francesconi,

G. PeruginelliAuthors Info & Claims

ICAIL '07: Proceedings of the 11th international conference on Artificial intelligence and law

Pages 131 - 139

https://doi.org/10.1145/1276318.1276343

Published: 04 June 2007 Publication History

Abstract

Access to legal information and, in particular, to legal literature is examined in conjunction with the creation of a Portal to Italian legal doctrine. The design and implementation of services such as integrated access to a wide range of resources are described, with a particular focus on the importance of exploiting metadata assigned to disparate legal material. The integration of structured repositories and Web documents is the main purpose of the Portal: it is constructed on the basis of a federation system with service provider functions, aiming at creating a centralized index of legal resources. The index is based on a uniform metadata view created for structured data by means of the OAI approach and for Web documents by a machine learning approach. Subject searching is a major requirement for legal literature users and a solution based on the exploitation of Dublin Core metadata, as well as the use of legal ontologies and related terms prepared for accessing indexed articles have been implemented.

References

[1]

CYCLADES - An Open Collaborative Virtual Archive Environment. (http://www.ercim.org/cyclades/).

[2]

OAI - The Open Archives Initative Protocol for Metadata Harvesting. http://www.openarchives.org/OAI/openarchivesprotocol.htm.

[3]

Swish-e, Simple Web Indexing System for Humans Enhanced. Retrieved July 7, 2004, (http://swish-e.org).

[4]

TEL - The European Library. (http://www.europeanlibrary.org).

[5]

TORII - The Digital Research Community. (http://torii.sissa.it).

[6]

A. Apps. A journal article bibliographic citation dublin core structured value. Retrieved on May 2, 2003 from (http://epub.mimas.ac.uk/DC/citdcsv.html), 2003.

[7]

C. Biagioli, E. Francesconi, A. Passerini, S. Montemagni, and C. Soria. Automatic semantics extraction in law documents. In Proceedings of International Conference on Artificial Intelligence and Law, pages 133--139, 2005.

Digital Library

[8]

C. Buckley and G. Salton. Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5):513--523, 1988.

Digital Library

[9]

C. Burges. A tutorial on support vector machines for pattern recognition. In Data Mining and Knowledge Discovery. Kluwer Academic Publishers, Boston, 1998. (Volume 2).

Digital Library

[10]

S. W. C. Apté, F. J. Damerau. Automated learning of decision rules for text categorization. ACM Transactions on Information Systems, 12(3):233--251, 1994.

Digital Library

[11]

C. Cortes and V. Vapnik. Support vector networks. Machine Learning, 20:1--25, 1995.

[12]

K. Crammer and Y. Singer. On the algorithmic implementation of multiclass kernel-based vector machines. Journal on Machine Learning Research, 2:265--292, 2002.

Digital Library

[13]

S. Dumais, J. Platt, D. Heckerman, and M. Sahami. Inductive learning algorithms and representations for text categorization. In CIKM '98: Proceedings of the seventh international conference on Information and knowledge management, pages 148--155, New York, NY, USA, 1998. ACM Press.

Digital Library

[14]

E. Francesconi and G. Peruginelli. Opening the legal literature portal to multilingual access. In Proceedings of the Dublin Core Conference, pages 37--44, 2004.

Digital Library

[15]

B. Hachey and C. Grover. Automatic legal text summarisation: Experiments with summary structuring. In Proceedings of International Conference on Artificial Intelligence and Law, pages 75--84, 2005.

Digital Library

[16]

C.-W. Hsu and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415--425, 2002.

Digital Library

[17]

W. R. J. Greenberg. Semantic web construction: An inquiry of authors' views on collaborative metadata generation. In Proceedings of the International Conference on Dublin Core and Metadata for e-Communities, pages 45--52, 2002.

Digital Library

[18]

T. Joachims. A probabilistic analysis of the rocchio algorithm with tfidf for text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 143--151. Morgan Kaufmann Publishers Inc., San Francisco, US, 1997.

Digital Library

[19]

A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. In Information Retrieval Journal, pages 127--163, 2000.

Digital Library

[20]

M.-F. Moens. Combining structured and unstructured information in a retrieval model for accessing legislation. In Proceedings of International Conference on Artificial Intelligence and Law, pages 141--145, 2005.

Digital Library

[21]

J. Quinlan. Inductive learning of decision trees. Machine Learning, 1:81--106, 1986.

Digital Library

[22]

F. Sebastiani. Machine learning in automated text categorization. ACM Computing Surveys, 34(1):1--47, 2002.

Digital Library

[23]

V. Vapnik. Statistical Learning Theory. Wiley, New York, 1998.

Digital Library

[24]

Y. Yang and J. Pedersen. A comparative study on feature selection in text categorization. In Proceedings of the Fourteenth International Conference on Machine Learning, pages 412--420. Morgan Kaufmann Publishers Inc., 1997.

Digital Library

Cited By

Tetteh E(2022)Public Administration Curriculum-Based Big Data Policy-Analytic EpistemologyResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch063(1307-1328)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch063
Tetteh E(2019)Public Administration Curriculum-Based Big Data Policy-Analytic EpistemologyHandbook of Research on Big Data and the IoT10.4018/978-1-5225-7432-3.ch024(467-488)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7432-3.ch024
Sawant VGhorpade V(2014)Automatic semantic classification and categorization of web services in digital environmentInternational Conference on Computing and Communication Technologies10.1109/ICCCT2.2014.7066749(1-6)Online publication date: Dec-2014
https://doi.org/10.1109/ICCCT2.2014.7066749
Show More Cited By

Searching and retrieving legal literature through automated semantic indexing
1. Information systems
  1. Information retrieval
  2. Information systems applications

Recommendations

Semantic indexing of legal documents
Semantic Processing of Legal Texts

Automated semantic indexing may be the answer to insufficient recall of legal information systems. The semantic web has created powerful tools for mark-up and ontological representation. Re-use in legal applications remains low due to inappropriate ...
Integrated access to legal literature through automated semantic classification

Access to legal information and, in particular, to legal literature is examined for the creation of a search and retrieval system for Italian legal literature. The design and implementation of services such as integrated access to a wide range of ...
A RDF-based graph to representing and searching parts of legal documents
Abstract
Despite the public availability of legal documents, there is a need for finding specific information contained in them, such as paragraphs, clauses, items and so on. With such support, users could find more specific information than only finding ...

Comments

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICAIL '07: Proceedings of the 11th international conference on Artificial intelligence and law

June 2007

302 pages

ISBN:9781595936806

DOI:10.1145/1276318

Conference Chair:
Anne Gardner
Atherton, California
,
Program Chair:
Radboud Winkels
Leibniz Center for Law, Universiteit van Amsterdam, The Netherlands

Copyright © 2007 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 ACM 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

International Association for Artificial Intelligence and Law

In-Cooperation

SIGAI: ACM Special Interest Group on Artificial Intelligence

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 June 2007

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Article

Conference

ICAIL07

Sponsor:

ICAIL07: 11th International Conference on Artificial Intelligence and Law

June 4 - 8, 2007

California, Stanford

Acceptance Rates

Overall Acceptance Rate 69 of 169 submissions, 41%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

7
Total Citations
View Citations
436
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 01 Mar 2025

Other Metrics

View Author Metrics

Citations

Cited By

Tetteh E(2022)Public Administration Curriculum-Based Big Data Policy-Analytic EpistemologyResearch Anthology on Big Data Analytics, Architectures, and Applications10.4018/978-1-6684-3662-2.ch063(1307-1328)Online publication date: 2022
https://doi.org/10.4018/978-1-6684-3662-2.ch063
Tetteh E(2019)Public Administration Curriculum-Based Big Data Policy-Analytic EpistemologyHandbook of Research on Big Data and the IoT10.4018/978-1-5225-7432-3.ch024(467-488)Online publication date: 2019
https://doi.org/10.4018/978-1-5225-7432-3.ch024
Sawant VGhorpade V(2014)Automatic semantic classification and categorization of web services in digital environmentInternational Conference on Computing and Communication Technologies10.1109/ICCCT2.2014.7066749(1-6)Online publication date: Dec-2014
https://doi.org/10.1109/ICCCT2.2014.7066749
Dong HHussain F(2011)Focused Crawling for Automatic Service Discovery, Annotation, and Classification in Industrial Digital EcosystemsIEEE Transactions on Industrial Electronics10.1109/TIE.2010.205075458:6(2106-2116)Online publication date: Jun-2011
https://doi.org/10.1109/TIE.2010.2050754
Dong HHussain FChang E(2011)A framework for discovering and classifying ubiquitous services in digital health ecosystemsJournal of Computer and System Sciences10.1016/j.jcss.2010.02.00977:4(687-704)Online publication date: 1-Jul-2011
https://dl.acm.org/doi/10.1016/j.jcss.2010.02.009
Dong HHussain FChang E(2009)State of the Art in Semantic Focused CrawlersProceedings of the International Conference on Computational Science and Its Applications: Part II10.1007/978-3-642-02457-3_74(910-924)Online publication date: 9-Jul-2009
https://dl.acm.org/doi/10.1007/978-3-642-02457-3_74
Hai Dong Farookh Khadeer Hussain Chang E(2008)State of the art in metadata abstraction crawlers2008 IEEE International Conference on Industrial Technology10.1109/ICIT.2008.4608573(1-6)Online publication date: Apr-2008
https://doi.org/10.1109/ICIT.2008.4608573

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Figures

Tables

Media

View Table of Conten