Improving legal information retrieval using an ontological framework

A variety of legal documents are increasingly being made available in electronic format. Automatic Information Search and Retrieval algorithms play a key role in enabling efficient access to such digitized documents. Although keyword-based search is the traditional method used for text retrieval, they perform poorly when literal term matching is done for query processing, due to synonymy and ambivalence of words. To overcome these drawbacks, an ontological framework to enhance the user’s query for retrieval of truly relevant legal judgments has been proposed in this paper. Ontologies ensure efficient retrieval by enabling inferences based on domain knowledge, which is gathered during the construction of the knowledge base. Empirical results demonstrate that ontology-based searches generate significantly better results than traditional search methods.

[1]  Marie-Francine Moens,et al.  Automatic Indexing and Abstracting of Document Texts , 2000, Computational Linguistics.

[2]  M. Saravanan,et al.  Improving Legal Document Summarization Using Graphical Models , 2006, JURIX.

[3]  Daniel Marcu,et al.  Bayesian Query-Focused Summarization , 2006, ACL.

[4]  Steffen Staab,et al.  Ontologies improve text document clustering , 2003, Third IEEE International Conference on Data Mining.

[5]  N. Guarino,et al.  Formal Ontology in Information Systems : Proceedings of the First International Conference(FOIS'98), June 6-8, Trento, Italy , 1998 .

[6]  Chris Buckley,et al.  New Retrieval Approaches Using SMART: TREC 4 , 1995, TREC.

[7]  Bernice W. Polemis Nonparametric Statistics for the Behavioral Sciences , 1959 .

[8]  Trevor J. M. Bench-Capon,et al.  Ontologies in legal information systems; the need for explicit specifications of domain conceptualisations , 1997, ICAIL '97.

[9]  Karen Sparck Jones,et al.  Book Reviews: Evaluating Natural Language Processing Systems: An Analysis and Review , 1996, CL.

[10]  Rubén Prieto Díaz A Faceted Approach to Building Ontologies. , 2003 .

[11]  N. Isaacs,et al.  Fundamental Legal Conceptions as Applied in Judicial Reasoning: And Other Legal Essays , 2010 .

[12]  A. Valente,et al.  Legal Knowledge Engineering - A Modelling Approach , 1995 .

[13]  John E. Freund,et al.  Probability and statistics for engineers , 1965 .

[14]  A. Valente,et al.  Making Ends Meet: Conceptual Models and Ontologies in Legal Problem Solving. , 1994 .

[15]  Oren Kurland,et al.  Inter-Document Similiarities, Language Models, and Ad Hoc Information Retrieval , 2006 .

[16]  Thomas R. Gruber,et al.  Toward principles for the design of ontologies used for knowledge sharing? , 1995, Int. J. Hum. Comput. Stud..

[17]  Radboud Winkels,et al.  Use and Reuse of Legal Ontologies in Knowledge Engineering and Information Management , 2003, Law and the Semantic Web.

[18]  Nicola Guarino,et al.  Formal Ontology and Information Systems , 1998 .

[19]  Sylvie Szulman,et al.  TERMINAE: A Linguistic-Based Tool for the Building of a Domain Ontology , 1999, EKAW.

[20]  Aldo Gangemi,et al.  Some Ontological Tools to Support Legal Regulatory Compliance, with a Case Study , 2003, OTM Workshops.

[21]  Richard L. Scheaffer,et al.  Probability and statistics for engineers , 1986 .

[22]  M. Saravanan,et al.  A probabilistic approach to multi-document summarization for generating a tiled summary , 2005, Sixth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA'05).

[23]  Guiraude Lame Constructing an IR-oriented legal ontology , 2001 .

[24]  Trevor J. M. Bench-Capon,et al.  METHODOLOGIES FOR ONTOLOGY DEVELOPMENT , 2007 .

[25]  Inderjeet Mani,et al.  The Tipster Summac Text Summarization Evaluation , 1999, EACL.