Abstract
The internet and the development of the semantic web have created the opportunity to provide structured legal data on the web. However, most legal information is in text. It is difficult to automatically determine the right natural language answer about the law to a given natural language question. One approach is to develop systems of legal ontologies and rules. Our example ontology represents semantic information about USA criminal law and procedure as well as the applicable legal rules. The purpose of the ontology is to provide reasoning support to a legal question answering tool that determines entailment between a pair of texts, one known as the background information (Bg) and the other question statement (Q), so whether Bg entails Q based on the application of the legal rules. The key contribution of this paper is the methodology and the semi-automated legal ontology generation tool, a clear and well-structured methodology that serves to develop such criminal law ontologies and rules (CLOR).
Highlights
Legal knowledge is usually expressed with domain-specific terminology and conveyed in textual form
We manually evaluated the ontology generated from the semiautomatic legal ontology generation tool against the manually constructed ontology in [5], which establishes our gold standard
We have developed a methodology and semi-automated legal ontology generation tool following our step-by-step approach in OWL with legal rules in Semantic Web Rule Language (SWRL) to infer conclusions
Summary
Legal knowledge is usually expressed with domain-specific terminology and conveyed in textual form. That to make the source text into something suitable for the textual entailment task, we have revised the multiple-choice questions into Bg and individual Q pairs, where any background information in the source Q is put into the Bg. For example, as shown, we took option b, made a proposition’ Mel should be acquitted’ as Q, and introduced’ his mistake negated required specific intent’ as part of the background [4]. A range of approaches can be applied to the textual entailment task, e.g. machine learning, lexical and syntactic information and semantic dependencies These techniques lack the sort of legal knowledge and reasoning required to determine entailment in the text representing bar examination questions.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
