Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction

Abstract

Term extraction is an important task that automatically extracts relative terms from the texts in a given domain. A significant number of web applications need to model information for specific topics. In particular, we have explored a Taiwan government website that maintains the Laws & Regulations Database of the Republic of China (R.O.C) to provide the current Chinese law text to the public. However, the main issue is that there is no efficient structured method to handle such a large number of law texts. Therefore, in this paper, we propose a novel approach to extract legal as well as domain-relative terms, and then build a law ontology. We used the current Chinese law text from the Laws & Regulations Database as the data source. We then utilized natural language processing tools and data mining techniques to extract legal keywords and their definitions automatically. Subsequently, we constructed a Taiwan law ontology with the legal keywords and relative definitions. We have extracted 1114 legal keywords with definitions. With the characteristics of an ontology, users can view one keyword with its information and the associated keywords. Furthermore, we provide a service, which includes both the graphical and text interfaces to users on the web, such that a user can readily access the legal information on the Internet.

An ontology-based knowledge representation using OWL for Indonesian local regulations

“…ViLO was developed as an ontology for Vietnamese legal documents utilizing the NeOn methodology framework [4]. Hwang et al [5] constructed a law ontology in Taiwan with legal keywords and relative definitions extracted from the laws and regulations database of the Republic of China represented in textual documents. Phan et al [6] developed a legal taxonomy of semantic types in Korean legislation.…”

A Semi-automated Ontology Construction for Legal Question Answering

“…This part reuses existing ontologies and extracts similar and complementary information, while the bottom-up strategy captures relevant legal concepts and relations from textual sources using some NLP techniques. Hwang et al [12] describe a technique for an automatic ontology construction from a structured text (databases). The approach captures legal concepts and relations from the Chinese Laws and Regulations Database and then constructs a law ontology.…”