# Building a Taiwan Law Ontology Based on Automatic Legal Definition Extraction
## 1 Introduction  
With the prevalence of the Internet, people tend to search information, for example, that concerning laws on the Web. However, it may take users a lot of time to acquire the \"right\" information since there is a lack of structured methods to effectively express the information. Furthermore, the content of some specific domain knowledge is too hard for users to fully understand. In those countries governed by law, law involves a legal compelling force to secure civil rights. Law systems can be classified into two categories: common law and continental law. Taiwan adopts the continental law system whereby law is a social norm, and numbers of laws and regulations have been enacted to deal with possible arguments or conflicts that people may encounter in their daily life. Therefore, it is important for people to know the definitions of legal terms as they are used in specific laws. In Taiwan's legal system, there are rules to name laws and regulations. According to Articles 2 and 3 of the Central Regulation Standard Act [1], statutes are named as acts, penal acts, special acts or comprehensive acts. Other ordinances publicized by a government agency according to their properties are named organic rules, norm rules, enforcement rules, directive rules, guidance rules, standard rules or canon rules. Currently, there are over six thousand laws and regulations, classified as acts or rules, in Taiwan. The Laws & Regulations Database of the Republic of China (Taiwan) is a legal platform on the Internet designed to provide the latest news and a query function [2]. It is convenient for peopleto acquire legal information; however, understanding this legislation still requires a certain level of knowledge on the part of the public. Hence, organizing and identifying professional knowledge by technical methods, and providing an easy and convenient service for users have become a potential issue of knowledge retrieval. Considering that the number of laws is huge and they are mostly expressed in a descriptive way without proper structural form, it is difficult for users to understand the legislation immediately. Thus, the goal of this paper is to develop a tool that facilitates expressing knowledge related to Taiwan's laws and making the legal information more accessible to users. In the field of knowledge engineering, the semantic web is an extension of the web through standards by the World Wide Web Consortium (W3C); these standards promote common data formats and exchange protocols on the Web [3]. Ontology is a knowledge representation method in the semantic web, which originated in the field of philosophy and which concerns what entities exist or can be said to exist [4]. In computer science, ontology is a practical application of philosophical ontology, which defines the types, properties, and interrelationships of the entities that really or fundamentally exist in particular domain knowledge [5]. In this paper, based on the current legal database, words can be analyzed by extracted keywords [6] and Chinese word segmentation tools. Chinese is more complex than English in terms of natural language processing (NLP) [7] because it does not use spaces to separate words. Therefore, using a Chinese word segmentation tool to pre-process the Chinese text is necessary. The legal keywords and their relative definitions from statutes can then be extracted using an automatic method. In our previous work [6], support vector machine (SVM) and expertise knowledge were used to automatically extract legal keywords from Taiwan's law database. Based on the extracted legal keywords, we proceed to find the existing relationships between the keywords. To make our ontology closer to the characteristics of laws, we compared the existing elements of the ontologies from different domains, and established the elements of the ontology which are suitable for the field of law. There were also a number of issues that needed to be addressed. First, it is a challenge to figure out the related attributes and the relationships between each keyword.