ATOB algorithm: an automatic ontology construction for Thai legal sentences retrieval

Abstract

Ontology plays an important role in knowledge representation, especially in the domain of information retrieval. However, building ontology remains a challenging problem because it is a time-consuming task for experts. To overcome these drawbacks, we propose a novel approach called the Automatic Thai Legal Ontology Building (ATOB) algorithm for automatic legal ontology building and to improve the court sentences retrieval process. The ATOB can automatically generate seed ontology and expand the ontology using Thai legal terminology, i.e. TLlexicon. The expansion process is terminated automatically by the threshold parameter. Moreover, the ATOB applies the concept of the ant colony algorithm to improve the court sentences retrieval process. We conclude that the effective ontology should be weight-embedded. The empirical results demonstrate that the performance of the ATOB algorithm is better than that of the traditional search method. The performance figures for the ATOB framework measured in terms of precision, recall, F-measure and diversity are 0.90, 0.91, 0.90 and 0.39, respectively.

Learning time-sensitive domain ontology from scientific papers with a hybrid learning method

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“…The development of the Semantic Web makes ontology construction a hot issue. Until now, many researchers have proposed many efficient automatic ontology construction methods [1–43]. According to the concept relation assignment methods used, most of the current automatic ontology construction methods can be classified into three types.…”

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“…The latest semantic and data processing techniques have been applied to the legal ontology domain. The proposed Framework of Automatic Thai Law Ontology can automatically generate a seed ontology and extend it using an ant colony algorithm (Boonchom and Soonthornphisaj, 2012). The online legal information retrieval system eMediation addresses the problem of matching the plaintiff's case description with court decisions (Jelali et al, 2015).…”

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