A Bottom-Up Approach for Moroccan Legal Ontology Learning from Arabic Texts

Abstract

Ontologies constitute an exciting model for representing a domain of interest, since they enable information-sharing and reuse. Existing inference machines can also use them to reason about various contexts. However, ontology construction is a time-consuming and challenging task. The ontology learning field answers this problem by providing automatic or semi-automatic support to extract knowledge from various sources, such as databases and structured and unstructured documents. This paper reviews the ontology learning process from unstructured text and proposes a bottom-up approach to building legal domain-specific ontology from Arabic texts. In this work, the learning process is based on Natural Language Processing (NLP) techniques and includes three main tasks: corpus study, term acquisition, and conceptualization. Corpus study enriches the original corpus with valuable linguistic information. Term acquisition selects tagged lemmas sequences as potential term candidates, and conceptualization drives concepts and their relationships from the extracted terms. We used the NooJ platform to implement the required linguistic resources for each task. Further, we developed a Java module to enrich the ontology vocabulary from the Arabic WordNet (AWN) project.The obtained results were essential but incomplete. The legal expert revised them manually, and then they were used to refine and expand a domain ontology for a Moroccan Legal Information Retrieval System (LIRS).