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Communication Dans Un Congrès Année : 2017

Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts

Résumé

The objective of this paper is to present the role of Ontology Learning Process in supporting an ontology engineer for creating and maintaining ontologies from textual resources. The knowledge structures that interest us are legal domain-specific ontologies. We will use these ontologies to build legal domain ontology for a Lebanese legal knowledge based system. The domain application of this work is the Lebanese criminal system. Ontologies can be learnt from various sources, such as databases, structured and unstructured documents. Here, the focus is on the acquisition of ontologies from unstructured text, provided as input. In this work, the Ontology Learning Process represents a knowledge extraction phase using Natural Language Processing techniques. The resulted ontology is considered as inexpressive ontology. There is a need to reengineer it in order to build a complete, correct and more expressive domain-specific ontology.

Dates et versions

hal-01644017 , version 1 (26-04-2019)

Identifiants

Citer

Mirna El Ghosh, Hala Naja, Habib Abdulrab, Mohamad Khalil. Ontology Learning Process as a Bottom-up Strategy for Building Domain-specific Ontology from Legal Texts. 9th International Conference on Agents and Artificial Intelligence (ICAART 2017), Feb 2017, Porto, Portugal. pp.473-480, ⟨10.5220/0006188004730480⟩. ⟨hal-01644017⟩
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