Authors:
Mirna El Ghosh
1
;
Hala Naja
2
;
Habib Abdulrab
1
and
Mohamad Khalil
3
Affiliations:
1
INSA, France
;
2
Lebanese University and Faculty of Sciences, Lebanon
;
3
Lebanese University and Faculty of Engineering, Lebanon
Keyword(s):
Ontology Learning, Semi-automatic Extraction, Natural Language Processing, Legal Ontologies, Domain-specific Ontologies.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Collaboration and e-Services
;
e-Business
;
Enterprise Engineering
;
Enterprise Information Systems
;
Enterprise Ontologies
;
Formal Methods
;
Knowledge Engineering and Ontology Development
;
Knowledge Representation and Reasoning
;
Knowledge-Based Systems
;
Natural Language Processing
;
Ontologies
;
Pattern Recognition
;
Semantic Web
;
Simulation and Modeling
;
Soft Computing
;
Symbolic Systems
Abstract:
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.