Towards a system for constructing Arabic Ontology based on natural text

This paper presents ArOntoLearn, a Framework for Arabic Ontology learning from textual resources. Supporting Arabic language and using domain knowledge in the learning process are the main features of our framework. Besides it represents the learned ontology in Probabilistic Ontology Model (POM), which can be translated into any knowledge representation formalism, and implements data-driven change discovery. Therefore it updates the POM according to the corpus changes only, and allows user to trace the evolution of the ontology with respect to the changes in the underlying corpus. Our framework analyses Arabic textual resources, and matches them to Arabic Lexico-syntactic patterns in order to learn new Concepts and Relations. Supporting Arabic language is not that easy task, because current linguistic analysis tools are not efficient enough to process unvocalized Arabic corpuses that rarely contain appropriate punctuation. So we tried to build a flexible and freely configured framework whereas any linguistic analysis tool can be replaced by more sophisticated one whenever it is available.