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 learne
d 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.
Morphological analysis is an important step in natural language processing and its
various applications. Each kind of these applications needs a certain balance between:
performance, accuracy, and generality of solutions (i.e. getting all possible
roots); while
we focus on performance with a good accuracy in Information retrieval applications,
we try to achieve high accuracy in systems like pos-tagger and machine translation, and
both high accuracy and high generality in systems like language learning systems and
Arabic lexical dictionaries. In this paper, we describe our approach to build a flexible
and application oriented Arabic morphological analyzer; this approach is designed to
satisfy various requirements of most applications which need morphological processing.
It also provides a separate stage (Original Letters Detection Algorithm) which can be
plugged easily in any Other morphological analyzer to improve its performance, and
with no negative effect on its reliability.
