Dissertation > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Text Processing

AraOntoLT: A Framework for Ontology Learning from Arabic Text

Author TaiMuEr
Tutor XuDeZhi
School Central South University
Course Computer Science and Technology
Keywords Semantic Web Ontology Ontology Learning Natural Language Processing linguistic patterns
CLC TP391.1
Type Master's thesis
Year 2011
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Ontology learning is one of the essential topics in the scope of an important area of current computer science and artificial intelligence (?) the upcoming Semantic Web. As the Semantic Web idea comprises semantically annotated descendant of the current World Wide Web and related tools and resources, the need of vast and reliable knowledge repositories is obvious. Ontologies present well defined, straightforward and standardized form of these repositories. However, the ontology creation process is very expensive, time-consuming and un-objective when performed manually. Ontology learning is one of the most significant approaches proposed to date for developing ontologies.Many different approaches and methods have been developed to extract ontologies in an automatic or semi-automatic way. By using these approaches several frameworks have been implemented and applied to resources of different languages especially English. A (semi-)automatic ontology learning framework can be used to significantly reduce the amount of time needed to update ontologies.A semi-automatic framework for ontology acquisition from Arabic text is proposed and implemented in this thesis. A hybrid approach combining linguistic and statistical techniques is adopted. After a raw text corpus is linguistically analyzed, it is annotated according to predefined annotation format. Then a set of defined mapping rules are applied to the resulted annotated files which lead to extract ontology candidates. Those candidates can be statistically filtered according to user’s input arguments. The candidates remain after filtering can be edited by the user and then ontology can be extracted from the chosen candidates.

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