Title: Ontology learning from domain specific web documents

Authors: Maryam Hazman, Samhaa R. El-Beltagy, Ahmed Rafea

Addresses: Central Lab for Agricultural Expert Systems, Agricultural Research Center, Ministry of Agriculture and Land Reclamation, El-Nour St. P.O. Box 438 Dokki, Giza, Egypt. ' Faculty of Computers and Information, Computer Science Department, Cairo University, 5 Dr. Ahmed Zewail Street, 12613 Orman, Giza, Egypt. ' Computer Science Department, American University in Cairo, 113 Kasr El Aini St., P.O. Box 2511, 11511 Cairo, Egypt

Abstract: Ontologies play a vital role in many web- and internet-related applications. This work presents a system for accelerating the ontology building process via semi-automatically learning a hierarchal ontology given a set of domain-specific web documents and a set of seed concepts. The methods are tested with web documents in the domain of agriculture. The ontology is constructed through the use of two complementary approaches. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents and evaluated against a modified version of the AGROVOC ontology.

Keywords: ontology learning; taxonomic ontology; AGROVOC; domain-specific documents; web documents; agriculture; Arabic extension documents.

DOI: 10.1504/IJMSO.2009.026251

International Journal of Metadata, Semantics and Ontologies, 2009 Vol.4 No.1/2, pp.24 - 33

Published online: 30 May 2009 *

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