Ontology learning from domain specific web documents

Ontologies play a vital role in many Web and internet related applications. However the demand on ontologies does not always meet with their availability and the manual construction of a high quality ontology is an expensive and time-consuming process. 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 first approach utilizes the structure of phrases appearing in HTML headings while the second uses the hierarchical structure of the HTML headings for identifying new concepts and their taxonomical relationship between seed concepts and between each other. The presented system has been used to build an ontology in the agricultural domain using a set of Arabic extension documents. The resulting ontology was evaluated against a modified version of the AGROVOC ontology..