A Domain Ontology Learning from Web Documents

Resources like ontologies are used in a number of applications, including natural language processing, information retrieval(especially from the Internet). Different methodologies have been proposed to build such resources. This paper proposes a methodology to extract information from the Web to built a taxonomy of terms and Web resources for a given domain. Firstly, a (CHIR) method is used to identify candidates terms. Then a similarity measure is introduced to select relevant concepts to build the ontology. The suite of resulting programs, called (CHIRSIM), is easy to implement and can be efficiently integrated into an information retrieval system to help improve the retrieval performance. Experimental results show that the proposed approach can effectively construct a cancer domain ontology from unstructured text documents.