Knowledge extraction for semantic web using web mining

Abstract

Semantic web, the future of all web technologies has its roots on ontologies. At present most of the ontologies are manually constructed, which is a time consuming, tedious task where significant domain knowledge is required. The manual nature of ontology development has given rise to the well known knowledge engineering bottleneck which hinders the rapid growth of semantic web. This paper investigates the problem of extracting knowledge from large number of web documents in order to develop ontologies. This research introduces web usage patterns as a novel source of semantics in ontology learning. The proposed methodology combines web content mining with web usage mining in the knowledge extraction process. Therefore, both the web user's and web author's perspectives are captured with respect to the web content, which ultimately leads to extraction of more realistic set of conceptual relationships. The evaluation results prove the effectiveness of the proposed methodology. This solution is intended to be usable for transformation of large web corpuses to semantic web and also it could be used to develop cross domain ontologies.