Using recursive ART network to construction domain ontology based on term frequency and inverse document frequency

Ontology describes data about data and offers a group of glossaries with a definition that encompasses them in their entire. It not only transfers syntax of words but also accurately transfers semantic data between human users and the network. Hence, the usefulness of the semantic web depends on whether the domain ontology can be constructed effectively and correctly. In this paper we propose an automated method to construct the domain ontology. First, we collected domain-related web pages from the Internet. Secondly, we use the HTML tag labels to choose meaningful terms from the web pages. Next, we use these terms to construct the domain ontology by calculating a TF–IDF to find the weight of terms, using a recursive ART network (Adaptive Resonance Theory Network) to cluster terms. Each group of terms will find a candidate keyword for ontology construction. Boolean operations locate individual keywords in a hierarchy. Finally, the system outputs an ontology in a Jena package using an RDF format. The primary experiment indicates that our method is useful for domain ontology creation.