ONTOLOGY MINING FOR PERSONALIZED WEB INFORMATION GATHERING (International Journal of Engineering and Computer Science, 2017)
As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized webinformation gathering. However, when representing user profiles, many models have utilized only knowledge from either a globalknowledge base or user local information. In this paper, a personalized ontology model is proposed for knowledge representationand reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user localinstance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering.User profiles represent the concept models possessed by users when gathering web information. A concept model is implicitlypossessed by users and is generated from their background knowledge. While this concept model cannot be proven in laboratories,many web ontologists have observed it in user behaviourThe results show that this ontology model is successful.