Customized Ontology model for Web Information Gathering using Clustering

Abstract

The explosion of data leads to the problem on how information should be retrieved accurately and effectively. To address this issue, ontology's are widely used to represent user profiles in personalized web information gathering. As a model for knowledge description and formalization, ontology's are widely used to represent user profiles in personalized web information gathering. When representing user profiles, many models have utilized only knowledge from either a global knowledge base or local knowledge base. Ontology model learns user profiles from both a world knowledge base and local knowledge base. A non-content based customized ontology model is proposed for knowledge representation and reasoning over user profiles. This model generates user Local Instance Repository which includes non-content based descriptors referring to the subjects. This model has improvement over former models in hit/miss ratio, precision and recall parameters.