Enhancing ontology learning with machine learning and natural language processing techniques

Abstract

Ontologies provide terms to describe and represent specific knowledge and are thus widely used in many semantic web applications for knowledge management purposes. Since creating ontologies manually can be extremely labor-intensive and time-consuming, there is an increase in the motivation to automate the process, which includes the automation of ontology components such as classes, relations, attributes, and the overall term coverage and structure. Towards the goal of ontology automation in semantic web, in this thesis, we focus on the portion of ”Ontology Learning” that seeks automatic or semi-automatic approaches for either creating or reusing existing ontology resources with certain task-oriented objectives. We aim to enhance aspects of ontology learning with methods that can be reused in different domains and applied at large scale using machine learning and natural language processing techniques. We consider classes, relations, attributes being three major components of ontology and study three areas of ontology learning with respect to the automation of these components.