Enhancing ontology learning with machine learning and natural language processing techniques

Authors
Liu, Yue
ORCID
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Other Contributors
McGuinness, Deborah L.
Hendler, James A.
Ji, Heng
Hahn, Juergen
Issue Date
2019-08
Keywords
Computer science
Degree
PhD
Terms of Use
This electronic version is a licensed copy owned by Rensselaer Polytechnic Institute, Troy, NY. Copyright of original work retained by author.
Full Citation
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.
Description
August 2019
School of Science
Department
Dept. of Computer Science
Publisher
Rensselaer Polytechnic Institute, Troy, NY
Relationships
Rensselaer Theses and Dissertations Online Collection
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