Publication Date
2015
Document Type
Thesis
Committee Members
Michelle Cheatham (Committee Member), John Gallagher (Committee Member), Pascal Hitzler (Advisor)
Degree Name
Master of Science (MS)
Abstract
The formal semantics of the Web Ontology Language (OWL) enables automated reasoning over OWL knowledge bases, which in turn can be used for a variety of purposes including knowledge base development, querying and management. Automated reasoning is usually done by means of deductive (proof-theoretic) algorithms which are either provably sound and complete or employ approximate methods to trade some correctness for improved efficiency. As has been argued elsewhere, however, reasoning methods for the Semantic Web do not necessarily have to be based on deductive methods, and approximate reasoning using statistical or machine-learning approaches may bring improved speed while maintaining high precision and recall, and which furthermore may be more robust towards errors in the knowledge base and logical inconsistencies. In this thesis, we show that it is possible to learn a linear-time classi?fier that closely approximates deductive OWL reasoning in some settings. In particular, we specify a method for extracting feature vectors from OWL ontologies that enables the ID3 and AdaBoost classifiers to approximate OWL query answering for single answer variable queries. Amongst other ontologies, we evaluate our approach using the LUBM benchmark and the DCC ontology (a large real-world dataset about traffic in Dublin and show considerable improvement over previous efforts.
Page Count
33
Department or Program
Department of Computer Science and Engineering
Year Degree Awarded
2015
Copyright
Copyright 2015, some rights reserved. My ETD may be copied and distributed only for non-commercial purposes and may not be modified.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.
