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OWL query answering using machine learning

Huster, Todd
http://orcid.org/0000-0003-4553-7904
http://rave.ohiolink.edu/etdc/view?acc_num=wright1446117806

Abstract Details

2015, Master of Science (MS), Wright State University, Computer Science.
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 classi fiers 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.
Pascal Hitzler, Ph.D. (Advisor)
Michelle Cheatham, Ph.D. (Committee Member)
John Gallagher, Ph.D. (Committee Member)
33 p.
Artificial Intelligence; Computer Science
approximate reasoning; OWL query answering; Semantic Web; SPARQL; ontology; description logic

Recommended Citations

Citations

  • Huster, T. (2015). OWL query answering using machine learning [Master's thesis, Wright State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=wright1446117806

    APA Style (7th edition)

  • Huster, Todd. OWL query answering using machine learning. 2015. Wright State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=wright1446117806.

    MLA Style (8th edition)

  • Huster, Todd. "OWL query answering using machine learning." Master's thesis, Wright State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=wright1446117806

    Chicago Manual of Style (17th edition)

wright1446117806
743
© 2015, some rights reserved.Creative Commons License OWL query answering using machine learning by Todd Huster is licensed under a Creative Commons Attribution-NonCommercial-NoDerivs 3.0 Unported License. Based on a work at etd.ohiolink.edu.
This open access ETD is published by Wright State University and OhioLINK.