Michelle Cheatham (Committee Member), John Gallagher (Committee Member), Pascal Hitzler (Advisor)
Master of Science (MS)
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 traffi?c in Dublin) and show considerable improvement over previous efforts.
Department or Program
Department of Computer Science and Engineering
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