Matthew E. Taylor's Publications

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Autonomous Classification of Knowledge into an Ontology

Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, and Michael Witbrock. Autonomous Classification of Knowledge into an Ontology. In The Twentieth International FLAIRS Conference, May 2007.
FLAIRS-2007

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Abstract

Ontologies are an increasingly important tool in knowledge representation, as they allow large amounts of data to be related in a logical fashion. Current research is concentrated on automatically constructing ontologies, merging ontologies with different structures, and optimal mechanisms for ontology building; in this work we consider the related, but distinct, problem of how to automatically determine where to place new knowledge into an existing ontology. Rather than relying on human knowledge engineers to carefully classify knowledge, it is becoming increasingly important for machine learning techniques to automate such a task. Automation is particularly important as the rate of ontology building via automatic knowledge acquisition techniques increases. This paper compares three well-established machine learning techniques and shows that they can be applied successfully to this knowledge placement task. Our methods are fully implemented and tested in the Cyc knowledge base system.

BibTeX Entry

@InProceedings{FLAIRS07-taylor-ontology,
        author="Matthew E.\ Taylor and Cynthia Matuszek and Bryan Klimt and Michael Witbrock",
        title="Autonomous Classification of Knowledge into an Ontology",
        booktitle="The Twentieth International FLAIRS Conference",
        month="May",year="2007", 
        abstract="Ontologies are an increasingly important tool in
        knowledge representation, as they allow large amounts of data
        to be related in a logical fashion. Current research is
        concentrated on automatically constructing ontologies, merging
        ontologies with different structures, and optimal mechanisms
        for ontology building; in this work we consider the related,
        but distinct, problem of how to automatically determine where
        to place new knowledge into an existing ontology. Rather than
        relying on human knowledge engineers to carefully classify
        knowledge, it is becoming increasingly important for machine
        learning techniques to automate such a task. Automation is
        particularly important as the rate of ontology building via
        automatic knowledge acquisition techniques increases. This
        paper compares three well-established machine learning
        techniques and shows that they can be applied successfully to
        this knowledge placement task. Our methods are fully
        implemented and tested in the Cyc knowledge base system.",
	wwwnote={<a href="http://www.cise.ufl.edu/~ddd/FLAIRS/flairs2007/">FLAIRS-2007</a>},
}

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