Autonomous Classification of Knowledge into an Ontology
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Type of Work6 PAGES
conference papers and proceedings preprints
Citation of Original PublicationMatthew E. Taylor, Cynthia Matuszek, Bryan Klimt, Michael Witbrock, Autonomous Classification of Knowledge into an Ontology, 20th International FLAIRS Conference (FLAIRS), Key West, Florida, May 2007
RightsThis item may be protected under Title 17 of the U.S. Copyright Law. It is made available by UMBC for non-commercial research and education. For permission to publish or reproduce, please contact the author.
Cyc knowledge base (KB)
Support Vector Machines
Interactive Robotics and Language Lab
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.