Ontologies are an increasingly important tool in kn owledge representation, as they allow large amounts of data to be related in a logical fashion. Current research is c oncentrated on automatically constructing ontologies, merging ontologies with different structures, and optimal mechanisms for ontology building; in this work we c onsider the related, but distinct, problem of how to automa tically determine where to place new knowledge into an existing ontology. Rather than relying on human knowledge engineers to carefully classify knowledge, it is be coming increasingly important for machine learning techniq ues to automate such a task. Automation is particularly im portant as the rate of ontology building via automatic know ledge acquisition techniques increases. This paper compar es three well-established machine learning techniques and sh ows that they can be applied successfully to this knowl edge placement task. Our methods are fully implemented and tested in the Cyc knowledge base system. 1
['Matthew E. Taylor', 'Cynthia Matuszek', 'Bryan Klimt', 'Michael J. Witbrock']
Autonomous Classification of Knowledge into an Ontology