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Matthew E. Taylor, Cynthia Matuszek, Bryan Klimt, and Michael Witbrock.
Autonomous Classification of Knowledge into an Ontology. In Proceedings of the Twentieth International FLAIRS Conference
(FLAIRS), May 2007. 52% acceptance rate
FLAIRS-2007
[PDF]107.8kB [postscript]522.6kB
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.
@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="Proceedings of the Twentieth International FLAIRS Conference ({FLAIRS})",
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.",
note = {52% acceptance rate},
wwwnote={<a href="http://www.cise.ufl.edu/~ddd/FLAIRS/flairs2007/">FLAIRS-2007</a>},
}
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