Automatically Refining the Wikipedia Infobox Ontology
Fei Wu
and
Daniel S. Weld
Abstract:
The combined efforts of human volunteers have recently extracted
numerous facts from Wikipedia, storing them as machine-harvestable
object-attribute-value triples in Wikipedia infoboxes. Machine learning
systems, such as Kylin, use these infoboxes as training data,
accurately extracting even more semantic knowledge from natural
language text. But in order to realize the full power of this information,
it must be situated in a cleanly-structured ontology. This paper
introduces KOG, an autonomous system for refining Wikipedia’s
infobox-class ontology towards this end. We cast the problem of
ontology refinement as a machine learning problem and solve it
using both SVMs and a more powerful joint-inference approach
expressed in Markov Logic Networks. We present experiments
demonstrating the superiority of the joint-inference approach and
evaluating other aspects of our system. Using these techniques, we
build a rich ontology, integrating Wikipedia’s infobox-class schemata
with WordNet. We demonstrate how the resulting ontology may be
used to enhance Wikipedia with improved query processing and
other features.
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