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Summary: Autonomously Semantifying Wikipedia
Fei Wu Daniel S. Weld
Computer Science & Engineering Department,
University of Washington, Seattle, WA, USA
{wufei,weld}@cs.washington.edu
ABSTRACT
Berners-Lee's compelling vision of a Semantic Web is hindered by
a chicken-and-egg problem, which can be best solved by a boot-
strapping method -- creating enough structured data to motivate
the development of applications. This paper argues that autonomously
"Semantifying Wikipedia" is the best way to solve the problem. We
choose Wikipedia as an initial data source, because it is compre-
hensive, not too large, high-quality, and contains enough manually-
derived structure to bootstrap an autonomous, self-supervised pro-
cess. We identify several types of structures which can be auto-
matically enhanced in Wikipedia (e.g., link structure, taxonomic
data, infoboxes, etc.), and we describe a prototype implementation
of a self-supervised, machine learning system which realizes our
vision. Preliminary experiments demonstrate the high precision of
our system's extracted data -- in one case equaling that of humans.
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