Machine Reading: from Wikipedia to the Web
Authors | Daniel S. Weld Fei Wu |
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Publication date | 2010 |
Links | Original |
Machine Reading: from Wikipedia to the Web - scientific work related to Wikipedia quality published in 2010, written by Daniel S. Weld and Fei Wu.
Overview
Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-egg problem, which can be best solved via machine reading — automatically extracting information from natural-language texts to make them accessible to software agents. Authors argue bootstrapping is the best way to build such a system. Authors choose Wikipedia as an initial data source, because it is comprehensive, high-quality, and contains enough collaboratively-created structure to launch a self-supervised bootstrapping process. Authors have developed three systems that realize vision: • KYLIN, which applies Wikipedia heuristic of matching sentences with infoboxes to create training examples for learning relation-specific extractors. • KOG, which automatically generates Wikipedia Infobox Ontology by integrating evidence from heterogeneous resources via joint inference using Markov Logic Networks. • WOE, which uses Wikipedia heuristic to create matching sentence set as done in KYLIN, but it abstracts these examples to relation-independent training data to learn an unlexicalized open extractor.
Embed
Wikipedia Quality
Weld, Daniel S.; Wu, Fei. (2010). "[[Machine Reading: from Wikipedia to the Web]]". University of Washington.
English Wikipedia
{{cite journal |last1=Weld |first1=Daniel S. |last2=Wu |first2=Fei |title=Machine Reading: from Wikipedia to the Web |date=2010 |url=https://wikipediaquality.com/wiki/Machine_Reading:_from_Wikipedia_to_the_Web |journal=University of Washington}}
HTML
Weld, Daniel S.; Wu, Fei. (2010). "<a href="https://wikipediaquality.com/wiki/Machine_Reading:_from_Wikipedia_to_the_Web">Machine Reading: from Wikipedia to the Web</a>". University of Washington.