Autonomously Semantifying Wikipedia
Authors | Feifei Wu Daniel S. Weld |
|---|---|
Publication date | 2007 |
ISBN | 978-159593803-9 |
DOI | 10.1145/1321440.1321449 |
Links |
Autonomously Semantifying Wikipedia - scientific work about Wikipedia quality published in 2007, written by Feifei Wu and Daniel S. Weld.
Overview
Berners-Lee's compelling vision of a Semantic Web is hindered by a chicken-and-egg problem, which can be best solved by a bootstrapping 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. Authors choose Wikipedia as an initial data source, because it is comprehensive, not too large, high-quality, and contains enough manually- derived structure to bootstrap an autonomous, self-supervised process. Authors identify several types of structures which can be automatically enhanced in Wikipedia (e.g., link structure, taxonomic data, infoboxes, etc.), and authors describe a prototype implementation of a self-supervised, machine learning system which realizes their vision. Preliminary experiments demonstrate the high precision of their system's extracted data - in one case equaling that of humans.
Embed
Wikipedia Quality
Wu, Feifei; Weld, Daniel S.. (2007). "[[Autonomously Semantifying Wikipedia]]". International Conference on Information and Knowledge Management, Proceedings 2007, pp. 41-50. ISBN: 978-159593803-9. DOI: 10.1145/1321440.1321449.
English Wikipedia
{{cite journal |last1=Wu |first1=Feifei |last2=Weld |first2=Daniel S. |title=Autonomously Semantifying Wikipedia |date=2007 |isbn=978-159593803-9 |doi=10.1145/1321440.1321449 |url=https://wikipediaquality.com/wiki/Autonomously_Semantifying_Wikipedia |journal=International Conference on Information and Knowledge Management, Proceedings 2007, pp. 41-50}}
HTML
Wu, Feifei; Weld, Daniel S.. (2007). "<a href="https://wikipediaquality.com/wiki/Autonomously_Semantifying_Wikipedia">Autonomously Semantifying Wikipedia</a>". International Conference on Information and Knowledge Management, Proceedings 2007, pp. 41-50. ISBN: 978-159593803-9. DOI: 10.1145/1321440.1321449.
