Dustin Lange
Felix Naumann
ABSTRACT
Roughly every third Wikipedia article contains an infobox - a table that displays important facts about the subject in attribute-value form. The schema of an infobox, i.e., the attributes that can be expressed for a concept, is defined by an infobox template. Often, authors do not specify all template attributes, resulting in incomplete infoboxes.
With iPopulator, we introduce a system that automatically populates infoboxes of Wikipedia articles by extracting attribute values from the article's text. In contrast to prior work, iPopulator detects and exploits the structure of attribute values to independently extract value parts. We have tested iPopulator on the entire set of infobox templates and provide a detailed analysis of its effectiveness. For instance, we achieve an average extraction precision of 91% for 1,727 distinct infobox template attributes.
- J. D. Lafferty, A. McCallum, and F. C. N. Pereira. Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data. In Proc. of the 18th Intl. Conf. on Machine Learning, pages 282--289, 2001. Google Scholar
Digital Library
- D. Lange, C. Böhm, and F. Naumann. Extracting Structured Information from Wikipedia Articles to Populate Infoboxes. Technical Report 38, Hasso Plattner Institute, Potsdam, 2010. ISBN 978-3-86956-081-6.Google ScholarDigital Library
- N. Okazaki. CRFsuite: a fast implementation of Conditional Random Fields (CRFs), 2007. http://www.chokkan.org/software/crfsuite/.Google Scholar
- S. Sarawagi. Information Extraction. Foundations and Trends in Databases, 1(3), 2008. Google ScholarDigital Library
- F. Wu, R. Hoffmann, and D. S. Weld. Information Extraction from Wikipedia: Moving Down the Long tail. In Proc. of the 14th Intl. Conf. on Knowledge Discovery and Data Mining, pages 731--739, 2008. Google ScholarDigital Library
- F. Wu and D. S. Weld. Autonomously Semantifying Wikipedia. In Proc. of the 16th Conf. on Information and Knowledge Management, pages 41--50, 2007. Google ScholarDigital Library
Index Terms
- Extracting structured information from Wikipedia articles to populate infoboxes
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