Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia

Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics, pages 641–650, Sofia, Bulgaria, August 4-9 2013. c⃝2013 Association for Computational Linguistics Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia Zhigang Wang†, Zhixing Li†, Juanzi Li†, Jie Tang†, and Jeff Z. Pan‡ † Tsinghua National Laboratory for Information Science and Technology DCST, Tsinghua University, Beijing, China {wzhigang,zhxli,ljz,tangjie}@keg.cs.tsinghua.edu.cn ‡ Department of Computing Science, University of Aberdeen, Aberdeen, UK jeff.z.pan@abdn.ac.uk Abstract Wikipedia infoboxes are a valuable source of structured knowledge for global knowl- edge sharing. However, infobox infor- mation is very incomplete and imbal- anced among the Wikipedias in differen- t languages. It is a promising but chal- lenging problem to utilize the rich struc- tured knowledge from a source language Wikipedia to help complete the missing in- foboxes for a target language. In this paper, we formulate the prob- lem of cross-lingual knowledge extraction from multilingual Wikipedia sources, and present a novel framework, called Wiki- CiKE, to solve this problem. An instance- based transfer learning method is utilized to overcome the problems of topic drift and translation errors. Our experimen- tal results demonstrate that WikiCiKE out- performs the monolingual knowledge ex- traction method and the translation-based method. 1 Introduction In recent years, the automatic knowledge extrac- tion using Wikipedia has attracted significant re- search interest in research fields, such as the se- mantic web. As a valuable source of structured knowledge, Wikipedia infoboxes have been uti- lized to build linked open data (Suchanek et al., 2007; Bollacker et al., 2008; Bizer et al., 2008; Bizer et al., 2009), support next-generation in- formation retrieval (Hotho et al., 2006), improve question answering (Bouma et al., 2008; Fer- r´andez et al., 2009), and other aspects of data ex- ploitation (McIlraith et al., 2001; Volkel et al., 2006; Hogan et al., 2011) using semantic web s- tandards, such as RDF (Pan and Horrocks, 2007; Heino and Pan, 2012) and OWL (Pan and Hor- rocks, 2006; Pan and Thomas, 2007; Fokoue et al., 2012), and their reasoning services. However, most infoboxes in different Wikipedi- a language versions are missing. Figure 1 shows the statistics of article numbers and infobox infor- mation for six major Wikipedias. Only 32.82% of the articles have infoboxes on average, and the numbers of infoboxes for these Wikipedias vary significantly. For instance, the English Wikipedi- a has 13 times more infoboxes than the Chinese Wikipedia and 3.5 times more infoboxes than the second largest Wikipedia of German language. English German French Dutch Spanish Chinese 0 0.5 1 1.5 2 2.5 3 3.5 4 x 10 6 Languages Number of Instances Article Infobox Figure 1: Statistics for Six Major Wikipedias. To solve this problem, KYLIN has been pro- posed to extract the missing infoboxes from un- structured article texts for the English Wikipedi- a (Wu and Weld, 2007). KYLIN performs well when sufficient training data are available, and such techniques as shrinkage and retraining have been used to increase recall from English Wikipedia’s long tail of sparse infobox classes (Weld et …

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