Li, Juanzi

Li, Zhixing

Pan, Jeff Z.

Tang, Jie

Wang, Zhigang

English

Wikipedia infoboxes are a valuable source of structured knowledge for global knowledge sharing. However, infobox information is very incomplete and imbalanced among the Wikipedias in different languages. It is a promising but challenging problem to utilize the rich structured knowledge from a source language Wikipedia to help complete the missing infoboxes for a target language. In this paper, we formulate the problem of cross-lingual knowledge extraction from multilingual Wikipedia sources, and present a novel framework, called WikiCiKE, to solve this problem. An instance based transfer learning method is utilized to overcome the problems of topic drift and translation errors. Our experimental results demonstrate that WikiCiKE outperforms the monolingual knowledge extraction method and the translation-based method

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Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia

https://www.research.ed.ac.uk/files/259789182/Transfer_Learning_WANG_DOA07042013_VOR_CC_BY.pdf

Transfer Learning Based Cross-lingual Knowledge Extraction for Wikipedia

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