Relation Extraction from Wikipedia Using Subtree Mining. Nguyen, D., P., T., Matsuo, Y., & Ishizuka, M. In Proceedings of the 22nd national conference on Artificial intelligence, volume 22, pages 1414-1420, 2007. Association for the Advancement of Artificial Intelligence.
Relation Extraction from Wikipedia Using Subtree Mining [pdf]Paper  Relation Extraction from Wikipedia Using Subtree Mining [pdf]Website  abstract   bibtex   
The exponential growth and reliability of Wikipedia have made it a promising data source for intelligent systems. The first challenge of Wikipedia is to make the encyclopedia machine-processable. In this study, we address the problem of extracting relations among entities from Wikipedia’s En- glish articles, which in turn can serve for intelligent systems to satisfy users’ information needs. Our proposed method first anchors the appearance of entities in Wikipedia articles using some heuristic rules that are supported by their encyclo- pedic style. Therefore, it uses neither the Named Entity Rec- ognizer (NER) nor the Coreference Resolution tool, which are sources of errors for relation extraction. It then classifies the relationships among entity pairs using SVM with features extracted from the web structure and subtrees mined from the syntactic structure of text. The innovations behind our work are the following: a) our method makes use of Wikipedia characteristics for entity allocation and entity classification, which are essential for relation extraction; b) our algorithm extracts a core tree, which accurately reflects a relationship between a given entity pair, and subsequently identifies key features with respect to the relationship from the core tree. We demonstrate the effectiveness of our approach through evaluation of manually annotated data from actual Wikipedia articles.

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