Exploiting Syntactic and Semantic Information for Relation Extraction from Wikipedia
Yutaka Matsuo, Mitsuru Ishizuka · 2006
The exponential growth of Wikipedia recently attracts the attention of a large number of researchers and practitioners. One of the current challenge on Wikipedia is to make the encyclopedia processable for machines. In this paper, we deal with the problem of extracting relations between entities from Wikipedia's En- glish articles, which can straightforwardly be transformed into Semantic Web meta data. We propose a method to exploit syntactic and semantic information for relation extraction. In addition, our method can utilize the nature of Wikipedia to automatically obtain training data. The preliminary results of our experiments strongly support our hyperthesis that using information in higher level of description is better for relation extraction on Wikipedia and show that our method is promising for text understanding.