Bricking Semantic Wikipedia by relation population and predicate suggestion

  • Authors:

  • Haofen Wang;Linyun Fu;Yong Yu

  • Affiliations:

  • Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. E-mail: {whfcarter,fulinyun,yyu}@apex.sjtu.edu.cn;Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. E-mail: {whfcarter,fulinyun,yyu}@apex.sjtu.edu.cn;Dept. of Computer Science & Engineering, Shanghai Jiao Tong University, 200240 Shanghai, China. E-mail: {whfcarter,fulinyun,yyu}@apex.sjtu.edu.cn

  • Venue:
  • Web Intelligence and Agent Systems
  • Year:
  • 2012

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Abstract

Semantic Wikipedia aims to enhance Wikipedia by adding explicit semantics to links between Wikipedia entities. However, we have observed that it currently suffers the following limitations: lack of semantic annotations and lack of semantic annotators. In this paper, we resort to relation population to automatically extract relations between any entity pair to enrich semantic data, and predicate suggestion to recommend proper relation labels to facilitate semantic annotating. Both tasks leverage relation classification which tries to classify extracted relation instances into predefined relations. However, due to the lack of labeled data and the excessiveness of noise in Semantic Wikipedia, existing approaches cannot be directly applied to these tasks to obtain high-quality annotations. In this paper, to tackle the above problems brought by Semantic Wikipedia, we use a label propagation algorithm and exploit semantic features like domain and range constraints on categories as well as linguistic features such as dependency trees of context sentences in Wikipedia articles. The experimental results on 7 typical relation types show the effectiveness and efficiency of our approach in dealing with both tasks.