Language-Independent Type Inference of the Instances from Multilingual Wikipedia

Tianxing Wu, Guilin Qi, Bin Luo, Lei Zhang and Haofen Wang
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Tianxing Wu: School of Computer Science and Engineering, Southeast University, Nanjing, China
Guilin Qi: School of Computer Science and Engineering, Southeast University, Nanjing, China
Bin Luo: School of Computer Science and Engineering, Southeast University, Nanjing, China
Lei Zhang: Institute AIFB, Karlsruhe Institute of Technology, Karlsruhe, Germany
Haofen Wang: Gowild Inc., Shenzhen, China

International Journal on Semantic Web and Information Systems (IJSWIS), 2019, vol. 15, issue 2, 22-46

Abstract: Extracting knowledge from Wikipedia has attracted much attention in recent ten years. One of the most valuable kinds of knowledge is type information, which refers to the axioms stating that an instance is of a certain type. Current approaches for inferring the types of instances from Wikipedia mainly rely on some language-specific rules. Since these rules cannot catch the semantic associations between instances and classes (i.e. candidate types), it may lead to mistakes and omissions in the process of type inference. The authors propose a new approach leveraging attributes to perform language-independent type inference of the instances from Wikipedia. The proposed approach is applied to the whole English and Chinese Wikipedia, which results in the first version of MulType (Multilingual Type Information), a knowledge base describing the types of instances from multilingual Wikipedia. Experimental results show that not only the proposed approach outperforms the state-of-the-art comparison methods, but also MulType contains lots of new and high-quality type information.

Date: 2019
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