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Regular Paper

Enriching Context Information for Entity Linking with Web Data

Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University Suzhou 215006, China
iFLYTEK Research, Suzhou 215000, China
State Key Laboratory of Cognitive Intelligence, iFLYTEK, Hefei 230000, China
Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology Jeddah 23955, Saudi Arabia
Anhui Toycloud Technology Co.,Ltd., Hefei 230000, China

A preliminary version of the paper was published in the Proceedings of WISE 2019.

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Abstract

Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F1 score of entity linking.

Keywords

entity linkingweb search engine (WSE)attention mechanismabstract extraction

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Journal of Computer Science and Technology
Volume 35 Issue 4,
July 2020
Pages 724-738
DOI: 10.1007/s11390-020-0280-1
Cite this article:
Wang Y-T, Shen J, Li Z-X, et al. Enriching Context Information for Entity Linking with Web Data. Journal of Computer Science and Technology, 2020, 35(4): 724-738. https://doi.org/10.1007/s11390-020-0280-1

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Received: 08 February 2020
Revised: 20 May 2020
Published: 27 July 2020
©Institute of Computing Technology, Chinese Academy of Sciences 2020
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