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Enriching Context Information for Entity Linking with Web Data

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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.

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Authors and Affiliations

  1. Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Yi-Ting Wang, Jie Shen, Zhi-Xu Li, An Liu, Peng-Peng Zhao, Jia-Jie Xu & Lei Zhao

  2. iFLYTEK Research, Suzhou, 215000, China

    Zhi-Xu Li

  3. State Key Laboratory of Cognitive Intelligence, iFLYTEK, Hefei, 230000, China

    Zhi-Xu Li

  4. Computer, Electrical and Mathematical Sciences and Engineering, King Abdullah University of Science and Technology, Jeddah, 23955, Saudi Arabia

    Qiang Yang

  5. Anhui Toycloud Technology Co., Ltd., Hefei, 230000, China

    Xun-Jie Yang

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  1. Yi-Ting Wang
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  2. Jie Shen
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  3. Zhi-Xu Li
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  5. An Liu
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  7. Jia-Jie Xu
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  8. Lei Zhao
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  9. Xun-Jie Yang
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Correspondence to Zhi-Xu Li.

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Wang, YT., Shen, J., Li, ZX. et al. Enriching Context Information for Entity Linking with Web Data. J. Comput. Sci. Technol. 35, 724–738 (2020). https://doi.org/10.1007/s11390-020-0280-1

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  • DOI: https://doi.org/10.1007/s11390-020-0280-1

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