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Relevance-Based Entity Embedding

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Database Systems for Advanced Applications (DASFAA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11448))

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Abstract

Entity embedding plays an indispensable role in many entity-related problems. Currently, mainstream entity embedding methods build on the notion that entities with similar contexts or close proximity should be placed adjacently in the embedding space. Nonetheless, this goal fails to meet the objectives of many downstream tasks, where the relevance among entities is more significant. To fill this gap, in this paper, a novel relevance-based entity embedding approach, Lead, is proposed, where the relevance is captured via query-document information. The experimental results verify the superiority of our proposal.

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Notes

  1. 1.

    https://www.dbpedia-spotlight.org/.

  2. 2.

    We used Wikipedia dump on 20-Mar-2018.

  3. 3.

    http://lucene.apache.org/.

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Acknowledgements

This work was partially supported by NSFC under grants Nos. 61872446, 61876193 and 71690233, and NSF of Hunan province under grant No. 2019JJ20024.

Author information

Authors and Affiliations

  1. Key Laboratory of Science and Technology on Information System Engineering, National University of Defense Technology, Changsha, China

    Weixin Zeng, Xiang Zhao, Jiuyang Tang & Jinzhi Liao

  2. School of Data and Computer Science, Sun Yat-sen University, Guangzhou, China

    Chang-Dong Wang

  3. Collaborative Innovation Center of Geospatial Technology, Wuhan, China

    Xiang Zhao & Jiuyang Tang

Authors
  1. Weixin Zeng
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  2. Xiang Zhao
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  3. Jiuyang Tang
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  4. Jinzhi Liao
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  5. Chang-Dong Wang
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Corresponding author

Correspondence to Xiang Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Guoliang Li

  2. Duke University, Durham, NC, USA

    Jun Yang

  3. University of Porto, Porto, Portugal

    Joao Gama

  4. Chiang Mai University, Chiang Mai, Thailand

    Juggapong Natwichai

  5. Beihang University, Beijing, China

    Yongxin Tong

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Zeng, W., Zhao, X., Tang, J., Liao, J., Wang, CD. (2019). Relevance-Based Entity Embedding. In: Li, G., Yang, J., Gama, J., Natwichai, J., Tong, Y. (eds) Database Systems for Advanced Applications. DASFAA 2019. Lecture Notes in Computer Science(), vol 11448. Springer, Cham. https://doi.org/10.1007/978-3-030-18590-9_33

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  • DOI: https://doi.org/10.1007/978-3-030-18590-9_33

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-18589-3

  • Online ISBN: 978-3-030-18590-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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