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Context-Dependent Representation of Knowledge Graphs

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Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding (CCKS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1134))

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Abstract

Recently, there is a growing interest of leveraging graph’s structural information for knowledge representation. However, they fail to capture global connectivity patterns in knowledge graphs or depict unique structural properties of various graph context. In this paper, we propose a novel representation framework, Context-dependent Representation of Knowledge Graphs (CRKG), to utilize the diversity of graph’s structural information for knowledge representation. We introduce triplet context to effectively capture semantic information from two types of graph structures around a triple. One is K-degree neighborhoods of a source entity in the target triple, which captures global connectivity patterns of entities. The other is multiple relation paths between the entity pair in the target triple, reflecting rich inference patterns between entities. Considering the unique characteristics of two kinds of triplet context, we design distinct embedding strategies to preserve their connectivity pattern diversities. Experimental results on three challenging datasets show that CRKG has significant improvements compared with baselines on link prediction task.

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Authors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Binling Nie & Shouqian Sun

Authors
  1. Binling Nie
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  2. Shouqian Sun
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Corresponding author

Correspondence to Binling Nie .

Editor information

Editors and Affiliations

  1. Department of Computer Science and Technology, Tsinghua University, Beijing, China

    Xiaoyan Zhu

  2. Harbin Institute of Technology, Harbin, China

    Bing Qin

  3. Queen's University, Kingston, Canada

    Xiaodan Zhu

  4. Harbin Institute of Technology, Harbin, China

    Ming Liu

  5. Soochow University, Soochow, China

    Longhua Qian

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Nie, B., Sun, S. (2019). Context-Dependent Representation of Knowledge Graphs. In: Zhu, X., Qin, B., Zhu, X., Liu, M., Qian, L. (eds) Knowledge Graph and Semantic Computing: Knowledge Computing and Language Understanding. CCKS 2019. Communications in Computer and Information Science, vol 1134. Springer, Singapore. https://doi.org/10.1007/978-981-15-1956-7_2

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  • DOI: https://doi.org/10.1007/978-981-15-1956-7_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-1955-0

  • Online ISBN: 978-981-15-1956-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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