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Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence

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Big Data and Social Computing (BDSC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1640))

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Abstract

Most of the existing knowledge graphs are not usually complete and can be complemented by some reasoning algorithms. The reasoning method based on path features is widely used in the field of knowledge graph reasoning and completion on account of that its have strong interpretability. However, reasoning methods based on path features still have several problems in the following aspects: Path search is inefficient, insufficient paths for sparse tasks and some paths are not helpful for reasoning tasks. In order to solve the above problems, this paper proposes a method called DC-Path that combines dynamic relation confidence and other indicators to evaluate path features, and then guide path search, finally conduct relation reasoning. Experimental result show that compared with the existing relation reasoning algorithm, this method can select the most representative features in the current reasoning task from the knowledge graph and achieve better performance on the current relation reasoning task.

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Notes

  1. 1.

    https://github.com/thunlp/Fast-TransX/.

  2. 2.

    https://github.com/thunlp/OpenKE/.

  3. 3.

    https://github.com/xwhan/DeepPath/.

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Acknowledgment

This work was supported by the National Natural Science Foundation of China (Grant No. 62103374),Basic Public Welfare Research Project of Zhejiang Province(Grant No. LGF20F020016) and Open Project of the Key Laboratory of Public Security Informatization Application Based on Big Data Architecture(Grant No. 2020DSJSYS003).

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Authors and Affiliations

  1. Institute of Cyberspace Security, Zhejiang University of Technology, Hangzhou, 310023, China

    Shanqing Yu, Yijun Wu, Ran Gan, Jiajun Zhou & Qi Xuan

  2. College of Information Engineering, Zhejiang University of Technology, Hangzhou, 310023, China

    Shanqing Yu, Yijun Wu, Ran Gan, Jiajun Zhou & Qi Xuan

  3. Zhejiang Police College, Hangzhou, 310053, China

    Ziwan Zheng

Authors
  1. Shanqing Yu
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  2. Yijun Wu
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  3. Ran Gan
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  4. Jiajun Zhou
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  5. Ziwan Zheng
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  6. Qi Xuan
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Corresponding author

Correspondence to Shanqing Yu .

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Editors and Affiliations

  1. Renmin University of China, Beijing, China

    Xiaofeng Meng

  2. Zhejiang University of Technology, Hangzhou, China

    Qi Xuan

  3. Zhejiang University, Hangzhou, China

    Yang Yang

  4. Shenzhen University, Shenzhen, China

    Yang Yue

  5. Hangzhou Normal University, Hangzhou, China

    Zi-Ke Zhang

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Yu, S., Wu, Y., Gan, R., Zhou, J., Zheng, Z., Xuan, Q. (2022). Discover Important Paths in the Knowledge Graph Based on Dynamic Relation Confidence. In: Meng, X., Xuan, Q., Yang, Y., Yue, Y., Zhang, ZK. (eds) Big Data and Social Computing. BDSC 2022. Communications in Computer and Information Science, vol 1640. Springer, Singapore. https://doi.org/10.1007/978-981-19-7532-5_22

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  • DOI: https://doi.org/10.1007/978-981-19-7532-5_22

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  • Online ISBN: 978-981-19-7532-5

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