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Path-Based Learning for Plant Domain Knowledge Graph

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Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence (CCKS 2017)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 784))

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Abstract

Learning to embed the knowledge graph has been a hot topic in research communities. As for that, TransE is a promising method that can achieve state-of-art performance for many of the benchmark tasks. However, none of the previous work considers the knowledge graph in plant domain in which case the properties of the graph are significantly different. For the knowledge graph in plant domain, most of its relations belong to one-to-many, many-to-one or many-to-many types (actually majority of them are attribute-type relations), which are not in the scope of consideration for classical TransE model. In order to deal with such unique challenges, we propose a novel model called PTA (path-based TransE for attributes). It constructs the relation path by combining attributes and hyponymy relations, and embeds them to a lower dimensional space as well. We conduct extensive experiments on link prediction task where the performance is measured by mean rank and Hit@10. The results show that our new model significantly outperforms other competing methods on several different tasks.

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Authors and Affiliations

  1. College of Information and Electrical Engineering, China Agricultural University, Beijing, 100083, China

    Cuicui Dong, Huifang Du, Yaru Du, Ying Chen & Ming Zhao

  2. University of Southern California, Los Angeles, USA

    Wenzhe Li

Authors
  1. Cuicui Dong
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  2. Huifang Du
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  3. Yaru Du
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  4. Ying Chen
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  5. Wenzhe Li
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  6. Ming Zhao
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Corresponding author

Correspondence to Ming Zhao .

Editor information

Editors and Affiliations

  1. Tsinghua University, Beijing, China

    Juanzi Li

  2. Beijing Xigema Center, Beijing, China

    Ming Zhou

  3. School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China

    Guilin Qi

  4. Google, Mountain View, California, USA

    Ni Lao

  5. East China University of Science and Technology, Shanghai, China

    Tong Ruan

  6. Guangdong University of Foreign Studies, Guangzhou, China

    Jianfeng Du

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Dong, C., Du, H., Du, Y., Chen, Y., Li, W., Zhao, M. (2017). Path-Based Learning for Plant Domain Knowledge Graph. In: Li, J., Zhou, M., Qi, G., Lao, N., Ruan, T., Du, J. (eds) Knowledge Graph and Semantic Computing. Language, Knowledge, and Intelligence. CCKS 2017. Communications in Computer and Information Science, vol 784. Springer, Singapore. https://doi.org/10.1007/978-981-10-7359-5_2

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  • DOI: https://doi.org/10.1007/978-981-10-7359-5_2

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7358-8

  • Online ISBN: 978-981-10-7359-5

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