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TransO: a knowledge-driven representation learning method with ontology information constraints

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Abstract

Representation learning techniques for knowledge graphs (KGs) are crucial for constructing knowledge-driven decisions in complex network data application scenarios. Most existing methods focus mainly on structured information, ignoring the important value of rich ontology information constraints and complements, however, ontology information is the key for building knowledge-driven decision-making processes. In this paper, we propose a novel ontology information constrained knowledge representation learning model, TransO, which can efficiently model relations explicitly and seamlessly incorporate rich ontology information to improve model performance and maintain low model complexity. Moreover, specific constraint strategies are proposed for entity types, relations, and hierarchical information to effectively implement reasoning and completion of KGs and construct knowledge-driven decisions that are more consistent with the logic of human knowledge in complex network applications. The experimental tasks of link prediction and triple classification are performed on two public datasets. The experimental results demonstrate the effectiveness of our proposed method with better performance than state-of-the-art methods.

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Acknowledgements

This work is supported by the National Key R&D Program of China (2020AAA0108504) and the National Natural Science Foundation of China (61972275).

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Authors and Affiliations

  1. College of Intelligence and Computing, Tianjin University, Tianjin, China

    Zhao Li, Xin Liu, Xin Wang, Pengkai Liu & Yuxin Shen

  2. Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin, China

    Zhao Li, Xin Liu, Xin Wang, Pengkai Liu & Yuxin Shen

  3. Financial Big Data Laboratory, Bank of Shizuishan, Yinchuan, China

    Zhao Li

  4. Tianjin International Engineering Institute, Tianjin University, Tianjin, China

    Xin Liu & Xin Wang

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  1. Zhao Li
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  2. Xin Liu
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  3. Xin Wang
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  5. Yuxin Shen
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Correspondence to Xin Wang.

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The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Zhao Li and Xin Liu contributed equally to this work.

This article belongs to the Topical Collection: Special Issue on Decision Making in Heterogeneous Network Data Scenarios and Applications

Guest Editors: Jianxin Li, Chengfei Liu, Ziyu Guan, and Yinghui Wu

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Li, Z., Liu, X., Wang, X. et al. TransO: a knowledge-driven representation learning method with ontology information constraints. World Wide Web 26, 297–319 (2023). https://doi.org/10.1007/s11280-022-01016-3

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  • DOI: https://doi.org/10.1007/s11280-022-01016-3

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