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Path-Based Knowledge Graph Completion Combining Reinforcement Learning with Soft Rules

  • Wenting Yu
  • Xiangnan Ma
  • Luyi BaiEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1075)

Abstract

Knowledge graphs are useful resources for numerous applications, but they are often greatly incomplete. A popular method of knowledge graph completion is path-based reasoning that is to infer new relations by connecting other existing paths at a pair of entities. But this traditional method does not consider the reality of paths. In this paper, we propose a model that combines the reinforcement learning (RL) framework with soft rules to learn reasoning path. In our model, we adjust the partially observed Markov decision process and extract the soft rules with different confidence levels from datasets. In contrast to prior work, we modify the reward function to make RL-agent incline to choose the paths which conform to soft rules, and set the probability of reasoning paths. Meanwhile, we make some restrictions in order to extract soft rules with high confidence levels. We analyze the complexity of our algorithm and use an instance to evaluate the correctness of our model.

Keywords

Knowledge graph completion Reinforcement learning Soft rules 

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (61402087), the Natural Science Foundation of Hebei Province (F2019501030), the Natural Science Foundation of Liaoning Province (2019-MS-130), and the Fundamental Research Funds for the Central Universities (N172304026).

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.School of Computer and Communication EngineeringNortheastern University (Qinhuangdao)QinhuangdaoChina

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