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DSKG: A Deep Sequential Model for Knowledge Graph Completion

  • Lingbing Guo
  • Qingheng Zhang
  • Weiyi Ge
  • Wei Hu
  • Yuzhong Qu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 957)

Abstract

Knowledge graph (KG) completion aims to fill the missing facts in a KG, where a fact is represented as a triple in the form of (subjectrelationobject). Current KG completion models compel two-thirds of a triple provided (e.g., subject and relation) to predict the remaining one. In this paper, we propose a new model, which uses a KG-specific multi-layer recurrent neutral network (RNN) to model triples in a KG as sequences. It outperformed several state-of-the-art KG completion models on the conventional entity prediction task for many evaluation metrics, based on two benchmark datasets and a more difficult dataset. Furthermore, our model is enabled by the sequential characteristic and thus capable of predicting the whole triples only given one entity. Our experiments demonstrated that our model achieved promising performance on this new triple prediction task.

Keywords

Knowledge graph completion Deep Sequential model Recurrent neutral network 

Notes

Acknowledgements

This work was supported by the National Natural Science Foundation of China (Nos. 61872172 and 61772264).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Lingbing Guo
    • 1
    • 2
  • Qingheng Zhang
    • 1
  • Weiyi Ge
    • 2
  • Wei Hu
    • 1
  • Yuzhong Qu
    • 1
  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina
  2. 2.Science and Technology on Information Systems Engineering LabNanjingChina

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