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Relation and Fact Type Supervised Knowledge Graph Embedding via Weighted Scores

  • Bo Zhou
  • Yubo Chen
  • Kang LiuEmail author
  • Jun Zhao
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11856)

Abstract

Knowledge graph embedding aims at learning low-dimensional representations for entities and relations in knowledge graph. Previous knowledge graph embedding methods use just one score to measure the plausibility of a fact, which can’t fully utilize the latent semantics of entities and relations. Meanwhile, they ignore the type of relations in knowledge graph and don’t use fact type explicitly. We instead propose a model to fuse different scores of a fact and utilize relation and fact type information to supervise the training process. Specifically, scores by inner product of a fact and scores by neural network are fused with different weights to measure the plausibility of a fact. For each fact, besides modeling the plausibility, the model learns to classify different relations and differentiate positive facts from negative ones which can be seen as a muti-task method. Experiments show that our model achieves better link prediction performance than multiple strong baselines on two benchmark datasets WN18 and FB15k.

Keywords

Knowledge graph embedding Relation supervised Fact type supervised Weighted scores 

Notes

Acknowledgments

This work is supported by the National Natural Science Foundation of China (No. 61533018), the Natural Key R&D Program of China (No. 2017YFB1002101), the National Natural Science Foundation of China (No. 61806201, No. 61702512) and the independent research project of National Laboratory of Pattern Recognition. This work was also supported by CCF-Tencent Open Research Fund.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina
  2. 2.University of Chinese Academy of SciencesBeijingChina

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