Knowledge Graph Completion with Adaptive Sparse Transfer Matrix

Authors

  • Guoliang Ji Institute of Automation, Chinese Academy of Sciences
  • Kang Liu Institute of Automation, Chinese Academy of Sciences
  • Shizhu He Institute of Automation, Chinese Academy of Sciences
  • Jun Zhao Institute of Automation, Chinese Academy of Sciences

DOI:

https://doi.org/10.1609/aaai.v30i1.10089

Abstract

We model knowledge graphs for their completion by encoding each entity and relation into a numerical space. All previous work including Trans(E, H, R, and D) ignore the heterogeneity (some relations link many entity pairs and others do not) and the imbalance (the number of head entities and that of tail entities in a relation could be different) of knowledge graphs. In this paper, we propose a novel approach TranSparse to deal with the two issues. In TranSparse, transfer matrices are replaced by adaptive sparse matrices, whose sparse degrees are determined by the number of entities (or entity pairs) linked by relations. In experiments, we design structured and unstructured sparse patterns for transfer matrices and analyze their advantages and disadvantages. We evaluate our approach on triplet classification and link prediction tasks. Experimental results show that TranSparse outperforms Trans(E, H, R, and D) significantly, and achieves state-of-the-art performance.

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Published

2016-02-21

How to Cite

Ji, G., Liu, K., He, S., & Zhao, J. (2016). Knowledge Graph Completion with Adaptive Sparse Transfer Matrix. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1). https://doi.org/10.1609/aaai.v30i1.10089

Issue

Section

Technical Papers: Knowledge Representation and Reasoning