Knowledge graph embedding with shared latent semantic units

Zhao Zhang, Fuzhen Zhuang, Meng Qu, Zheng Yu Niu, Hui Xiong, Qing He

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


Knowledge graph embedding (KGE) aims to project both entities and relations into a continuous low-dimensional space. However, for a given knowledge graph (KG), only a small number of entities and relations occur many times, while the vast majority of entities and relations occur less frequently. This data sparsity problem has largely been ignored by most of the existing KGE models. To this end, in this paper, we propose a general technique to enable knowledge transfer among semantically similar entities or relations. Specifically, we define latent semantic units (LSUs), which are the sub-components of entity and relation embeddings. Semantically similar entities or relations are supposed to share the same LSUs, and thus knowledge can be transferred among entities or relations. Finally, extensive experiments show that the proposed technique is able to enhance existing KGE models and can provide better representations of KGs.

Original languageEnglish (US)
Pages (from-to)140-148
Number of pages9
JournalNeural Networks
StatePublished - Jul 2021

All Science Journal Classification (ASJC) codes

  • Cognitive Neuroscience
  • Artificial Intelligence


  • Embedding
  • Knowledge graph
  • Reinforcement learning


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