GAEAT: Graph Auto-Encoder Attention Networks for Knowledge Graph Completion

Yanfei Han, Quan Fang, Jun Hu, Shengsheng Qian, Changsheng Xu

Abstract: Knowledge graph embedding (KGE) encodes components of a KG including entities and relations into continuous low vector space. Most existing methods focus on treating entities and relations in triples independently and thus failing to capture the complex and hidden information that is inherently implicit inside the local neighborhood surrounding a triple. In this paper, we present a new approach for knowledge graph completion called GAEAT (Graph Auto-encoder Attention Network Embedding), which can encapsulate both entity and relation features. Specifically, we construct a triple-level auto-encoder by extending graph attention mechanisms to obtain latent representations of entities and relations simultaneously. To justify our proposed model, we evaluate GAEAT on two real-world datasets. The experimental results demonstrate that GAEAT can outperform state-of-the-art KGE models in knowledge graph completion task, which validates the effectiveness of GAEAT. The source code of this paper can be obtained from https://github.com/TomersHan/GAEAT.