A Graph Neural Network for Ship Link Prediction Based on Graph Attention Mechanism and Quaternion Embedding

In recent years, research on knowledge graphs has exploded due to their capability of effective organization and representation for massive heterogeneous data. However, existing knowledge graphs are often incomplete and contain incorrect triples, which easily incurs a negative impact on the performance of downstream tasks. Besides, few works have been done on the construction of ship knowledge graph. For the former, the mainstream solution is link prediction, also known as knowledge graph completion. To this end, we propose a novel method of knowledge graph embeddings for link prediction, called QuatGAT, combining graph attention mechanism with quaternion embeddings. Specifically, multi-head attention mechanism is employed firstly to obtain entity embeddings by capturing features of both entities and relations of the neighborhood. Then, to represent relations more sufficiently, we use quaternion embeddings to explicitly represent relations as well as entities. Experimental results on benchmark datasets FB15k and FB15k-237 demonstrate the superiority of QuatGAT over existing state-of-the-art methods. Moreover, in terms of ship knowledge graph construction, we also build a multimodal ship knowledge graph named MSKG. Likewise, experimental results on this dataset verify the effectiveness of QuatGAT.