GCE: Global Contextual Information for Knowledge Graph Embedding

Most existing large-scale knowledge graphs are suffering from incompleteness, and many research efforts have been devoted to the task of knowledge graph completion. One popular approach is to learn low-dimensional representations for all entities and relations, and then employ them to infer new facts. However, we find that most of the current knowledge graph embedding models are lack of suitable strategy to utilize global contextual information. In this paper, we propose an embedding model, named GCE, to explore the capability of global contextual information to the task of knowledge graph completion. In GCE, we carefully design a global contextual information module with the attention mechanism. This module could aggregate global contextual information adaptively, thus enhancing feature representation for knowledge graph completion. To demonstrate the effectiveness of our proposed GCE, we conduct extensive experiments on two benchmark datasets FB15k-237 and WN18RR. Experimental results show that GCE achieves competitive results compared with the existing state-of-the-art embedding models on both datasets. The results validate our central hypothesis – that global contextual information is beneficial to knowledge graph completion performance.