Node Co occurrence based Dual Quaternion Graph Neural Networks for Knowledge Graph Link Prediction
We introduce a novel embedding model, named NoGE, which aims to integrate cooccurrence among entities and relations into graph neural networks to improve knowledge graph completion (i.e. link prediction) Given a knowledge graph, NoGE constructs a single graph considering entities and relations as individual nodes. NoGE then computes weights for edges among nodes based on the co occurrence of entities and relations. Next, NoGE proposes Dual Quaternion Graph Neural Networks (Dual QGNN) and utilizes DualQGNN to update vector representations for entity and relation nodes. NoGE then adopts a score function to produce the triple scores. Comprehensive experimental results show that NoGE obtains state of the art results on three new and difficult benchmark datasets CoDEx for knowledge graph completion.