Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
This work addresses knowledge graph completion, a key problem in AI for tasks like recommendation systems, but it is incremental as it builds on existing graph neural network methods.
The paper tackles knowledge graph link prediction by integrating entity and relation co-occurrence into graph neural networks, resulting in state-of-the-art performance on three CoDEx benchmark datasets.
We introduce a novel embedding model, named NoGE, which aims to integrate co-occurrence 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 (DualQGNN) 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.