Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction

• URL: http://arxiv.org/abs/2104.07396v1
• Date: Thu, 15 Apr 2021 11:51:52 GMT
• Title: Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
• Authors: Dai Quoc Nguyen and Vinh Tong and Dinh Phung and Dat Quoc Nguyen
• Abstract summary: NoKE aims to integrate co-occurrence among entities and relations into graph neural networks to improve knowledge graph completion. NoKE obtains state-of-the-art results on three new, challenging, and difficult benchmark datasets CoDEx for knowledge graph completion.
• Score: 13.934907240846197
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We introduce a novel embedding model, named NoKE, 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, NoKE constructs a single graph considering entities and relations as individual nodes. NoKE then computes weights for edges among nodes based on the co-occurrence of entities and relations. Next, NoKE utilizes vanilla GNNs to update vector representations for entity and relation nodes and then adopts a score function to produce the triple scores. Comprehensive experimental results show that our NoKE obtains state-of-the-art results on three new, challenging, and difficult benchmark datasets CoDEx for knowledge graph completion, demonstrating the power of its simplicity and effectiveness.

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