Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction
Abstract
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.
Cite
@article{arxiv.2104.07396,
title = {Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction},
author = {Dai Quoc Nguyen and Vinh Tong and Dinh Phung and Dat Quoc Nguyen},
journal= {arXiv preprint arXiv:2104.07396},
year = {2021}
}
Comments
To appear in Proceedings of WSDM 2022. The first two authors contributed equally to this work