April 24, 2022 Natural Language Processing

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

  • 05 minutes
  • Dai Quoc Nguyen*, Vinh Tong*, Dinh Phung and Dat Quoc Nguyen

  • WSDM 2022
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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.

Bibtex

@inproceedings{NoGE,
author = {Dai Quoc Nguyen and Vinh Tong and Dinh Phung and Dat Quoc Nguyen},
title = {{Node Co-occurrence based Graph Neural Networks for Knowledge Graph Link Prediction}},
booktitle = {Proceedings of the 15th ACM International Conference on Web Search and Data Mining},
year = {2022}
}

Back to Research
  • 05 minutes
  • Dai Quoc Nguyen*, Vinh Tong*, Dinh Phung and Dat Quoc Nguyen

  • WSDM 2022
Share

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