Rewiring Knowledge Graphs by Graph Neural Network Link Predictions

Alex Romanova

2023

Abstract

Knowledge Graphs recently received increasing attention from academia and industry as a new era in data-driven technology. By building relationships graphs are ’connecting the dots’ and moving data from zerodimensional to multi-dimensional space. Emerging Graph Neural Network (GNN) models are building a bridge between graph topology and deep learning. In this study we examine how to use GNN link prediction models to rewire knowledge graphs and detect unexplored relationships between graph nodes. We investigate diverse advantages of using highly connected and highly disconnected node pairs for graph mining techniques.

Paper Citation

in Harvard Style

Romanova A. (2023). Rewiring Knowledge Graphs by Graph Neural Network Link Predictions. In Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-623-1, pages 149-156. DOI: 10.5220/0011664400003393

in Bibtex Style

@conference{icaart23,
author={Alex Romanova},
title={Rewiring Knowledge Graphs by Graph Neural Network Link Predictions},
booktitle={Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2023},
pages={149-156},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011664400003393},
isbn={978-989-758-623-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 15th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Rewiring Knowledge Graphs by Graph Neural Network Link Predictions
SN - 978-989-758-623-1
AU - Romanova A.
PY - 2023
SP - 149
EP - 156
DO - 10.5220/0011664400003393