Bibtex

@InProceedings{10.1007/978-3-031-33455-9_16,
author=”Tong, Vinh
and Nguyen, Dai Quoc
and Phung, Dinh
and Nguyen, Dat Quoc”,
editor=”Pesquita, Catia
and Jimenez-Ruiz, Ernesto
and McCusker, Jamie
and Faria, Daniel
and Dragoni, Mauro
and Dimou, Anastasia
and Troncy, Raphael
and Hertling, Sven”,
title=”Two-View Graph Neural Networks for Knowledge Graph Completion”,
booktitle=”The Semantic Web”,
year=”2023″,
publisher=”Springer Nature Switzerland”,
address=”Cham”,
pages=”262–278″,
abstract=”We present an effective graph neural network (GNN)-based knowledge graph embedding model, which we name WGE, to capture entity- and relation-focused graph structures. Given a knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a GNN-based architecture to better learn vector representations of entities and relations from these two single entity- and relation-focused graphs. WGE feeds the learned entity and relation representations into a weighted score function to return the triple scores for knowledge graph completion. Experimental results show that WGE outperforms strong baselines on seven benchmark datasets for knowledge graph completion.”,
isbn=”978-3-031-33455-9″
}