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Two-view Graph Neural Networks for Knowledge Graph Completion

2021-12-16 22:36:17
Vinh Tong, Dai Quoc Nguyen, Dinh Phung, Dat Quoc Nguyen
arXiv_AI
           
Abstract

In this paper, we introduce a novel GNN-based knowledge graph embedding model, named WGE, to capture entity-focused graph structure and relation-focused graph structure. In particular, given the knowledge graph, WGE builds a single undirected entity-focused graph that views entities as nodes. In addition, WGE also constructs another single undirected graph from relation-focused constraints, which views entities and relations as nodes. WGE then proposes a new architecture of utilizing two vanilla GNNs directly on these two single graphs to better update vector representations of entities and relations, followed by a weighted score function to return the triple scores. Experimental results show that WGE obtains state-of-the-art performances on three new and challenging benchmark datasets CoDEx for knowledge graph completion.

Abstract (translated)

URL

https://arxiv.org/abs/2112.09231

PDF

https://arxiv.org/pdf/2112.09231.pdf

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