mlsanityData to 7 Oct 2026

Two-view Graph Neural Networks for Knowledge Graph Completion

arXiv 2112.09231 ↗First posted 16 Dec 2021cs.CL cs.AI cs.LGcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 16 Dec 2021.

Builds on

27 of 50 references.

References · 27

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15 more
  1. 2015Holographic Embeddings of Knowledge Graphscited by 181
  2. 2019Quaternion Knowledge Graph Embeddingscited by 175
  3. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 0; 2026: 1.

3 papers.

  1. 2022Joint Multilingual Knowledge Graph Completion and Alignmentcited by 3
  2. 2024Fully Hyperbolic Rotation for Knowledge Graph Embeddingcited by 3
  3. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0