mlsanityData to 7 Oct 2026

TripleRE: Knowledge Graph Embeddings via Tripled Relation Vectors

arXiv 2209.08271 ↗First posted 17 Sep 2022cs.AIcited by 10

Not on the map. Why?

Abstract (arXiv)

Translation-based knowledge graph embedding has been one of the most important branches for knowledge representation learning since TransE came out. Although many translation-based approaches have achieved some progress in recent years, the performance was still unsatisfactory. This paper proposes a novel knowledge graph embedding method named TripleRE with two versions. The first version of TripleRE creatively divide the relationship vector into three parts. The second version takes advantage of the concept of residual and achieves better performance. In addition, attempts on using NodePiece to encode entities achieved promising results in reducing the parametric size, and solved the problems of scalability. Experiments show that our approach achieved state-of-the-art performance on the large-scale knowledge graph dataset, and competitive performance on other datasets.

Key facts. Cited by 10 papers in the map (data through 7 Oct 2026). Builds on 18 of its 34 references that are in the map. First posted on arXiv 17 Sep 2022.

Builds on

18 of 34 references.

References · 18

  1. 2017Neural Message Passing for Quantum Chemistrycited by 3,460
  2. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,148
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  5. 2016Complex Embeddings for Simple Link Predictioncited by 897
  6. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  7. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  8. 2019Inductive Relation Prediction by Subgraph Reasoningcited by 237
  9. 2018SimplE Embedding for Link Prediction in Knowledge Graphscited by 195
  10. 2015Holographic Embeddings of Knowledge Graphscited by 181
  11. 2021Knowledge Graph Reasoning with Relational Digraphcited by 91
  12. 2021NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphscited by 71
6 more
  1. 2020PairRE: Knowledge Graph Embeddings via Paired Relation Vectorscited by 67
  2. 2019Orthogonal Relation Transforms with Graph Context Modeling for Knowledge Graph Embeddingcited by 38
  3. 2021Relation Prediction as an Auxiliary Training Objective for Improving Multi-Relational Graph Representationscited by 37
  4. 2019AutoSF: Searching Scoring Functions for Knowledge Graph Embeddingcited by 34
  5. 2014Open Question Answering with Weakly Supervised Embedding Modelscited by 26
  6. 2020SEEK: Segmented Embedding of Knowledge Graphscited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 2; 2023: 4; 2024: 3; 2025: 0; 2026: 1.

10 papers.

  1. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  2. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  3. 2023Zero-shot causal learningcited by 17
  4. 2024Chain of History: Learning and Forecasting with LLMs for Temporal Knowledge Graph Completioncited by 10
  5. 2023Knowledge Graph Embedding with 3D Compound Geometric Transformationscited by 3
  6. 2026S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answeringcited by 3
  7. 2023TransERR: Translation-based Knowledge Graph Embedding via Efficient Relation Rotationcited by 2
  8. 2023Relation-aware Ensemble Learning for Knowledge Graph Embeddingcited by 2
  9. 2024The Role of Graph Topology in the Performance of Biomedical Knowledge Graph Completion Modelscited by 2
  10. 2024Knowledge Graph Embeddings: A Comprehensive Survey on Capturing Relation Propertiescited by 2