mlsanityData to 14 Oct 2026

InterHT: Knowledge Graph Embeddings by Interaction between Head and Tail Entities

arXiv 2202.04897 ↗First posted 10 Feb 2022cs.CLcited by 6

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Abstract (arXiv)

Knowledge graph embedding (KGE) models learn the representation of entities and relations in knowledge graphs. Distance-based methods show promising performance on link prediction task, which predicts the result by the distance between two entity representations. However, most of these methods represent the head entity and tail entity separately, which limits the model capacity. We propose two novel distance-based methods named InterHT and InterHT+ that allow the head and tail entities to interact better and get better entity representation. Experimental results show that our proposed method achieves the best results on ogbl-wikikg2 dataset.

Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). Builds on 18 of its 28 references that are in the map. First posted on arXiv 10 Feb 2022.

Builds on

18 of 28 references.

References · 18

  1. 2018How Powerful are Graph Neural Networks?cited by 4,393
  2. 2017Neural Message Passing for Quantum Chemistrycited by 3,487
  3. 2020Open Graph Benchmark: Datasets for Machine Learning on Graphscited by 2,163
  4. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 932
  5. 2016Complex Embeddings for Simple Link Predictioncited by 902
  6. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 898
  7. 2015Gated Graph Sequence Neural Networkscited by 796
  8. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 324
  9. 2015Holographic Embeddings of Knowledge Graphscited by 181
  10. 2021Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Basescited by 107
  11. 2021NodePiece: Compositional and Parameter-Efficient Representations of Large Knowledge Graphscited by 72
  12. 2020PairRE: Knowledge Graph Embeddings via Paired Relation Vectorscited by 67
6 more
  1. 2013A Semantic Matching Energy Function for Learning with Multi-relational Datacited by 61
  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. 2016Probabilistic Reasoning via Deep Learning: Neural Association Modelscited by 20
  6. 2021Robust Knowledge Graph Completion with Stacked Convolutions and a Student Re-Ranking Networkcited by 11

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Cited by year

Citing papers per year: 2022: 4; 2023: 2.

6 papers.

  1. 2023Temporal Graph Benchmark for Machine Learning on Temporal Graphscited by 138
  2. 2022A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodalcited by 106
  3. 2022Knowledge Graph Embedding: A Survey from the Perspective of Representation Spacescited by 41
  4. 2022TranS: Transition-based Knowledge Graph Embedding with Synthetic Relation Representationcited by 7
  5. 2023Relation-aware Ensemble Learning for Knowledge Graph Embeddingcited by 2
  6. 2022AutoWeird: Weird Translational Scoring Function Identified by Random Searchcited by 0