mlsanityData to 7 Oct 2026

A Review of Knowledge Graph Completion

arXiv 2208.11652 ↗First posted 24 Aug 2022cs.AIcited by 8

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Abstract (arXiv)

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. View Full-Text

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 38 of its 66 references that are in the map. First posted on arXiv 24 Aug 2022.

Builds on

38 of 66 references.

References · 38

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2019Language Models as Knowledge Bases?cited by 1,922
  4. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  5. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  6. 2014Embedding Entities and Relations for Learning and Inference in Knowledge Basescited by 929
  7. 2016Complex Embeddings for Simple Link Predictioncited by 897
  8. 2019RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Spacecited by 893
  9. 2015Gated Graph Sequence Neural Networkscited by 792
  10. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  11. 2020Knowledge Graphscited by 619
  12. 2019Composition-based Multi-Relational Graph Convolutional Networkscited by 395
26 more
  1. 2019KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representationcited by 356
  2. 2019KG-BERT: BERT for Knowledge Graph Completioncited by 323
  3. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  4. 2018SimplE Embedding for Link Prediction in Knowledge Graphscited by 195
  5. 2019Quaternion Knowledge Graph Embeddingscited by 175
  6. 2017A Novel Embedding Model for Knowledge Base Completion Based on Convolutional Neural Networkcited by 143
  7. 2017Encoding Sentences with Graph Convolutional Networks for Semantic Role Labelingcited by 137
  8. 2018End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completioncited by 136
  9. 2019Learning Hierarchy-Aware Knowledge Graph Embeddings for Link Predictioncited by 133
  10. 2019Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphscited by 125
  11. 2020BoxE: A Box Embedding Model for Knowledge Base Completioncited by 105
  12. 2020Language Models are Open Knowledge Graphscited by 95
  13. 2020JAKET: Joint Pre-training of Knowledge Graph and Language Understandingcited by 73
  14. 2013A Semantic Matching Energy Function for Learning with Multi-relational Datacited by 61
  15. 2019InteractE: Improving Convolution-based Knowledge Graph Embeddings by Increasing Feature Interactionscited by 58
  16. 2015Modeling Relation Paths for Representation Learning of Knowledge Basescited by 56
  17. 2018Hypernetwork Knowledge Graph Embeddingscited by 38
  18. 2020Convolutional Complex Knowledge Graph Embeddingscited by 21
  19. 2021DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Networkcited by 19
  20. 2020Improving Entity Linking by Modeling Latent Entity Type Informationcited by 18
  21. 2019TransGCN:Coupling Transformation Assumptions with Graph Convolutional Networks for Link Predictioncited by 17
  22. 2021Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embeddingcited by 15
  23. 2021r-GAT: Relational Graph Attention Network for Multi-Relational Graphscited by 9
  24. 2021OKGIT: Open Knowledge Graph Link Prediction with Implicit Typescited by 6
  25. 2020Distributed Representations of Entities in Open-World Knowledge Graphscited by 5
  26. 2019Composing Knowledge Graph Embeddings via Word Embeddingscited by 2

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Cited by year

Citing papers per year: 2023: 2; 2024: 2; 2025: 4.

8 papers.

  1. 2025Do LLMs Really Forget? Evaluating Unlearning with Knowledge Correlation and Confidence Awarenesscited by 13
  2. 2025Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applicationscited by 6
  3. 2023Knowledge-enhanced Neural Machine Reasoning: A Reviewcited by 3
  4. 2023Knowledge Graphs are not Created Equal: Exploring the Properties and Structure of Real KGscited by 1
  5. 2024Resilience in Knowledge Graph Embeddingscited by 1
  6. 2024Guiding Catalogue Enrichment with User Queriescited by 0
  7. 2025Type Information-Assisted Self-Supervised Knowledge Graph Denoisingcited by 0
  8. 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0