mlsanityData to 7 Oct 2026

A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

arXiv 2308.02457 ↗First posted 4 Aug 2023cs.AIcited by 14

Not on the map. Why?

Abstract (arXiv)

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 66 of its 157 references that are in the map. First posted on arXiv 4 Aug 2023.

Builds on

66 of 157 references.

References · 66

  1. 2017Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networkscited by 4,861
  2. 2018Neural Ordinary Differential Equationscited by 3,733
  3. 2017Modeling Relational Data with Graph Convolutional Networkscited by 1,684
  4. 2014On the Properties of Neural Machine Translation: Encoder-Decoder Approachescited by 1,303
  5. 2018On First-Order Meta-Learning Algorithmscited by 1,003
  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. 2017Convolutional 2D Knowledge Graph Embeddingscited by 729
  10. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 663
  11. 2019TuckER: Tensor Factorization for Knowledge Graph Completioncited by 290
  12. 2020Low-Dimensional Hyperbolic Knowledge Graph Embeddingscited by 195
38 more
  1. 2019DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphscited by 173
  2. 2018Canonical Tensor Decomposition for Knowledge Base Completioncited by 150
  3. 2017Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphscited by 149
  4. 2018Learning Sequence Encoders for Temporal Knowledge Graph Completioncited by 145
  5. 2020Tensor Decompositions for temporal knowledge base completioncited by 115
  6. 2021Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learningcited by 106
  7. 2020BoxE: A Box Embedding Model for Knowledge Base Completioncited by 105
  8. 2019Diachronic Embedding for Temporal Knowledge Graph Completioncited by 104
  9. 2019Mixed-curvature Variational Autoencoderscited by 85
  10. 2020Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networkscited by 84
  11. 2021TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecastingcited by 67
  12. 2020Understanding Negative Sampling in Graph Representation Learningcited by 66
  13. 2020TeMP: Temporal Message Passing for Temporal Knowledge Graph Completioncited by 61
  14. 2021Temporal Knowledge Graph Forecasting with Neural ODEcited by 61
  15. 2022Temporal Knowledge Graph Completion: A Surveycited by 61
  16. 2021TLogic: Temporal Logical Rules for Explainable Link Forecasting on Temporal Knowledge Graphscited by 59
  17. 2020TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotationcited by 48
  18. 2022Temporal Knowledge Graph Reasoning with Historical Contrastive Learningcited by 42
  19. 2021Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphscited by 39
  20. 2021ChronoR: Rotation Based Temporal Knowledge Graph Embeddingcited by 34
  21. 2021Temporal Knowledge Graph Completion using Box Embeddingscited by 34
  22. 2018A Capsule Network-based Embedding Model for Knowledge Graph Completion and Search Personalizationcited by 30
  23. 2020DyERNIE: Dynamic Evolution of Riemannian Manifold Embeddings for Temporal Knowledge Graph Completioncited by 30
  24. 2022EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphscited by 27
  25. 2020Event-QA: A Dataset for Event-Centric Question Answering over Knowledge Graphscited by 26
  26. 2021TempoQR: Temporal Question Reasoning over Knowledge Graphscited by 26
  27. 2022Improving Time Sensitivity for Question Answering over Temporal Knowledge Graphscited by 26
  28. 2021Spatio-Temporal Urban Knowledge Graph Enabled Mobility Predictioncited by 24
  29. 2022Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphscited by 21
  30. 2020LowFER: Low-rank Bilinear Pooling for Link Predictioncited by 20
  31. 2022Ultrahyperbolic Knowledge Graph Embeddingscited by 20
  32. 2023Normalizing Flow-based Neural Process for Few-Shot Knowledge Graph Completioncited by 19
  33. 2020Tucker decomposition-based Temporal Knowledge Graph Completioncited by 18
  34. 2020One-shot Learning for Temporal Knowledge Graphscited by 14
  35. 2022TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graphcited by 13
  36. 2022Graph Sequential Neural ODE Process for Link Prediction on Dynamic and Sparse Graphscited by 13
  37. 2021Hyperbolic Temporal Knowledge Graph Embeddings with Relational and Time Curvaturescited by 12
  38. 2023Shrinking Embeddings for Hyper-Relational Knowledge Graphscited by 12

50 most cited of 66 All 66 (JSON)

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 7; 2025: 3; 2026: 2.

14 papers.

Sort:

By area:

  1. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  2. 2024Large Language Models-guided Dynamic Adaptation for Temporal Knowledge Graph Reasoningcited by 41
  3. 2024TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphscited by 35
  4. 2023ChatRule: Mining Logical Rules with Large Language Models for Knowledge Graph Reasoningcited by 26
  5. 2024Atomic Fact Decomposition Helps Attributed Question Answeringcited by 21
  6. 2024Graph Learning under Distribution Shifts: A Comprehensive Survey on Domain Adaptation, Out-of-distribution, and Continual Learningcited by 17
  7. 2025Rethinking Regularization Methods for Knowledge Graph Completioncited by 6
  8. 2024Neurosymbolic Methods for Dynamic Knowledge Graphscited by 3
  9. 2024Resilience in Knowledge Graph Embeddingscited by 1
  10. 2025StaR-KVQA: Structured Reasoning Traces for Implicit-Knowledge Visual Question Answeringcited by 1
  11. 2026CAGE: Cognitive Attribution Graphs for Faithful Inline Citation Generation in Long-Form Question Answeringcited by 1
  12. 2024Query-Enhanced Adaptive Semantic Path Reasoning for Inductive Knowledge Graph Completioncited by 0
2 more
  1. 2025A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospectscited by 0
  2. 2026Knowledge Restoration-driven Prompt Optimization: Unlocking LLM Potential for Open-Domain Relational Triplet Extractioncited by 0