# A Survey on Temporal Knowledge Graph: Representation Learning and Applications

arXiv: https://arxiv.org/abs/2403.04782 · HTML: https://mlsanity.com/paper/2403.04782/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 48 of its 81 references that are in the map.
- First posted on arXiv 2 Mar 2024.

## Builds on (most cited first, 30 of 48)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Graph Attention Networks](https://mlsanity.com/paper/1710.10903/) (2017)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Modeling Relational Data with Graph Convolutional Networks](https://mlsanity.com/paper/1703.06103/) (2017)
- [Complex Embeddings for Simple Link Prediction](https://mlsanity.com/paper/1606.06357/) (2016)
- [RotatE: Knowledge Graph Embedding by Relational Rotation in Complex Space](https://mlsanity.com/paper/1902.10197/) (2019)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Composition-based Multi-Relational Graph Convolutional Networks](https://mlsanity.com/paper/1911.03082/) (2019)
- [TuckER: Tensor Factorization for Knowledge Graph Completion](https://mlsanity.com/paper/1901.09590/) (2019)
- [SimplE Embedding for Link Prediction in Knowledge Graphs](https://mlsanity.com/paper/1802.04868/) (2018)
- [Know-Evolve: Deep Temporal Reasoning for Dynamic Knowledge Graphs](https://mlsanity.com/paper/1705.05742/) (2017)
- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1809.03202/) (2018)
- [End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion](https://mlsanity.com/paper/1811.04441/) (2018)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Temporal Knowledge Graph Reasoning Based on Evolutional Representation Learning](https://mlsanity.com/paper/2104.10353/) (2021)
- [BoxE: A Box Embedding Model for Knowledge Base Completion](https://mlsanity.com/paper/2007.06267/) (2020)
- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1907.03143/) (2019)
- [Improving Multi-hop Knowledge Base Question Answering by Learning Intermediate Supervision Signals](https://mlsanity.com/paper/2101.03737/) (2021)
- [Question Answering Over Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.01515/) (2021)
- [Learning from History: Modeling Temporal Knowledge Graphs with Sequential Copy-Generation Networks](https://mlsanity.com/paper/2012.08492/) (2020)
- [TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting](https://mlsanity.com/paper/2109.04101/) (2021)
- [Temporal Knowledge Graph Forecasting with Neural ODE](https://mlsanity.com/paper/2101.05151/) (2021)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation](https://mlsanity.com/paper/2010.01029/) (2020)
- [Search from History and Reason for Future: Two-stage Reasoning on Temporal Knowledge Graphs](https://mlsanity.com/paper/2106.00327/) (2021)
- [Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning](https://mlsanity.com/paper/2305.10613/) (2023)

## Built on by (most cited first, 30 of 30)

- [TGB 2.0: A Benchmark for Learning on Temporal Knowledge Graphs and Heterogeneous Graphs](https://mlsanity.com/paper/2406.09639/) (2024)
- [Sora Detector: A Unified Hallucination Detection for Large Text-to-Video Models](https://mlsanity.com/paper/2405.04180/) (2024)
- [Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledge](https://mlsanity.com/paper/2606.26511/) (2026)
- [Temporal Knowledge Graph Question Answering: A Survey](https://mlsanity.com/paper/2406.14191/) (2024)
- [SEMMA: A Semantic Aware Knowledge Graph Foundation Model](https://mlsanity.com/paper/2505.20422/) (2025)
- [ATOM: AdapTive and OptiMized dynamic temporal knowledge graph construction using LLMs](https://mlsanity.com/paper/2510.22590/) (2025)
- [An Ontology-Driven Graph RAG for Legal Norms: A Structural, Temporal, and Deterministic Approach](https://mlsanity.com/paper/2505.00039/) (2025)
- [Towards Foundation Model on Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2506.06367/) (2025)
- [Temporal Graph Pattern Machine](https://mlsanity.com/paper/2601.22454/) (2026)
- [On the Temporal Question-Answering Capabilities of Large Language Models Over Anonymized Data](https://mlsanity.com/paper/2504.07646/) (2025)
- [Modeling the Diachronic Evolution of Legal Norms: An LRMoo-Based, Component-Level, Event-Centric Approach to Legal Knowledge Graphs](https://mlsanity.com/paper/2506.07853/) (2025)
- [Large Language Models as Interpolated and Extrapolated Event Predictors](https://mlsanity.com/paper/2406.10492/) (2024)
- [Higher-order Structure Boosts Link Prediction on Temporal Graphs](https://mlsanity.com/paper/2505.15746/) (2025)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Executable Ontologies: Synthesizing Event Semantics with Dataflow Architecture](https://mlsanity.com/paper/2509.09775/) (2025)
- [An Empirical Survey and Benchmark of Learned Distance Indexes for Road Networks](https://mlsanity.com/paper/2602.04068/) (2026)
- [VITA: Versatile Time Representation Learning for Temporal Hyper-Relational Knowledge Graphs](https://mlsanity.com/paper/2505.11803/) (2025)
- [A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects](https://mlsanity.com/paper/2506.11012/) (2025)
- [Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2601.21978/) (2026)
- [Retrieval Is Not Enough: Why Organizational AI Needs Epistemic Infrastructure](https://mlsanity.com/paper/2604.11759/) (2026)
- [Continuous Knowledge Metabolism: Generating Scientific Hypotheses from Evolving Literature](https://mlsanity.com/paper/2604.12243/) (2026)
- [The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence](https://mlsanity.com/paper/2605.02106/) (2026)
- [Beyond Probabilistic Similarity: Structural, Temporal, and Causal Limitations of Retrieval-Augmented Generation in the Legal Domain](https://mlsanity.com/paper/2606.09724/) (2026)
- [DYNA : Dynamic Episodic Memory Networks for Augmenting Large Language Models with Temporal Knowledge Graphs in Continuous Learning](https://mlsanity.com/paper/2606.15778/) (2026)
- [Time Imprint: Learning Time-Aware Representations in Multi-Modal Knowledge Graphs](https://mlsanity.com/paper/2607.09777/) (2026)
- [THBKG: A Temporal Biomedical Knowledge Graph for Decision-Aligned Clinical Advancement Prediction](https://mlsanity.com/paper/2608.05982/) (2026)
- [FITTER: Vocabulary-Agnostic Cross-Domain Inference on Temporal Knowledge Graphs](https://mlsanity.com/paper/2608.10668/) (2026)
- [SABET-QA: Temporal Knowledge Graph Question Answering](https://mlsanity.com/paper/2608.20083/) (2026)
- [An Explainable Coherence Score for Detecting Temporal Inconsistencies in Political News](https://mlsanity.com/paper/2608.29175/) (2026)
- [NS-ST-GraphRAG: Neuro-Symbolic Spatio-Temporal GraphRAG for Literary Knowledge Processing](https://mlsanity.com/paper/2609.05139/) (2026)

## Abstract (arXiv)

Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications. However, most current studies mainly focus on static knowledge graphs, whose facts do not change with time, and disregard their dynamic evolution over time. As a result, temporal knowledge graphs have attracted more attention because a large amount of structured knowledge exists only within a specific period. Knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. The representation learning of temporal knowledge graphs incorporates time information into the standard knowledge graph framework and can model the dynamics of entities and relations over time. In this paper, we conduct a comprehensive survey of temporal knowledge graph representation learning and its applications. We begin with an introduction to the definitions, datasets, and evaluation metrics for temporal knowledge graph representation learning. Next, we propose a taxonomy based on the core technologies of temporal knowledge graph representation learning methods, and provide an in-depth analysis of different methods in each category. Finally, we present various downstream applications related to the temporal knowledge graphs. In the end, we conclude the paper and have an outlook on the future research directions in this area.