# ChronoR: Rotation Based Temporal Knowledge Graph Embedding

arXiv: https://arxiv.org/abs/2103.10379 · HTML: https://mlsanity.com/paper/2103.10379/

## Key facts

- Cited by 34 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 65 references that are in the map.
- First posted on arXiv 18 Mar 2021.

## Builds on (most cited first, 22 of 22)

- [Embedding Entities and Relations for Learning and Inference in Knowledge Bases](https://mlsanity.com/paper/1412.6575/) (2014)
- [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)
- [Representation Learning for Dynamic Graphs: A Survey](https://mlsanity.com/paper/1905.11485/) (2019)
- [Holographic Embeddings of Knowledge Graphs](https://mlsanity.com/paper/1510.04935/) (2015)
- [Quaternion Knowledge Graph Embeddings](https://mlsanity.com/paper/1904.10281/) (2019)
- [DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs](https://mlsanity.com/paper/1911.00055/) (2019)
- [Canonical Tensor Decomposition for Knowledge Base Completion](https://mlsanity.com/paper/1806.07297/) (2018)
- [Learning Sequence Encoders for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1809.03202/) (2018)
- [Tensor Decompositions for temporal knowledge base completion](https://mlsanity.com/paper/2004.04926/) (2020)
- [Diachronic Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/1907.03143/) (2019)
- [Unifying Knowledge Graph Learning and Recommendation: Towards a Better Understanding of User Preferences](https://mlsanity.com/paper/1902.06236/) (2019)
- [TeMP: Temporal Message Passing for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2010.03526/) (2020)
- [TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation](https://mlsanity.com/paper/2010.01029/) (2020)
- [Leveraging Knowledge Bases in LSTMs for Improving Machine Reading](https://mlsanity.com/paper/1902.09091/) (2019)
- [Temporal Knowledge Base Completion: New Algorithms and Evaluation Protocols](https://mlsanity.com/paper/2005.05035/) (2020)
- [Embedding Models for Episodic Knowledge Graphs](https://mlsanity.com/paper/1807.00228/) (2018)
- [Temporal Knowledge Graph Embedding Model based on Additive Time Series Decomposition](https://mlsanity.com/paper/1911.07893/) (2019)
- [Graph Hawkes Neural Network for Forecasting on Temporal Knowledge Graphs](https://mlsanity.com/paper/2003.13432/) (2020)
- [Predicting the Co-Evolution of Event and Knowledge Graphs](https://mlsanity.com/paper/1512.06900/) (2015)
- [Hotel2vec: Learning Attribute-Aware Hotel Embeddings with Self-Supervision](https://mlsanity.com/paper/1910.03943/) (2019)
- [Mining Rules Incrementally over Large Knowledge Bases](https://mlsanity.com/paper/1904.09399/) (2019)

## Built on by (most cited first, 30 of 34)

- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [Temporal Knowledge Graph Completion: A Survey](https://mlsanity.com/paper/2201.08236/) (2022)
- [Temporal Knowledge Graph Reasoning with Historical Contrastive Learning](https://mlsanity.com/paper/2211.10904/) (2022)
- [Temporal Knowledge Graph Completion using Box Embeddings](https://mlsanity.com/paper/2109.08970/) (2021)
- [A Survey on Temporal Knowledge Graph: Representation Learning and Applications](https://mlsanity.com/paper/2403.04782/) (2024)
- [EventNarrative: A large-scale Event-centric Dataset for Knowledge Graph-to-Text Generation](https://mlsanity.com/paper/2111.00276/) (2021)
- [A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects](https://mlsanity.com/paper/2308.02457/) (2023)
- [TFLEX: Temporal Feature-Logic Embedding Framework for Complex Reasoning over Temporal Knowledge Graph](https://mlsanity.com/paper/2205.14307/) (2022)
- [RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2203.07993/) (2022)
- [IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2403.19881/) (2024)
- [A Simple But Powerful Graph Encoder for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2112.07791/) (2021)
- [Few-Shot Inductive Learning on Temporal Knowledge Graphs using Concept-Aware Information](https://mlsanity.com/paper/2211.08169/) (2022)
- [Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge Graph](https://mlsanity.com/paper/2405.00352/) (2024)
- [NeuSTIP: A Novel Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs](https://mlsanity.com/paper/2305.11301/) (2023)
- [History Repeats: Overcoming Catastrophic Forgetting For Event-Centric Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2305.18675/) (2023)
- [Reasoning over Multi-view Knowledge Graphs](https://mlsanity.com/paper/2209.13702/) (2022)
- [Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory](https://mlsanity.com/paper/2604.11544/) (2026)
- [Simple but Effective Compound Geometric Operations for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2408.06603/) (2024)
- [Search to Pass Messages for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2210.16740/) (2022)
- [Improving Few-Shot Inductive Learning on Temporal Knowledge Graphs using Confidence-Augmented Reinforcement Learning](https://mlsanity.com/paper/2304.00613/) (2023)
- [Learning Multi-graph Structure for Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2312.03004/) (2023)
- [Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding](https://mlsanity.com/paper/2405.00358/) (2024)
- [Temporal Smoothness Regularisers for Neural Link Predictors](https://mlsanity.com/paper/2309.09045/) (2023)
- [Logic Diffusion for Knowledge Graph Reasoning](https://mlsanity.com/paper/2306.03515/) (2023)
- [Exploring the Limits of Historical Information for Temporal Knowledge Graph Extrapolation](https://mlsanity.com/paper/2308.15002/) (2023)
- [Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space](https://mlsanity.com/paper/2403.02355/) (2024)
- [Learning Granularity Representation for Temporal Knowledge Graph Completion](https://mlsanity.com/paper/2408.15293/) (2024)
- [Beyond Known Facts: Generating Unseen Temporal Knowledge to Address Data Contamination in LLM Evaluation](https://mlsanity.com/paper/2601.13658/) (2026)
- [Bridging Graph Structure and Knowledge-Guided Editing for Interpretable Temporal Knowledge Graph Reasoning](https://mlsanity.com/paper/2601.21978/) (2026)
- [The Dynamic Gist-Based Memory Model (DGMM): A Memory-Centric Architecture for Artificial Intelligence](https://mlsanity.com/paper/2605.02106/) (2026)

## Abstract (arXiv)

Despite the importance and abundance of temporal knowledge graphs, most of the current research has been focused on reasoning on static graphs. In this paper, we study the challenging problem of inference over temporal knowledge graphs. In particular, the task of temporal link prediction. In general, this is a difficult task due to data non-stationarity, data heterogeneity, and its complex temporal dependencies. We propose Chronological Rotation embedding (ChronoR), a novel model for learning representations for entities, relations, and time. Learning dense representations is frequently used as an efficient and versatile method to perform reasoning on knowledge graphs. The proposed model learns a k-dimensional rotation transformation parametrized by relation and time, such that after each fact's head entity is transformed using the rotation, it falls near its corresponding tail entity. By using high dimensional rotation as its transformation operator, ChronoR captures rich interaction between the temporal and multi-relational characteristics of a Temporal Knowledge Graph. Experimentally, we show that ChronoR is able to outperform many of the state-of-the-art methods on the benchmark datasets for temporal knowledge graph link prediction.