TeRo: A Time-aware Knowledge Graph Embedding via Temporal Rotation (2020.coling-main)
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| Challenge: | Existing knowledge graphs that contain time information for entities and relations have been used for learning and inference. |
| Approach: | They propose a temporal evolution of entity embedding that defines the temporal rotation from the initial time to the current time in the complex vector space. |
| Outcome: | The proposed model outperforms existing state-of-the-art models for link prediction on three different TKGs. |
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| Challenge: | Existing models rely on historical information to learn embeddings for entities, but ignore the evolution of facts. |
| Approach: | They propose a Temporal Meta-learning framework to learn evolutionary meta-knowledge from TKGs. |
| Outcome: | The proposed method improves on four widely-used datasets and three backbones on a wide range of scenarios on tKGs. |
RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion (2022.acl-long)
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| Challenge: | Existing methods for temporal knowledge graphs can hardly model temporal relation patterns, lacking of interpretability. |
| Approach: | They propose a temporal modeling method which represents temporal entities as Rotations in Quaternion Vector Space and relations as complex vectors in Hamilton’s quaterniont space. |
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Leveraging 3D Gaussian for Temporal Knowledge Graph Embedding (2025.findings-emnlp)
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| Challenge: | Representation learning in knowledge graphs (KGs) has focused on static data, yet many real-world knowledge graph are inherently dynamic. |
| Approach: | They propose a temporal embedding method inspired by 3D Gaussian Splatting where entities, relations, and timestamps are modeled as 3D gaussian distributions with learnable structured covariance. |
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Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning (2025.coling-main)
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| Challenge: | Current knowledge graph models focus on embedding entities and relations, overlooking the broader structure of the entire knowledge graph. |
| Approach: | They propose a Temporal Knowledge Graph Reasoning model that embeds relation embeddings into the TKG. |
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Time-aware Graph Neural Network for Entity Alignment between Temporal Knowledge Graphs (2021.emnlp-main)
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| Challenge: | Existing embedding-based approaches disregard time information that exists in many large-scale knowledge graphs, leaving much room for improvement. |
| Approach: | They propose a Time-aware Entity Alignment approach that incorporates relation and time information into a vector space and uses Graph Neural Networks to learn entity representations. |
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zrLLM: Zero-Shot Relational Learning on Temporal Knowledge Graphs with Large Language Models (2024.naacl-long)
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| Challenge: | Existing methods to forecast links on temporal knowledge graphs are embedding-based . but they face a strong challenge in modeling the unseen zero-shot relations . |
| Approach: | They propose to embed knowledge graphs (TKGF) entities and relations based on observed contexts into embedding-based methods to model unseen zero-shot relations. |
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TeRDy: Temporal Relation Dynamics through Frequency Decomposition for Temporal Knowledge Graph Completion (2025.acl-long)
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| Challenge: | Existing methods for temporal knowledge graph completion struggle to capture long-term changes and short-term variability of relations. |
| Approach: | They propose a method that captures temporal relational dynamics by time-invariant embeddings and time-outvariant time-variant embeddedding. |
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GenTKG: Generative Forecasting on Temporal Knowledge Graph with Large Language Models (2024.findings-naacl)
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| Challenge: | Existing methods for temporal relational forecasting are limited and require limited training data. |
| Approach: | They propose a retrieval-augmented generation framework that uses temporal logical rule-based retrieval and parameter-efficient instruction tuning to solve temporal knowledge forecasting challenges. |
| Outcome: | The proposed framework outperforms conventional methods in the temporal knowledge graph domain with low computation resources. |
TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting (2021.emnlp-main)
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| Challenge: | Existing methods focus on reasoning at past timestamps to complete the missing facts, and there are only a few works of reasoning on known TKGs to forecast future facts. |
| Approach: | They propose a time-shaped reward method that captures historical knowledge graph snapshots and a new representation method for unseen entities to improve the inductive inference ability of the model. |
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TeAST: Temporal Knowledge Graph Embedding via Archimedean Spiral Timeline (2023.acl-long)
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| Challenge: | Existing temporal knowledge graph embedding models fuse temporal information into entities, limiting their effectiveness and potential applications. |
| Approach: | They propose a temporal knowledge graph embedding model which encodes Temporal knowledge graphs via Archimedean Spiral Timeline. |
| Outcome: | The proposed model outperforms existing TKGE methods in terms of relational consistency and interpretability. |