Papers with Temporal Knowledge Graph
Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)
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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Existing models for TKG reasoning focus on modeling fact sequences of a fixed length, which cannot discover complex evolutional patterns that vary in length. |
| Approach: | They propose to use a length-aware Convolutional Neural Network to handle evolutional patterns of different lengths via an easy-to-difficult curriculum learning strategy. |
| Outcome: | The proposed model improves performance under both offline and online learning strategies. |
Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)
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| Challenge: | Temporal Knowledge Graphs (TKGs) are dynamic structures representing entities and their evolving relationships through time. |
| Approach: | They propose a non-parametric model that encodes subject-centric histories into sequential embeddings. |
| Outcome: | The proposed model encodes subject-centric histories of entities, relations and temporal intervals into sequential embeddings. |
SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)
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| Challenge: | Existing models for temporal knowledge graph reasoning suffer from low training efficiency and insufficient generalization ability. |
| Approach: | They propose a temporal knowledge graph reasoning approach that uses multilayer perceptron to model the structural dependencies of events and adopts a fixed-frequency strategy to incorporate historical frequency during inference. |
| Outcome: | The proposed model achieves state-of-the-art performance with faster convergence speed and better generalization ability. |
From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)
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Guo Tang, Ke Cheng, Huiming Fan, Heng Chang, Wenxiang Zheng, Xianhao Ou, Junjia Xiang, Ming Liu, Yujun Zhou, Li Lanyu, Bing Qin
| Challenge: | Existing Large Language Models (LLMs) struggle with implicit modality alignment and suboptimal graph linearization. |
| Approach: | They propose a training-free, test-time adaptive framework that reframes TKG prediction as explicit evidence-driven reasoning. |
| Outcome: | ExE-LLM outperforms fully trained graph neural networks on four benchmarks . it achieves SOTA performance in inductive settings, significantly outperforming fully trained neural networks . |
Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning (2024.emnlp-main)
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| Challenge: | Existing models for Temporal Knowledge Graph reasoning capture repetitive history, ignoring the entity's multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
| Approach: | They propose a multi-granularity history and entity similarity learning model which captures the similarity between entities. |
| Outcome: | The proposed model can predict unknown facts based on historical information, but most existing models ignore multi-hop neighbour history which can provide valuable background knowledge for TKG reasoning. |
Predicting the Unpredictable: Uncertainty-Aware Reasoning over Temporal Knowledge Graphs via Diffusion Process (2024.findings-acl)
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| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture indeterminacy in future events, but they are limited in capturing it. |
| Approach: | They propose a Temporal Knowledge Graph reasoning process that denoises historical events and introduces Gaussian noise to corrupt target facts. |
| Outcome: | Empirical results show that DiffuTKG outperforms state-of-the-art methods on four real-world datasets. |
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. |
| Outcome: | The proposed method can model key patterns of relations in TKG, such as symmetry, asymmetry, and inverse, and can capture time-evolved relations by theory. |
Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs (2022.emnlp-main)
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| Challenge: | Existing methods for entity prediction cannot predict when an event will occur . there are many facts not related to the query that can confuse the model . |
| Approach: | They propose a temporal knowledge Graph reasoning model based on Graph Hawkes Transformer . the model captures instantaneous structural and temporal evolution information . |
| Outcome: | The proposed model performs much better under long-term evolution scenarios. |
HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)
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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng
| Challenge: | Temporal Knowledge Graphs (TKGs) store facts as triples in the form of subject, relation, object, timestamps. |
| Approach: | They propose a Temporal Knowledge Graph (TKG) model that extends each triple with a timestamp to describe dynamic facts. |
| Outcome: | The proposed model improves on six benchmark datasets with up to 5.6% performance improvement compared to the state-of-the-art models. |
Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)
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| Challenge: | Existing methods for Temporal Knowledge Graph reasoning capture intra- and inter-time latent relations between entities that appear at different times. |
| Approach: | They propose a Latent relations Learning method for TKG reasoning that captures latent relations between entities at different times. |
| Outcome: | The proposed method exploits the intra- and inter-time latent relations of entities at different times. |
STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)
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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan
| Challenge: | Temporal Knowledge Graphs (TKGs) store dynamic facts in the real world. |
| Approach: | They propose a Spatial-Temporal Knowledge Adapter which integrates the evolving graph encoder and the LLM to facilitate TKG reasoning. |
| Outcome: | The proposed method outperforms state-of-the-art methods on benchmark datasets and exhibits strong generalization capabilities in cross-dataset task. |
Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting (2024.findings-acl)
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| Challenge: | Existing graph-based models excel at capturing structural information within TKGs but lack semantic comprehension abilities. |
| Approach: | They propose a plug-and-play module to enhance the performance of graph-based TKG models by exploring high-order histories step-by-step. |
| Outcome: | Experiments on three datasets and backbones show that CoH is effective in capturing high-order historical information for LLMs. |
MAGIC: Deep Geometric Evolution with Structural Consensus for Temporal Knowledge Graph Reasoning (2026.acl-long)
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| Challenge: | Existing multi-geometry approaches face two key bottlenecks: Riemannian depth barrier and gate collapse. |
| Approach: | They propose a framework for Temporal Knowledge Graph reasoning that integrates a Tangent-Residual Engine into multi-geometric spaces to regulate gradient flow and prevent collapse. |
| Outcome: | The proposed framework improves state-of-the-art in TKG reasoning by up to 2.9 points. |
Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing models cannot abstain from uncertain predictions, which will bring risks in real-world applications. |
| Approach: | They propose to abstain from uncertain future facts by using a confidence estimator . they take both the certainty of the current prediction and the accuracy of historical predictions into account . |
| Outcome: | The proposed abstention mechanism helps existing models make selective predictions instead of indiscriminate ones. |
Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)
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| Challenge: | Existing methods to learn temporal evolutional representations of entities are hard to capture the complex temporal patterns such as sequential and repetitive. |
| Approach: | They propose a Sequential and Repetitive Pattern Learning method that captures both sequential and repetitive patterns. |
| Outcome: | The proposed method outperforms state-of-the-art methods on four representative benchmarks on GDELT dataset, where performance improvement of MRR reaches up to 18.84%. |
Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)
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| Challenge: | Existing models that model temporal dynamics with knowledge graphs and graph convolution networks lack high-order interactions between objects in TKG, which is an important factor to predict future facts. |
| Approach: | They propose to embed temporal knowledge graph reasoning by constructing hypergraphs based on temporal information graphs at different timestamps and then adapt dynamic meta-embedding to fit TKG. |
| Outcome: | The proposed method outperforms baseline models on public TKG datasets and provides good interpretation for the predicted results. |