TKG

Complex Evolutional Pattern Learning for Temporal Knowledge Graph Reasoning (2022.acl-short)

Challenge:	Existing methods that incorporate time information into static knowledge graph embedding ignore the contextual nature of the TKG structure.
Approach:	They propose a method that employs pre-trained language models to learn joint Structural and Temporal Contextualized Knowledge Embeddings.
Outcome:	The proposed method is superior to existing methods that ignore the contextual nature of the TKG structure.

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Zixuan Li, Saiping Guan, Xiaolong Jin, Weihua Peng, Yajuan Lyu, Yong Zhu, Long Bai, Wei Li, Jiafeng Guo, Xueqi Cheng

Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning (2023.emnlp-main)

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.

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Dong-Ho Lee, Kian Ahrabian, Woojeong Jin, Fred Morstatter, Jay Pujara

Relation Logical Reasoning and Relation-aware Entity Encoding for Temporal Knowledge Graph Reasoning (2025.coling-main)

Challenge:	Temporal knowledge graphs (TKGs) are used to represent real-world facts in a structured way.
Approach:	They propose to use in-context learning with large language models for TKG forecasting . they compare naive LLMs to state-of-the-art (SOTA) supervised models .
Outcome:	The proposed approach performs well against pre-trained large language models . the proposed approach is based on simple heuristics and state-of-the-art models compared with pre-trainers .

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Longzhou Liu, Chenglong Xiao, Shanshan Wang, Tingwen Liu

Arbitrary Time Information Modeling via Polynomial Approximation for Temporal Knowledge Graph Embedding (2024.lrec-main)

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.
Outcome:	The proposed model outperforms state-of-the-art models on five public datasets . it uses relation-aware attention mechanisms to learn relation embeddings based on query relations .

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Zhiyu Fang, Jingyan Qin, Xiaobin Zhu, Chun Yang, Xu-Cheng Yin

Negative-Aware Diffusion Process for Temporal Knowledge Graph Extrapolation (2026.findings-eacl)

Challenge:	Existing knowledge graphs lack rich inference patterns and the limited ability to model arbitrary timestamps continuously.
Approach:	They propose a temporal knowledge graph-based temporal representation method that decomposes time information by polynomials and then enhances the model's capability to represent arbitrary timestamps flexibly.
Outcome:	The proposed method can encode arbitrary time information or even unseen timestamps while capturing rich inference patterns and higher-arity relations of the knowledge base.

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Yanglei Gan, Peng He, Yuxiang Cai, Run Lin, Guanyu Zhou, Qiao Liu

A Simple Temporal Information Matching Mechanism for Entity Alignment between Temporal Knowledge Graphs (2022.coling-1)

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.

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Li Cai, Xin Mao, Meirong Ma, Hao Yuan, Jianchao Zhu, Man Lan

Hawkes based Representation Learning for Reasoning over Scale-free Community-structured Temporal Knowledge Graphs (2025.coling-main)

Challenge:	Existing methods for EA between temporal KGs incorporate relational and temporal information into entity embeddings.
Approach:	They propose a method to generate unsupervised alignment seeds using temporal information from TKGs.
Outcome:	The proposed method outperforms the previous methods by using temporal information.

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Yuwei Du, Xinyue Liu, Wenxin Liang, Linlin Zong, Xianchao Zhang

SiMFy: A Simple Yet Effective Approach for Temporal Knowledge Graph Reasoning (2023.findings-emnlp)

Challenge:	Temporal knowledge graph reasoning is a useful tool for many practical tasks.
Approach:	They propose a Hawkes process-based Evolutional Representation Learning Network model which learns structural information and evolutional patterns of a TKG simultaneously.
Outcome:	The proposed model learns structural information and evolutional patterns of a TKG simultaneously, considering the characteristics of real-world networks: community structure, scale-free and temporal decaying.

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Zhengtao Liu, Lei Tan, Mengfan Li, Yao Wan, Hai Jin, Xuanhua Shi

From Implicit Graph Encoding to Explicit Evidence: A Training-Free LLM Framework for Temporal Knowledge Graph Reasoning (2026.findings-acl)

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.

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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

NeuSTIP: A Neuro-Symbolic Model for Link and Time Prediction in Temporal Knowledge Graphs (2023.emnlp-main)

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 .

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Ishaan Singh, Navdeep Kaur, Garima Gaur, null Mausam

Multi-Granularity History and Entity Similarity Learning for Temporal Knowledge Graph Reasoning (2024.emnlp-main)

Challenge:	Temporal Knowledge Graphs (KGs) are factual information repositories where a fact is associated with a time interval.
Approach:	They propose a temporal NS model for knowledge graph completion that performs link prediction and time interval prediction in a TKG.
Outcome:	The proposed model shows competitive performance on link prediction and time prediction.

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Shi Mingcong, Chunjiang Zhu, Detian Zhang, Shiting Wen, Li Qing

Predicting the Unpredictable: Uncertainty-Aware Reasoning over Temporal Knowledge Graphs via Diffusion Process (2024.findings-acl)

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.

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Yuxiang Cai, Qiao Liu, Yanglei Gan, Changlin Li, Xueyi Liu, Run Lin, Da Luo, JiayeYang JiayeYang

RotateQVS: Representing Temporal Information as Rotations in Quaternion Vector Space for Temporal Knowledge Graph Completion (2022.acl-long)

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.

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Kai Chen, Ye Wang, Yitong Li, Aiping Li

Temporal Knowledge Graph Completion with Approximated Gaussian Process Embedding (2022.coling-1)

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.

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Linhai Zhang, Deyu Zhou

RTFE: A Recursive Temporal Fact Embedding Framework for Temporal Knowledge Graph Completion (2021.naacl-main)

Challenge:	Existing TKGC methods are based on deterministic vector embeddings, which are not flexible and expressive enough.
Approach:	They propose a method that maps entities and relations to multivariate Gaussian processes by mapping global trends and local fluctuations in TKGs.
Outcome:	The proposed method can predict global trends and local fluctuations in the TKGs and can be optimized on two real-world benchmark datasets.

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Youri Xu, Haihong E, Meina Song, Wenyu Song, Xiaodong Lv, Wang Haotian, Yang Jinrui

History repeats: Overcoming catastrophic forgetting for event-centric temporal knowledge graph completion (2023.findings-acl)

Challenge:	Existing methods for static knowledge graph embedding (SKGE) ignore the continuity of states of TKGs in time evolution.
Approach:	They propose a Recursive Temporal Fact Embedding framework to transplant SKGE models to TKGs and enhance the performance of existing TKGE models.
Outcome:	The proposed framework can be used to transplant SKGE models to TKGs and improve existing models for TKG completion.

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Mehrnoosh Mirtaheri, Mohammad Rostami, Aram Galstyan

Graph Hawkes Transformer for Extrapolated Reasoning on Temporal Knowledge Graphs (2022.emnlp-main)

Challenge:	Existing methods for knowledge graph completion are incomplete and can lead to errors . retraining the model with the entire updated TKG can mitigate forgetting but is computationally burdensome.
Approach:	They propose a temporal regularization framework that allows repurposing of parameters . they propose 'clustering-based experience replay' that reinforces the past knowledge .
Outcome:	The proposed framework adapts to new events while reducing catastrophic forgetting.

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Haohai Sun, Shangyi Geng, Jialun Zhong, Han Hu, Kun He

TR-Rules: Rule-based Model for Link Forecasting on Temporal Knowledge Graph Considering Temporal Redundancy (2023.findings-emnlp)

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.

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Ningyuan Li, Haihong E, Shi Li, Mingzhi Sun, Tianyu Yao, Meina Song, Yong Wang, Haoran Luo

HiSMatch: Historical Structure Matching based Temporal Knowledge Graph Reasoning (2022.findings-emnlp)

Challenge:	Existing models suffer from temporal redundancy when leveraged under dynamic settings.
Approach:	They propose a temporal knowledge graph extrapolation method which solves temporal redundancy issues by using cyclic rules to capture more information lurking in TKGs.
Outcome:	The proposed model captures more information lurking in TKGs, and also mines and properly leverages acyclic rules, which has not been explored by existing models.

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Zixuan Li, Zhongni Hou, Saiping Guan, Xiaolong Jin, Weihua Peng, Long Bai, Yajuan Lyu, Wei Li, Jiafeng Guo, Xueqi Cheng

Natural Evolution-based Dual-Level Aggregation for Temporal Knowledge Graph Reasoning (2024.findings-emnlp)

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.

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Bin Chen, Chunjing Xiao, Fan Zhou

TimeTraveler: Reinforcement Learning for Temporal Knowledge Graph Forecasting (2021.emnlp-main)

Challenge:	Existing models ignore asynchronous characteristics of event evolution, resulting in suboptimal performance.
Approach:	They propose a Natural Evolution-based Dual-level Aggregation framework for TKG reasoning that incorporates asynchronous characteristics of event evolution into the model.
Outcome:	The proposed model incorporates the asynchronous characteristics of event evolution for representation computation, thus improving prediction performance.

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Haohai Sun, Jialun Zhong, Yunpu Ma, Zhen Han, Kun He

Disentangled Multi-span Evolutionary Network against Temporal Knowledge Graph Reasoning (2025.findings-acl)

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.
Outcome:	The proposed method improves on four benchmark datasets with higher explainability, less calculation, and fewer parameters when compared with existing state-of-the-art methods.

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Hao Dong, Ziyue Qiao, Zhiyuan Ning, Qi Hao, Yi Du, Pengyang Wang, Yuanchun Zhou

MusTQ: A Temporal Knowledge Graph Question Answering Dataset for Multi-Step Temporal Reasoning (2024.findings-acl)

Challenge:	Existing methods for temporal knowledge Graphs neglect internal structural interactions between subgraphs and ignore potential smooth features that do not lead to semantic changes.
Approach:	They propose to use a disentangled multi-span evolutionary network to capture local neighbor features while perceiving historical neighbor semantic information.
Outcome:	Extensive experiments show that the proposed model outperforms the state-of-the-art in TKG reasoning by 22.7%.

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Tingyi Zhang, Jiaan Wang, Zhixu Li, Jianfeng Qu, An Liu, Zhigang Chen, Hongping Zhi

Learning Latent Relations for Temporal Knowledge Graph Reasoning (2023.acl-long)

Challenge:	Existing studies focus on fact-centered reasoning with limited attention to temporal reasoning.
Approach:	They propose a new TKGQA dataset, MusTQ, which contains 666K multi-step temporal reasoning questions and a TKG.
Outcome:	The proposed model achieves state-of-the-art multi-step temporal reasoning ability with entity-time attention mechanism and optimized temporal knowledge graph representation.

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Mengqi Zhang, Yuwei Xia, Qiang Liu, Shu Wu, Liang Wang

TeAST: Temporal Knowledge Graph Embedding via Archimedean Spiral Timeline (2023.acl-long)

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.

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Jiang Li, Xiangdong Su, Guanglai Gao

SRM-LLM: Semantic Relationship Mining with LLMs for Temporal Knowledge Graph Extrapolation (2025.findings-emnlp)

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.

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Fu Zhang, Panfeng Zhang, Jingwei Cheng

STK-Adapter: Incorporating Evolving Graph and Event Chain for Temporal Knowledge Graph Extrapolation (2026.acl-long)

Challenge:	Existing methods for temporal knowledge graph extrapolation neglect the complex semantic relationships between relations when modeling their dynamic evolution.
Approach:	They propose a method for extracting semantic relationships to achieve TKG extrapolation . they use large language models to analyze the types of relations in TKGs .
Outcome:	The proposed method improves on five TKG datasets and shows performance gains.

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Shuyuan Zhao, Wei Chen, Weijie Zhang, Xinrui Hou, Junfeng Shen, Boyan Shi, Shengnan Guo, Youfang Lin, Huaiyu Wan

Chain-of-History Reasoning for Temporal Knowledge Graph Forecasting (2024.findings-acl)

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.

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Yuwei Xia, Ding Wang, Qiang Liu, Liang Wang, Shu Wu, Xiao-Yu Zhang

MAGIC: Deep Geometric Evolution with Structural Consensus for Temporal Knowledge Graph Reasoning (2026.acl-long)

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.

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Chengao Liu, Yuan Li, Yingze Wang, Jianbin Jiao

DLTKG: Denoising Logic-based Temporal Knowledge Graph Reasoning (2025.findings-emnlp)

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.

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Xiaoke Wang, Fu Zhang, Jingwei Cheng, Yiwen Chi, Jiashun Peng, Yingsong Ning

G2S: A General-to-Specific Learning Framework for Temporal Knowledge Graph Forecasting with Large Language Models (2025.findings-acl)

Challenge:	Current approaches to temporal knowledge representation face limited generalization to unseen facts and insufficient interpretability of reasoning processes.
Approach:	They propose a framework that uses a denoising diffusion process to complete reasoning tasks . they propose introducing a noise source and historical conditionguiding mechanism to improve interpretability .
Outcome:	The proposed framework outperforms state-of-the-art methods on three benchmark datasets.

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Long Bai, Zixuan Li, Xiaolong Jin, Jiafeng Guo, Xueqi Cheng, Tat-Seng Chua

Selective Temporal Knowledge Graph Reasoning (2024.lrec-main)

Challenge:	Recent studies have introduced Large Language Models (LLMs) for this task to enhance the models’ generalization abilities.
Approach:	They propose a General-to-Specific learning framework that disentangles the learning processes of two kinds of knowledge in a temporal temporal structure.
Outcome:	The proposed framework disentangles the learning processes of the above two kinds of knowledge and improves their generalization abilities.

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Zhongni Hou, Xiaolong Jin, Zixuan Li, Long Bai, Jiafeng Guo, Xueqi Cheng

Sequential and Repetitive Pattern Learning for Temporal Knowledge Graph Reasoning (2024.lrec-main)

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.

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Xuefei Li, Huiwei Zhou, Weihong Yao, Wenchu Li, Yingyu Lin, Lei Du

Temporal Knowledge Graph Reasoning with Dynamic Hypergraph Embedding (2024.lrec-main)

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%.

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Xinyue Liu, Jianan Zhang, Chi Ma, Wenxin Liang, Bo Xu, Linlin Zong

Critic Rule Induction: Improving Temporal Knowledge Graph Forecasting with Generator-Critic Language Models (2026.findings-acl)

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.

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Yingsong Ning, Fu Zhang, Jingwei Cheng, Jiashun Peng, Xiaoke Wang

Evolving Beyond Snapshots: Harmonizing Structure and Sequence via Entity State Tuning for Temporal Knowledge Graph Forecasting (2026.acl-long)

Challenge:	Existing methods for predicting future facts from time-evolving graphs rely on statistical co-occurrences and extensive path enumeration.
Approach:	They propose a Critic-Guided Rule Induction method which treats temporal rules as rule hypotheses to be examined and adopts a decoupled Generation-Discrimination pipeline to induce rules that are high-coverage and high-precision.
Outcome:	The proposed method outperforms strong baselines on three benchmarks and achieves state-of-the-art performance.

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Siyuan Li, Yunjia Wu, Yiyong Xiao, Pingyang Huang, Peize Li, Ruitong Liu, Yan Wen, Te Sun