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TKGER

Some papers on Temporal Knowledge Graph Embedding and Reasoning

Useful research resources

  1. Graph-based Deep Learning Literature, Github

    links to conference publications in graph-based deep learning

  2. Reinforcement learning on graphs: A survey, Github

    This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers.

  3. Awesome Machine Learning for Combinatorial Optimization Resources, Github

    Awesome machine learning for combinatorial optimization papers.

  4. Awesome-TKGC, Github

    A collection of papers and resources about temporal knowledge graph completion (TKGC).

  5. AKGR: Awesome Knowledge Graph Reasoning, Github

    AKGR: Awesome Knowledge Graph Reasoning is a collection of knowledge graph reasoning works, including papers, codes and datasets.

  6. Awesome Knowledge Graph, Github

    A curated list of Knowledge Graph related learning materials, databases, tools and other resources.

  7. Awesome-DynamicGraphLearning, Github

    Awesome papers about machine learning (deep learning) on dynamic (temporal) graphs (networks / knowledge graphs).

  8. KGE, Github

    Some papers on Knowledge Graph Embedding(KGE)

  9. KGLQ, Github

    Some papers about Logical Query on Knowledge Graphs (KGLQ)

  10. ADGC: Awesome Deep Graph Clustering, Github

    Awesome Deep Graph Clustering is a collection of SOTA, novel deep graph clustering methods (papers, codes, and datasets).

  11. Graph Adversarial Learning Literature, Github

    A curated list of adversarial attacks and defenses papers on graph-structured data.

Tutorial

2024

[1] New Frontiers of Knowledge Graph Reasoning: Recent Advances and Future Trends - WWW 2024, Webpage

2023

[1] Knowledge Graph Reasoning and Its Applications - KDD 2023, Webpage

2022

[1] Reasoning on Knowledge Graphs: Symbolic or Neural? - AAAI 2022, Webpage

2021

[1] All You Need to Know to Build a Product Knowledge Graph - KDD 2021 Tutorial, Webpage

2018

[1] Fact Checking: Theory and Practice - KDD 2018 Tutorial, Webpage

Survey Papers

2024

  1. Knowledge Graph Embedding: An Overview, APSIPA Transactions on Signal and Information Processing, 2024. paper Ge, X., Wang, Y. C., Wang, B., & Kuo, C. C. J

  2. Survey of Temporal Knowledge Graph Completion Methods, Journal of Computer Engineering & Applications, 2024. paper Lei, X. I. A. O., & Qi, L. I.

  3. Overview of Knowledge Reasoning for Knowledge Graph, Neurocomputing, 2024. paper Liu, X., Mao, T., Shi, Y., & Ren, Y.

  4. Knowledge graph embedding: A survey from the perspective of representation spaces, ACM Computing Surveys, 2024. paper Cao, J., Fang, J., Meng, Z., & Liang, S.

  5. A survey on graph representation learning methods, ACM Transactions on Intelligent Systems and Technology, 2024. paper Khoshraftar, S., & An, A.

  6. Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey, ArXiv, 2024. paper Chen, Z., Zhang, Y., Fang, Y., Geng, Y., Guo, L., Chen, X., … & Chen, H.

  7. A survey for managing temporal data in RDF, Information Systems, 2024. paper Wu, Di, Hsien-Tseng Wang, and Abdullah Uz Tansel

  8. A Survey on Temporal Knowledge Graph: Representation Learning and Applications, ArXiv, 2024. paper Cai, Li, Xin Mao, Yuhao Zhou, Zhaoguang Long, Changxu Wu, and Man Lan.

  9. A survey of inductive knowledge graph completion. Neural Computing and Applications, 36(8), 3837-3858, 2024. paper Liang, X., Si, G., Li, J., Tian, P., An, Z., & Zhou, F.

  10. Survey and open problems in privacy-preserving knowledge graph: merging, query, representation, completion, and applications. International Journal of Machine Learning and Cybernetics, 1-20., 2024. paper Chen, C., Zheng, F., Cui, J., Cao, Y., Liu, G., Wu, J., & Zhou, J.

  11. A comprehensive survey on deep graph representation learning. Neural Networks, 106207, 2024. paper Ju, W., Fang, Z., Gu, Y., Liu, Z., Long, Q., Qiao, Z., … & Zhang, M.

  12. Knowledge Graph Embedding: An Overview. APSIPA Transactions on Signal and Information Processing, 13(1), 2024. paper Ge, X., Wang, Y. C., Wang, B., & Kuo, C. C. J.

  13. Temporal Knowledge Graph Question Answering: A Survey. arXiv preprint arXiv:2406.14191. paper Su, M., Li, Z., Chen, Z., Bai, L., Jin, X., & Guo, J.

  14. Temporal Knowledge Graph Reasoning: A Review. ig Data and Social Computing. BDSC 2024. paper Yu, C., Luo, T., Wang, J., Cao, Z.

  15. Neural-Symbolic Methods for Knowledge Graph Reasoning: A Survey. ACM Transactions on Knowledge Discovery from Data. paper Cheng, K., Ahmed, N. K., Rossi, R. A., Willke, T., & Sun, Y. (2024).

  16. A survey on temporal knowledge graph embedding: Models and applications. Knowledge-Based Systems (2024): 112454. paper Zhang, Yuchao, Xiangjie Kong, Zhehui Shen, Jianxin Li, Qiuhua Yi, Guojiang Shen, and Bo Dong

2023

  1. A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects, ArXiv, 2023. paper

    Jiapu Wang, Boyue Wang, Meikang Qiu, Shirui Pan, Bo Xiong, Heng Liu, Linhao Luo, Tengfei Liu, Yongli Hu, Baocai Yin, Wen Gao

  2. Knowledge Graphs: Opportunities and Challenges, Artificial Intelligence Review, 2023, paper

    Ciyuan Peng, Feng Xia, Mehdi Naseriparsa & Francesco Osborne

  3. Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs, ArXiv, 2023. paper

    Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen

  4. A Comprehensive Survey on Automatic Knowledge Graph Construction, ArXiv, 2023. paper

    Lingfeng Zhong, Jia Wu, Qian Li, Hao Peng, Xindong Wu

2022

  1. Temporal Knowledge Graph Completion: A Survey ArXiv, 2022. paper

    Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li.

Update: Borui Cai, Yong Xiang, Longxiang Gao, He Zhang, Yunfeng Li, Jianxin Li, Temporal Knowledge Graph Completion: A Survey, 2023 Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Survey Track. Pages 6545-6553. paper

  1. Reasoning over different types of knowledge graphs: Static, temporal and multi-modal, ArXiv, 2022. paper

  2. A Survey on Temporal Knowledge Graphs-Extrapolation and Interpolation Tasks, Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery, paper

    Sulin Chen & Jingbin Wang

  3. Generalizing to Unseen Elements: A Survey on Knowledge Extrapolation for Knowledge Graphs. 2023 Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, Survey Track. paper

    Mingyang Chen, Wen Zhang, Yuxia Geng, Zezhong Xu, Jeff Z. Pan, Huajun Chen

2021

  1. Survey on Temporal Knowledge Graph, 2021 IEEE Sixth International Conference on Data Science in Cyberspace (DSC). paper

    Chong Mo; Ye Wang; Yan Jia; Qing Liao

Datasets

Name #Entities #Relations #Timestamps #Collections Timestamp Link download
ICEWS14 7128 230 365 90730 point https://paperswithcode.com/sota/link-prediction-on-icews14-1
ICEWS05-15 10488 251 4017 479329 point https://paperswithcode.com/sota/link-prediction-on-icews05-15-1
ICEWS18 23033 256 304 468558 point https://docs.dgl.ai/en/0.8.x/generated/dgl.data.ICEWS18Dataset.html
GDELT 500 20 366 3419607 point https://www.gdeltproject.org/
YAGO15k 15403 32 169 138048 interval https://paperswithcode.com/sota/link-prediction-on-yago15k-1
WIKIDATA 11153 96 328 150079 interval https://www.wikidata.org/wiki/Wikidata:Main_Page

2024

Knowledge-Based Systems

[1] Yue, L., Ren, Y., Zeng, Y., Zhang, J., Zeng, K., Wan, J., & Zhou, M. (2024). Complex expressional characterizations learning based on block decomposition for temporal knowledge graph completion. Knowledge-Based Systems, 111591.

[2] Zhu, L., Zhang, H., & Bai, L. (2024). Hierarchical pattern-based complex query of temporal knowledge graph. Knowledge-Based Systems, 284, 111301.

[3] Huang, H., Xie, L., Liu, M., Lin, J., & Shen, H. (2024). An embedding model for temporal knowledge graphs with long and irregular intervals. Knowledge-Based Systems, 111893.

[4] Guo, J., Yu, J., Zhao, M., Yu, M., Yu, R., Xu, L., … & Li, X. (2024). TELS: Learning time-evolving information and latent semantics using dual quaternion for temporal knowledge graph completion. Knowledge-Based Systems, 112268.

[5] Hu, J., Zhu, Y., Teng, F., & Li, T. (2024). Temporal knowledge graph reasoning based on relation graphs and time-guided attention mechanism. Knowledge-Based Systems, 112280.

Applied Intelligence

[1] Wang, J., Wu, R., Wu, Y., Zhang, F., Zhang, S., & Guo, K. (2024). MPNet: temporal knowledge graph completion based on a multi-policy network. Applied Intelligence, 1-17. Github

[2] Ma, Q., Zhang, X., Ding, Z., Gao, C., Shang, W., Nong, Q., … & Jin, Z. (2024). Temporal knowledge graph reasoning based on evolutional representation and contrastive learning. Applied Intelligence, 1-19.

ACM TKDD

[1] Li, X., Zhou, H., Yao, W., Li, W., Liu, B., & Lin, Y. (2024). Intricate Spatiotemporal Dependency Learning for Temporal Knowledge Graph Reasoning. ACM Transactions on Knowledge Discovery from Data.

Information Science

[1] (THOR) Lee, Y. C., Lee, J., Lee, D., & Kim, S. W. (2024). Learning to compensate for lack of information: Extracting latent knowledge for effective temporal knowledge graph completion. Information Sciences, 654, 119857.

Extended version from: Y. -C. Lee, J. Lee, D. Lee and S. -W. Kim, “THOR: Self-Supervised Temporal Knowledge Graph Embedding via Three-Tower Graph Convolutional Networks,” 2022 IEEE International Conference on Data Mining (ICDM), Orlando, FL, USA, 2022, pp. 1035-1040, doi: 10.1109/ICDM54844.2022.00127. Github

[2] (Joint-MTComplEx) Zhang, F., Chen, H., Shi, Y., Cheng, J., & Lin, J. (2024). Joint framework for tensor decomposition-based temporal knowledge graph completion. Information Sciences, 654, 119853.

[3] (DGTL) Liu, Z., Li, Z., Li, W., & Duan, L. (2024). Deep Graph Tensor Learning for Temporal Link Prediction. Information Sciences, 120085. Github

[4] (CRmod) Zhu, L., Chai, D., & Bai, L. (2024). CRmod: Context-Aware Rule-Guided reasoning over temporal knowledge graph. Information Sciences, 120343. Github

[5] Dai, Y., Guo, W., & Eickhoff, C. (2024). Wasserstein adversarial learning based temporal knowledge graph embedding. Information Sciences, 659, 120061.

[6] Xu, X., Jia, W., Yan, L., Lu, X., Wang, C., & Ma, Z. (2024). Spatiotemporal knowledge graph completion via diachronic and transregional word embedding. Information Sciences, 120477.

[7] Guo, J., Zhao, M., Yu, J., Yu, R., Song, J., Wang, Q., … & Yu, M. (2024). EHPR: Learning Evolutionary Hierarchy Perception Representation based on Quaternion for Temporal Knowledge Graph Completion. Information Sciences, 121409.

[8] Si, Y., Hu, X., Cheng, Q., Liu, X., Liu, S., & Huang, J. (2025). Coherence mode: Characterizing local graph structural information for temporal knowledge graph. Information Sciences, 686, 121357.

Information Fusion

[1] (MvTuckER) Wang, H., Yang, J., Yang, L. T., Gao, Y., Ding, J., Zhou, X., & Liu, H. (2024). MvTuckER: Multi-view knowledge graphs represention learning based on tensor tucker model. Information Fusion, 102249.

Information Processing & Management

[1] (STKGR-PR) Meng, X., Bai, L., Hu, J., & Zhu, L. (2024). Multi-hop path reasoning over sparse temporal knowledge graphs based on path completion and reward shaping. Information Processing & Management, 61(2), 103605. Github

[2] Ma, J., Li, K., Zhang, F., Wang, Y., Luo, X., Li, C., & Qiao, Y. (2024). TaReT: Temporal knowledge graph reasoning based on topology-aware dynamic relation graph and temporal fusion. Information Processing & Management, 61(6), 103848.

Expert Systems with Applications

[1] (CDRGN-SDE) Zhang, D., Feng, W., Wu, Z., Li, G., & Ning, B. (2024). CDRGN-SDE: Cross-Dimensional Recurrent Graph Network with neural Stochastic Differential Equation for temporal knowledge graph embedding. Expert Systems with Applications, 123295. Github

[2] (TPComplEx) Yang, J., Ying, X., Shi, Y., & Xing, B. (2024). Tensor decompositions for temporal knowledge graph completion with time perspective. Expert Systems with Applications, 237, 121267. Github

Frontiers of Computer Science

[1] (EvolveKG) Liu, J., Yu, Z., Guo, B., Deng, C., Fu, L., Wang, X., & Zhou, C. (2024). EvolveKG: a general framework to learn evolving knowledge graphs. Frontiers of Computer Science, 18(3), 183309.

Neural networks

[1] Shao, P., Tao, J., & Zhang, D. (2024). Bayesian hypernetwork collaborates with time-difference evolutional network for temporal knowledge prediction. Neural Networks, 106146.

[2] Bai, L., Li, N., Li, G., Zhang, Z., & Zhu, L. (2024). Embedding-based Entity Alignment of Cross-Lingual Temporal Knowledge Graphs. Neural Networks, 106143.

[3] 🔥 Mei, X., Yang, L., Jiang, Z., Cai, X., Gao, D., Han, J., & Pan, S. (2024). An Inductive Reasoning Model based on Interpretable Logical Rules over temporal knowledge graph. Neural Networks, 106219. Github

[4] Zhang, J., Sun, M., Huang, Q., & Tian, L. (2024). PLEASING: Exploring the historical and potential events for temporal knowledge graph reasoning. Neural Networks, 106516. Github

Engineering Applications of Artificial Intelligence

[1] Zhu, L., Zhao, W., & Bai, L. (2024). Quadruple mention text-enhanced temporal knowledge graph reasoning. Engineering Applications of Artificial Intelligence, 133, 108058. Github

Journal of Intelligent Information Systems

[1] Du, C., Li, X., & Li, Z. (2024). Semantic-enhanced reasoning question answering over temporal knowledge graphs. Journal of Intelligent Information Systems, 1-23.

Artificial Intelligence

[1] Dong, H., Wang, P., Xiao, M., Ning, Z., Wang, P., & Zhou, Y. (2024). Temporal Inductive Path Neural Network for Temporal Knowledge Graph Reasoning. Artificial Intelligence, 104085. Github

IEEE Transactions on Fuzzy Systems

[1] Ji, H., Yan, L., & Ma, Z. (2023). FSTRE: Fuzzy Spatiotemporal RDF Knowledge Graph Embedding Using Uncertain Dynamic Vector Projection and Rotation. IEEE Transactions on Fuzzy Systems.

[2] An, X., Bai, L., Zhou, L., & Song, J. (2024). Few-shot Fuzzy Temporal Knowledge Graph Completion via Fuzzy Semantics and Dynamic Attention Network. IEEE Transactions on Fuzzy Systems.

[3] Wang, C., Yan, L., & Ma, Z. (2024). Fuzzy Event Knowledge Graph Embedding Through Event Temporal and Causal Transfer. IEEE Transactions on Fuzzy Systems.

Electronics

[1] 🔥 Xu, H., Bao, J., Li, H., He, C., & Chen, F. (2024). A Multi-View Temporal Knowledge Graph Reasoning Framework with Interpretable Logic Rules and Feature Fusion. Electronics, 13(4), 742.

[2] Liu, Y., Shen, Y., & Dai, Y. (2024). Enhancing Temporal Knowledge Graph Representation with Curriculum Learning. Electronics, 13(17), 3397.

Neurocomputing

[1] He, M., Zhu, L., & Bai, L. (2024). ConvTKG: A query-aware convolutional neural network-based embedding model for temporal knowledge graph completion. Neurocomputing, 127680.

IEEE TKDE

[1] Zhang, F., Zhang, Z., Zhuang, F., Zhao, Y., Wang, D., & Zheng, H. (2024). Temporal Knowledge Graph Reasoning With Dynamic Memory Enhancement. IEEE Transactions on Knowledge and Data Engineering.

Tsinghua Science and Technology

[1] Han, Y., Lu, G., Zhang, S., Zhang, L., Zou, C., & Wen, G. (2024). A Temporal Knowledge Graph Embedding Model Based on Variable Translation. Tsinghua Science and Technology, 29(5), 1554-1565.

Applied Soft Computing

[1] Bai, L., Chen, M., & Xiao, Q. (2024). Multi-Hop Temporal Knowledge Graph Reasoning with Multi-Agent Reinforcement Learning. Applied Soft Computing, 111727. Github

IEEE Transactions on Cybernetics

[1] Wang, J., Wang, B., Gao, J., Pan, S., Liu, T., Yin, B., & Gao, W. (2024). MADE: Multicurvature Adaptive Embedding for Temporal Knowledge Graph Completion. IEEE Transactions on Cybernetics.

IEEE Transaction on AI

[1] Yang, J., Huang, C., Yang, X., Yang, L. T., Gao, Y., & Liu, C. (2024). Temporal Knowledge Extrapolation Based on Fine-grained Tensor Graph Attention Network for Responsible AI. IEEE Transactions on Artificial Intelligence.

IAENG International Journal of Computer Science

[1] Huang, C., & Zhong, Y. (2024). A Novel Approach for Representing Temporal Knowledge Graphs. IAENG International Journal of Computer Science, 51(6).

Information Systems

[1] Jia, W., Ma, R., Niu, W., Yan, L., & Ma, Z. (2024). SFTe: Temporal Knowledge Graphs Embedding for Future Interaction Prediction. Information Systems, 102423.

2023

Semantic Web Journal

[1] (TRKGE) Song, B., Amouzouvi, K., Xu, C., Wang, M., Lehmann, J., & Vahdati, S. Temporal Relevance for Representing Learning over Temporal Knowledge Graphs.

Expert Systems with Applications

[1] (TPRG) Bai, L., Chen, M., Zhu, L., & Meng, X. (2023). Multi-hop temporal knowledge graph reasoning with temporal path rules guidance. Expert Systems with Applications, 223, 119804. Github

The Journal of Supercomputing

[1] (TKGA) Wang, Z., You, X., & Lv, X. (2023). A relation enhanced model for temporal knowledge graph alignment. The Journal of Supercomputing, 1-23.

Information Systems

[1] (RITI) Liu, R., Yin, G., Liu, Z., & Tian, Y. (2023). Reinforcement learning with time intervals for temporal knowledge graph reasoning. Information Systems, 102292.

Information Sciences

[1] (T-GAE) Hou, X., Ma, R., Yan, L., & Ma, Z. (2023). T-GAE: A Timespan-Aware Graph Attention-based Embedding Model for Temporal Knowledge Graph Completion. Information Sciences, 119225.

[2] (TASTER) Wang, X., Lyu, S., Wang, X., Wu, X., & Chen, H. (2023). Temporal knowledge graph embedding via sparse transfer matrix. Information Sciences, 623, 56-69.

[3] (TLmod) Bai, L., Yu, W., Chai, D., Zhao, W., & Chen, M. (2023). Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules. Information Sciences, 621, 22-35.

IEEE/ACM Transactions on Audio, Speech, and Language Processing

[1] (TARGAT) Xie, Z., Zhu, R., Liu, J., Zhou, G., & Huang, J. X. (2023). TARGAT: A Time-Aware Relational Graph Attention Model for Temporal Knowledge Graph Embedding. IEEE/ACM Transactions on Audio, Speech, and Language Processing.

Applied Intelligence

[1] (TBDRI) Yu, M., Guo, J., Yu, J., Xu, T., Zhao, M., Liu, H., … & Yu, R. (2023). TBDRI: block decomposition based on relational interaction for temporal knowledge graph completion. Applied Intelligence, 53(5), 5072-5084.

[2] (GLANet) Wang, J., Lin, X., Huang, H., Ke, X., Wu, R., You, C., & Guo, K. (2023). GLANet: temporal knowledge graph completion based on global and local information-aware network. Applied Intelligence, 1-17.

[3] (ChronoR-CP) Li, M., Sun, Z., Zhang, W., & Liu, W. (2023). Leveraging semantic property for temporal knowledge graph completion. Applied Intelligence, 53(8), 9247-9260.

[4] (TIAR) Mu, C., Zhang, L., Ma, Y., & Tian, L. (2023). Temporal knowledge subgraph inference based on time-aware relation representation. Applied Intelligence, 53(20), 24237-24252.

[5] (TNTSimplE) He, P., Zhou, G., Zhang, M., Wei, J., & Chen, J. (2023). Improving temporal knowledge graph embedding using tensor factorization. Applied Intelligence, 53(8), 8746-8760.

Neural Networks

[1] (TFSC) Zhang, H., & Bai, L. (2023). Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism. Neural Networks, 161, 371-381. Github

[2] Shao, P., Liu, T., Che, F., Zhang, D., & Tao, J. (2023). Adaptive pseudo-Siamese policy network for temporal knowledge prediction. Neural Networks.

Neurocomputing

[1] Shao, P., He, J., Li, G., Zhang, D., & Tao, J. (2023). Hierarchical Graph Attention Network for Temporal Knowledge Graph Reasoning. Neurocomputing, 126390.

[2] (TANGO) Wang, Z., Ding, D., Ren, M., & Conti, M. (2023). TANGO: A Temporal Spatial Dynamic Graph Model for Event Prediction. Neurocomputing, 126249.

IEEE Transactions on Neural Networks and Learning Systems

[1] (QDN) Wang, J., Wang, B., Gao, J., Li, X., Hu, Y., & Yin, B. (2023). QDN: A Quadruplet Distributor Network for Temporal Knowledge Graph Completion. IEEE Transactions on Neural Networks and Learning Systems. Github

Journal of Systems Science and Systems Engineering

[1] Yan, Z., & Tang, X. (2023). Narrative Graph: Telling Evolving Stories Based on Event-centric Temporal Knowledge Graph. Journal of Systems Science and Systems Engineering, 32(2), 206-221.

Engineering Applications of Artificial Intelligence

[1] (RoAN) Bai, L., Ma, X., Meng, X., Ren, X., & Ke, Y. (2023). RoAN: A relation-oriented attention network for temporal knowledge graph completion. Engineering Applications of Artificial Intelligence, 123, 106308. Github

Future Generation Computer Systems

[1] (TAL-TKGC) Nie, H., Zhao, X., Yao, X., Jiang, Q., Bi, X., Ma, Y., & Sun, Y. (2023). Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion. Future Generation Computer Systems.

Cognitive Computation

[2] (MsCNN) Liu, W., Wang, P., Zhang, Z., & Liu, Q. (2023). Multi-Scale Convolutional Neural Network for Temporal Knowledge Graph Completion. Cognitive Computation, 1-7.

ACM Transactions on Knowledge Discovery from Data

[1] (DuCape) Zhang, S., Liang, X., Tang, H., Zheng, X., Zhang, A. X., & Ma, Y. DuCape: Dual Quaternion and Capsule Network Based Temporal Knowledge Graph Embedding. ACM Transactions on Knowledge Discovery from Data.

IEEE Transactions on Knowledge and Data Engineering

[1] Li, Y., Chen, H., Li, Y., Li, L., Philip, S. Y., & Xu, G. (2023). Reinforcement Learning based Path Exploration for Sequential Explainable Recommendation. IEEE Transactions on Knowledge and Data Engineering. Github

Knowledge-Based Systems

[1] (RLAT) Bai, L., Chai, D., & Zhu, L. (2023). RLAT: Multi-hop temporal knowledge graph reasoning based on Reinforcement Learning and Attention Mechanism. Knowledge-Based Systems, 269, 110514.

[2] Luo, X., Zhu, A., Zhang, J., & Shao, J. (2024). HierarT: Multi-hop temporal knowledge graph forecasting with hierarchical reinforcement learning. Knowledge-Based Systems, 112164.

Journal of Computational Design and Engineering

[1] (MetaRT) Zhu, L., Xing, Y., Bai, L., & Chen, X. (2023). Few-shot link prediction with meta-learning for temporal knowledge graphs. Journal of Computational Design and Engineering, 10(2), 711-721.

Entropy

[1] 🔥 (IMF) Du, Z., Qu, L., Liang, Z., Huang, K., Cui, L., & Gao, Z. (2023). IMF: Interpretable Multi-Hop Forecasting on Temporal Knowledge Graphs. Entropy, 25(4), 666. Github

Complex & Intelligent Systems

[1] (FTMO) Zhu, L., Bai, L., Han, S., & Zhang, M. (2023). Few-shot temporal knowledge graph completion based on meta-optimization. Complex & Intelligent Systems, 9(6), 7461-7474. Github

World Wide Web

[1] (FTMF) Bai, L., Zhang, M., Zhang, H., & Zhang, H. (2023). FTMF: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism. World Wide Web, 26(3), 1243-1270. Github

DMKD

[1] (OSLT) Ma, R., Mei, B., Ma, Y., Zhang, H., Liu, M., & Zhao, L. (2023). One-shot relational learning for extrapolation reasoning on temporal knowledge graphs. Data Mining and Knowledge Discovery, 1-18.

2022

Knowledge-Based Systems

[1] (EvoExplore) Jiasheng Zhang, Shuang Liang, Yongpan Sheng, Jie Shao. “Temporal knowledge graph representation learning with local and global evolutions”. Knowledge-Based Systems 2022. Github

[2] (TuckERT) Pengpeng Shao, Dawei Zhang, Guohua Yang, Jianhua Tao, Feihu Che, Tong Liu. “Tucker decomposition-based temporal knowledge graph completion”. Knowledge Based Systems 2022. Github

Expert Systems with Applications

[1] (BTDG) Yujing Lai, Chuan Chen, Zibin Zheng, Yangqing Zhang. “Block term decomposition with distinct time granularities for temporal knowledge graph completion”. Expert Systems with Applications 2022. Github

2021

Applied Soft Computing

[1] (TPath) Luyi Bai, Wenting Yu, Mingzhuo Chen, Xiangnan Ma. “Multi-hop reasoning over paths in temporal knowledge graphs using reinforcement learning”. Applied Soft Computing 2021.

TKDD

[1] (TPmod) Bai, L., Ma, X., Zhang, M., & Yu, W. (2021). Tpmod: A tendency-guided prediction model for temporal knowledge graph completion. ACM Transactions on Knowledge Discovery from Data, 15(3), 1-17. Github

[2] (Dacha) Chen, L., Tang, X., Chen, W., Qian, Y., Li, Y., & Zhang, Y. (2021). Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation. ACM Transactions on Knowledge Discovery from Data (TKDD), 16(3), 1-18.

2020

IEEE Access

[1] (TDG2E) Tang, X., Yuan, R., Li, Q., Wang, T., Yang, H., Cai, Y., & Song, H. (2020). Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution. IEEE Access, 8, 6849-6860.

[2] (3DRTE) Wang, J., Zhang, W., Chen, X., Lei, J., & Lai, X. (2020). 3drte: 3d rotation embedding in temporal knowledge graph. IEEE Access, 8, 207515-207523.

2019

Journal of Web Semantics

[1] (ConT) Ma, Y., Tresp, V., & Daxberger, E. A. (2019). Embedding models for episodic knowledge graphs. Journal of Web Semantics, 59, 100490.