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Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry.
G. Wan, S. Pan, C. Gong, C. Zhou, and G. Haffari, “Reasoning like human: Hierarchical reinforcement learning for knowledge graph reasoning,” in Proceedings of the International Joint Conference on Artificial Intelligence , 2020, pp. 1926–1932
1932
Earlier work this paper cites.
C. Brezinski, “A general extrapolation algorithm,” Numerische Mathematik , vol. 35, pp. 175–187, 1980
1980
Earlier work this paper cites.
F. M. Gardner, “Interpolation in digital modems. i. fundamentals,” IEEE Transactions on communications , vol. 41, no. 3, pp. 501–507, 1993
1993
Earlier work this paper cites.
E. Salvat and M.-L. Mugnier, “Sound and complete forward and backward chainings of graph rules,” in International Conference on Conceptual Structures . Springer, 1996, pp. 248–262
1996
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural Computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
L. R. Medsker and L. Jain, “Recurrent neural networks,” Design and Applications , vol. 5, no. 64-67, p. 2, 2001
2001
Earlier work this paper cites.
M. L. De Campos, S. Werner, and J. A. Apolinário, “Constrained adaptation algorithms employing householder transformation,” IEEE Transactions on Signal Processing , vol. 50, no. 9, pp. 2187–2195, 2002
2002
Earlier work this paper cites.
A. Lunardi, Interpolation theory . Springer, 2009, vol. 9
2009
Earlier work this paper cites.
S. S. Dasgupta, S. N. Ray, and P. P. Talukdar, “HyTE: Hyperplane-based temporally aware knowledge graph embedding.” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2018, pp. 2001–2011
2011
Earlier work this paper cites.
K. Leetaru and P. A. Schrodt, “Gdelt: Global data on events, location, and tone, 1979–2012,” in the International Studies Association Annual Convention , vol. 2, no. 4, 2013, pp. 1–49
2013
Earlier work this paper cites.
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Advances in Neural Information Processing Systems , 2013, pp. 2787–2795
2013
Earlier work this paper cites.
C. Brezinski and M. R. Zaglia, Extrapolation methods: theory and practice . Elsevier, 2013
2013
Earlier work this paper cites.
J. Dalton, L. Dietz, and J. Allan, “Entity query feature expansion using knowledge base links,” in Proceedings of the International ACM SIGIR Conference on Research & Development in Information Retrieval , 2014, pp. 365–374
2014
Earlier work this paper cites.
F. Erxleben, M. Günther, M. Krötzsch, J. Mendez, and D. Vrandečić, “Introducing wikidata to the linked data web,” in Proceedings of the International Semantic Web Conference , 2014, pp. 50–65
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Ma, P. A. Crook, R. Sarikaya, and E. Fosler-Lussier, “Knowledge graph inference for spoken dialog systems,” in International Conference on Acoustics, Speech and Signal Processing , 2015, pp. 5346–5350
2015
Earlier work this paper cites.
E. Boschee, J. Lautenschlager, S. O’Brien, S. Shellman, J. Starz, and M. Ward, “Icews coded event data,” in Harvard Dataverse , 2015
2015
Earlier work this paper cites.
F. Mahdisoltani, J. Biega, and F. Suchanek, “YAGO3: A knowledge base from multilingual wikipedias,” in Conference on Innovative Data Systems Research , 2015
2015
Earlier work this paper cites.
B. Yang, W. T. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in International Conference on Learning Representations , 2015, pp. 1–13
2015
Earlier work this paper cites.
Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2015, pp. 2181–2187
2015
Earlier work this paper cites.
T. Jiang, T. Liu, T. Ge, L. Sha, B. Chang, S. Li, and Z. Sui, “Towards time-aware knowledge graph completion,” in International Conference on Computational Linguistics: Technical Papers , 2016, pp. 1715–1724
2016
Earlier work this paper cites.
M. Chekol, G. Pirrò, J. Schoenfisch, and H. Stuckenschmidt, “Marrying uncertainty and time in knowledge graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 31, no. 1, 2017
2017
Earlier work this paper cites.
F. Yang, Z. Yang, and W. W. Cohen, “Differentiable learning of logical rules for knowledge base reasoning,” Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
R. Trivedi, H. Dai, Y. Wang, and L. Song, “Know-evolve: Deep temporal reasoning for dynamic knowledge graphs,” in the International Conference on Machine Learning , 2017, pp. 3462–3471
2017
Earlier work this paper cites.
C. Finn, P. Abbeel, and S. Levine, “Model-agnostic meta-learning for fast adaptation of deep networks,” in the International Conference on Machine Learning . PMLR, 2017, pp. 1126–1135
2017
Earlier work this paper cites.
A. García-Durán, S. Dumančić, and M. Niepert, “Learning sequence encoders for temporal knowledge graph completion,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2018, pp. 4816–4821
2018
Earlier work this paper cites.
J. Leblay and M. W. Chekol, “Deriving validity time in knowledge graph,” in International World Wide Web Conference , 2018, pp. 1771–1776
2018
Earlier work this paper cites.
T. Lacroix, N. Usunier, and G. Obozinski, “Canonical tensor decomposition for knowledge base completion,” in International Conference on Machine Learning , 2018, pp. 2863–2872
2018
Earlier work this paper cites.
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2D knowledge graph embeddings,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2018, pp. 1811–1818
2018
Earlier work this paper cites.
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in Proceedings of the International Semantic Web Conference , 2018, pp. 593–607
2018
Earlier work this paper cites.
R. T. Chen, Y. Rubanova, J. Bettencourt, and D. K. Duvenaud, “Neural ordinary differential equations,” Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
W. Zhang, T. Gu, W. Sun, Y. Phatpicha, L. Chang, and C. Bin, “Travel attractions recommendation with travel spatial-temporal knowledge graphs,” in the International Conference of Pioneering Computer Scientists, Engineers and Educators , 2018, pp. 213–226
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
I. Balažević, C. Allen, and T. M. Hospedales, “Tucker: Tensor factorization for knowledge graph completion,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2019, pp. 5185–5194
2019
Earlier work this paper cites.
Z. Sun, Z.-H. Deng, J.-Y. Nie, and J. Tang, “RotatE: Knowledge graph embedding by relational rotation in complex space,” in International Conference on Learning Representations , 2019, pp. 1–18
2019
Earlier work this paper cites.
Y. Wang, Y. Qiao, J. Ma, G. Hu, C. Zhang, A. K. Sangaiah, H. Zhang, and K. Ren, “A novel time constraint-based approach for knowledge graph conflict resolution,” Applied Sciences , vol. 9, no. 20, p. 4399, 2019
2019
Earlier work this paper cites.
T. Vu, T. D. Nguyen, D. Q. Nguyen, D. Phung et al. , “A capsule network-based embedding model for knowledge graph completion and search personalization,” in Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics , 2019, pp. 2180–2189
2019
Earlier work this paper cites.
A. Sadeghian, M. Armandpour, P. Ding, and D. Z. Wang, “Drum: End-to-end differentiable rule mining on knowledge graphs,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Earlier work this paper cites.
C. Li and M. Qiu, Reinforcement learning for cyber-physical systems: with cybersecurity case studies . Chapman and Hall/CRC, 2019
2019
Earlier work this paper cites.
R. Goel, S. M. Kazemi, M. Brubaker, and P. Poupart, “Diachronic embedding for temporal knowledge graph completion,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2020, pp. 3988–3995
2020
Earlier work this paper cites.
Z. Yang, M. Ding, C. Zhou, H. Yang, J. Zhou, and J. Tang, “Understanding negative sampling in graph representation learning,” in Proceedings of the International Conference on Knowledge Discovery & Data Mining , 2020, pp. 1666–1676
2020
Earlier work this paper cites.
T. Lacroix, G. Obozinski, and N. Usunier, “Tensor decompositions for temporal knowledge base completion,” in International Conference on Learning Representations , 2020, pp. 1–12
2020
Earlier work this paper cites.
R. Ni, Z. Ma, K. Yu, and X. Xu, “Specific time embedding for temporal knowledge graph completion,” in International Conference on Cognitive Informatics & Cognitive Computing , 2020, pp. 105–110
2020
Earlier work this paper cites.
L. Lin and K. She, “Tensor decomposition-based temporal knowledge graph embedding,” in International Conference on Tools with Artificial Intelligence , 2020, pp. 969–975
2020
Earlier work this paper cites.
J. Zhang, Y. Sheng, Z. Wang, and J. Shao, “TKGFrame: a two-phase framework for temporal-aware knowledge graph completion,” in Web and Big Data: International Joint Conference, APWeb-WAIM , 2020, pp. 196–211
2020
Earlier work this paper cites.
C. Xu, M. Nayyeri, F. Alkhoury, H. Yazdi, and J. Lehmann, “Temporal knowledge graph completion based on time series Gaussian embedding,” in Proceedings of the International Semantic Web Conference , 2020, pp. 654–671
2020
Earlier work this paper cites.
R. Abboud, I. Ceylan, T. Lukasiewicz, and T. Salvatori, “BoxE: A box embedding model for knowledge base completion,” Advances in Neural Information Processing Systems , vol. 33, pp. 9649–9661, 2020
2020
Earlier work this paper cites.
S. Amin, S. Varanasi, K. A. Dunfield, and G. Neumann, “Lowfer: Low-rank bilinear pooling for link prediction,” in International Conference on Machine Learning , 2020, pp. 257–268
2020
Earlier work this paper cites.
C. Xu, M. Nayyeri, F. Alkhoury, H. S. Yazdi, and J. Lehmann, “TeRo: A time-aware knowledge graph embedding via temporal rotation,” in International Conference on Computational Linguistics , 2020, pp. 1583–1593
2020
Earlier work this paper cites.
Z. Han, Y. Ma, P. Chen, and V. Tresp, “Dyernie: Dynamic evolution of riemannian manifold embeddings for temporal knowledge graph completion,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2020, pp. 7301–7316
2020
Earlier work this paper cites.
O. Skopek, O.-E. Ganea, and G. Bécigneul, “Mixed-curvature variational autoencoders,” in International Conference on Learning Representations , 2020
2020
Earlier work this paper cites.
I. Chami, A. Wolf, D. C. Juan, F. Sala, S. Ravi, and C. Ré, “Low-dimensional hyperbolic knowledge graph embeddings,” in Annual Meeting of the Association for Computational Linguistics , 2020, p. 6901–6914
2020
Earlier work this paper cites.
J. Leblay, M. W. Chekol, and X. Liu, “Towards temporal knowledge graph embeddings with arbitrary time precision,” in Proceedings of the ACM International Conference on Information & Knowledge Management , 2020, pp. 685–694
2020
Cited alongside, same era.
X. Tang, R. Yuan, Q. Li, T. Wang, H. Yang, Y. Cai, and H. Song, “Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution,” IEEE Access , vol. 8, pp. 6849–6860, 2020
2020
Cited alongside, same era.
S. Deng, H. Rangwala, and Y. Ning, “Dynamic knowledge graph based multi-event forecasting,” in Proceedings of the ACM SIGKDD Conference on Knowledge Discovery and Data Mining , 2020, pp. 1585–1595
2020
Cited alongside, same era.
J. Wu, M. Cao, J. C. K. Cheung, and W. L. Hamilton, “Temp: Temporal message passing for temporal knowledge graph completion,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2020, pp. 5730–5746
2020
Cited alongside, same era.
Y. Liu, Y. Ma, M. Hildebrandt, M. Joblin, and V. Tresp, “Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence , 2022, pp. 4120–4127
2022
Later among the works it cites.
2022
Later among the works it cites.
Y. Li, S. Sun, and J. Zhao, “TiRGN: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning,” in Proceedings of the International Joint Conference on Artificial Intelligence , 2022, pp. 2152–2158
2022
Later among the works it cites.
Z. Li, Z. Hou, S. Guan, X. Jin, W. Peng, L. Bai, Y. Lyu, W. Li, J. Guo, and X. Cheng, “HiSMatch: Historical structure matching based temporal knowledge graph reasoning,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2022, pp. 7328–7338
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2020
Cited alongside, same era.
2020
Cited alongside, same era.
T. Souza Costa, S. Gottschalk, and E. Demidova, “Event-QA: A dataset for event-centric question answering over knowledge graphs,” in Proceedings of the ACM International Conference on Information & Knowledge Management , 2020, pp. 3157–3164
2020
Cited alongside, same era.
F. Song, B. Wang, Y. Tang, and J. Sun, “Research of medical aided diagnosis system based on temporal knowledge graph,” in the International Conference on Advanced Data Mining and Applications , 2020, pp. 236–250
2020
Cited alongside, same era.
C. Yang, W. Li, X. Zhang, R. Zhang, and G. Qi, “A temporal semantic search system for traditional chinese medicine based on temporal knowledge graphs,” in the Joint International Conference on Semantic Technology . Springer, 2020, pp. 13–20
2020
Cited alongside, same era.
L. Zhao, H. Deng, L. Qiu, S. Li, Z. Hou, H. Sun, and Y. Chen, “Urban multi-source spatio-temporal data analysis aware knowledge graph embedding,” Symmetry , vol. 12, no. 2, p. 199, 2020
2020
Cited alongside, same era.
C. Xiao, L. Sun, and W. Ji, “Temporal knowledge graph incremental construction model for recommendation,” in International Joint Conference on Web and Big Data , 2020, pp. 352–359
2020
Cited alongside, same era.
S. Ma, A. Li, X. Zhao, and Y. Song, “Learning bilstm-based embeddings for relation prediction in temporal knowledge graph,” in Journal of Physics: Conference Series , vol. 1871, no. 1, 2021, p. 012050
2021
Cited alongside, same era.
2022
Later among the works it cites.
Z. Wang, H. Du, Q. Yao, and X. Li, “Search to pass messages for temporal knowledge graph completion,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2022, pp. 6160–6172
2022
Later among the works it cites.
N. Park, F. Liu, P. Mehta, D. Cristofor, C. Faloutsos, and Y. Dong, “EvoKG: Jointly modeling event time and network structure for reasoning over temporal knowledge graphs,” in Proceedings of the ACM International Conference on Web Search and Data Mining , 2022, pp. 794–803
2022
Later among the works it cites.
K. Liu, F. Zhao, H. Chen, Y. Li, G. Xu, and H. Jin, “DA-Net: Distributed attention network for temporal knowledge graph reasoning,” in Proceedings of the ACM International Conference on Information & Knowledge Management , 2022, pp. 1289–1298
2022
Later among the works it cites.
H. Duan, H. Jin, K. Chen, S. Du, T. Fang, and H. Huo, “An effective time-aware encoder for temporal knowledge graph reasoning,” in Proceedings of the International Conference on Machine Learning and Natural Language Processing , 2022, pp. 81–87
2022
Later among the works it cites.
J. Zhang, S. Liang, Y. Sheng, and J. Shao, “Temporal knowledge graph representation learning with local and global evolutions,” Knowledge-Based Systems , vol. 251, p. 109234, 2022
2022
Later among the works it cites.
S. Wang, X. Cai, Y. Zhang, and X. Yuan, “CRNet: Modeling concurrent events over temporal knowledge graph,” in Proceedings of the International Semantic Web Conference , 2022, pp. 516–533
2022
Later among the works it cites.
Y. Gao, L. Feng, Z. Kan, Y. Han, L. Qiao, and D. Li, “Modeling precursors for temporal knowledge graph reasoning via auto-encoder structure,” in Proceedings of the International Joint Conference on Artificial Intelligence , 2022, pp. 23–29
2022
Later among the works it cites.
H. Sun, S. Geng, J. Zhong, H. Hu, and K. He, “Graph hawkes transformer for extrapolated reasoning on temporal knowledge graphs,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing , 2022, pp. 7481–7493
2022
Later among the works it cites.
L. Bai, M. Zhang, H. Zhang, and H. Zhang, “Ftmf: Few-shot temporal knowledge graph completion based on meta-optimization and fault-tolerant mechanism,” International World Wide Web Conference , pp. 1–28, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
R. Wang, Z. Li, D. Sun, S. Liu, J. Li, B. Yin, and T. Abdelzaher, “Learning to sample and aggregate: Few-shot reasoning over temporal knowledge graphs,” Advances in Neural Information Processing Systems , vol. 35, pp. 16 863–16 876, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
C. Mavromatis, P. L. Subramanyam, V. N. Ioannidis, A. Adeshina, P. R. Howard, T. Grinberg, N. Hakim, and G. Karypis, “Tempoqr: temporal question reasoning over knowledge graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 36, no. 5, 2022, pp. 5825–5833
2022
Later among the works it cites.
W. Chen, H. Wan, S. Guo, H. Huang, S. Zheng, J. Li, S. Lin, and Y. Lin, “Building and exploiting spatial–temporal knowledge graph for next poi recommendation,” Knowledge-Based Systems , vol. 258, p. 109951, 2022
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Wang, B. Wang, J. Gao, X. Li, Y. Hu, and B. Yin, “QDN: A quadruplet distributor network for temporal knowledge graph completion,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Closest in time.
M. Yu, J. Guo, J. Yu, T. Xu, M. Zhao, H. Liu, X. Li, and R. Yu, “Tbdri: block decomposition based on relational interaction for temporal knowledge graph completion,” Applied Intelligence , vol. 53, no. 5, pp. 5072–5084, 2023
2023
Closest in time.
X. Zhao, A. Li, R. Jiang, K. Chen, and Z. Peng, “Householder transformation-based temporal knowledge graph reasoning,” Electronics , vol. 12, no. 9, p. 2001, 2023
2023
Closest in time.
J. Wang, B. Wang, J. Gao, Y. Hu, and B. Yin, “Multi-concept representation learning for knowledge graph completion,” ACM Transactions on Knowledge Discovery from Data , vol. 17, no. 1, pp. 1–19, 2023
2023
Closest in time.
X. Wang, S. Lyu, X. Wang, X. Wu, and H. Chen, “Temporal knowledge graph embedding via sparse transfer matrix,” Information Sciences , vol. 623, pp. 56–69, 2023
2023
Closest in time.
B. Xiong, M. Nayyeri, S. Pan, and S. Staab, “Shrinking embeddings for hyper-relational knowledge graphs,” in Annual Meeting of the Association for Computational Linguistics , 2023, pp. 1–13
2023
Closest in time.
C. Xu, M. Nayyeri, Y.-Y. Chen, and J. Lehmann, “Geometric algebra based embeddings for static and temporal knowledge graph completion,” IEEE Transactions on Knowledge and Data Engineering , vol. 35, no. 5, pp. 4838–4851, 2023
2023
Closest in time.
S. Zhang, X. Liang, Z. Li, J. Feng, X. Zheng, and B. Wu, “Biqcap: A biquaternion and capsule network-based embedding model for temporal knowledge graph completion,” in Database Systems for Advanced Applications , 2023, pp. 673–688
2023
Closest in time.
J. Wang, B. Wang, J. Gao, X. Li, Y. Hu, and B. Yin, “Tdn: Triplet distributor network for knowledge graph completion,” IEEE Transactions on Knowledge and Data Engineering , 2023
2023
Closest in time.
T. Li, W. Wang, X. Li, T. Wang, X. Zhou, and M. Huang, “Embedding uncertain temporal knowledge graphs,” Mathematics , vol. 11, no. 3, p. 775, 2023
2023
Closest in time.
J. Ma, C. Zhou, Y. Chen, Y. Wang, G. Hu, and Y. Qiao, “Tecre: A novel temporal conflict resolution method based on temporal knowledge graph embedding,” Information , vol. 14, no. 3, p. 155, 2023
2023
Closest in time.
L. Bai, X. Ma, X. Meng, X. Ren, and Y. Ke, “RoAN: A relation-oriented attention network for temporal knowledge graph completion,” Engineering Applications of Artificial Intelligence , vol. 123, p. 106308, 2023
2023
Closest in time.
H. Nie, X. Zhao, X. Yao, Q. Jiang, X. Bi, Y. Ma, and Y. Sun, “Temporal-structural importance weighted graph convolutional network for temporal knowledge graph completion,” Future Generation Computer Systems , vol. 143, pp. 30–39, 2023
2023
Closest in time.
Z. Du, L. Qu, Z. Liang, K. Huang, L. Cui, and Z. Gao, “IMF: Interpretable multi-hop forecasting on temporal knowledge graphs,” Entropy , vol. 25, no. 4, p. 666, 2023
2023
Closest in time.
L. Bai, M. Chen, L. Zhu, and X. Meng, “Multi-hop temporal knowledge graph reasoning with temporal path rules guidance,” Expert Systems with Applications , vol. 223, p. 119804, 2023
2023
Closest in time.
L. Bai, W. Yu, D. Chai, W. Zhao, and M. Chen, “Temporal knowledge graphs reasoning with iterative guidance by temporal logical rules,” Information Sciences , vol. 621, pp. 22–35, 2023
2023
Closest in time.
S. Xiong, Y. Yang, F. Fekri, and J. C. Kerce, “TILP: Differentiable learning of temporal logical rules on knowledge graphs,” in the International Conference on Learning Representations , 2023
2023
Closest in time.
M. Zhang, Y. Xia, Q. Liu, S. Wu, and L. Wang, “Learning long- and short-term representations for temporal knowledge graph reasoning,” in International World Wide Web Conference , 2023, pp. 2412–2422
2023
Closest in time.
Y. Xu, J. Ou, H. Xu, and L. Fu, “Temporal knowledge graph reasoning with historical contrastive learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 37, no. 4, 2023, pp. 4765–4773
2023
Closest in time.
R. Wang, Z. Li, J. Yang, T. Cao, C. Zhang, B. Yin, and T. Abdelzaher, “Mutually-paced knowledge distillation for cross-lingual temporal knowledge graph reasoning,” in Proceedings of the ACM Web Conference , 2023, pp. 2621–2632
2023
Closest in time.
X. Ren, L. Bai, Q. Xiao, and X. Meng, “Hierarchical self-attention embedding for temporal knowledge graph completion,” in International World Wide Web Conference , 2023, pp. 2539–2547
2023
Closest in time.
L. Luo, Y.-F. Li, G. Haffari, and S. Pan, “Normalizing flow-based neural process for few-shot knowledge graph completion,” in Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval , 2023
2023
Closest in time.
L. Luo, G. Haffari, and S. Pan, “Graph sequential neural ode process for link prediction on dynamic and sparse graphs,” in Proceedings of the ACM International Conference on Web Search and Data Mining , 2023, pp. 778–786
2023
Closest in time.
H. Zhang and L. Bai, “Few-shot link prediction for temporal knowledge graphs based on time-aware translation and attention mechanism,” Neural Networks , vol. 161, pp. 371–381, 2023
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
L. Bai, D. Chai, and L. Zhu, “RLAT: multi-hop temporal knowledge graph reasoning based on reinforcement learning and attention mechanism,” Knowledge-Based Systems , vol. 269, p. 110514, 2023
2023
Closest in time.
R. Ong, J. Sun, O. Șerban, and Y.-K. Guo, “TKGQA Dataset: Using question answering to guide and validate the evolution of temporal knowledge graph,” Data , vol. 8, no. 3, p. 61, 2023
2023
Closest in time.
S. Jiao, Z. Zhu, W. Wu, Z. Zuo, J. Qi, W. Wang, G. Zhang, and P. Liu, “An improving reasoning network for complex question answering over temporal knowledge graphs,” Applied Intelligence , vol. 53, no. 7, pp. 8195–8208, 2023
2023
Closest in time.
2023
Closest in time.
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in International Conference on Machine Learning , 2016, pp. 2071–2080
2080
Closest in time.