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Knowledge graph reasoning (KGR), aiming to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs), has become a fast-growing research direction.
R. H. Richens, “Preprogramming for mechanical translation.” Mech. Transl. Comput. Linguistics , 1956
1956
Earlier work this paper cites.
R. J. Rummel, “Dimensionality of nations project,” Tech. Rep., 1968
1968
Earlier work this paper cites.
W. W. Denham, “The detection of patterns in alyawara nonverbal behavior,” Ph.D. dissertation, 1973
1973
Earlier work this paper cites.
G. A. Miller, WordNet: An electronic lexical database . MIT press, 1998
1998
Earlier work this paper cites.
A. T. McCray, “An upper-level ontology for the biomedical domain,” Comparative and functional genomics , 2003
2003
Earlier work this paper cites.
O. Bodenreider, “The unified medical language system (umls): integrating biomedical terminology,” Nucleic acids research , 2004
2004
Earlier work this paper cites.
S. Kok and P. Domingos, “Statistical predicate invention,” in Proc. of ICML , 2007
2007
Earlier work this paper cites.
S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. G. Ives, “Dbpedia: A nucleus for a web of open data,” in ISWC/ASWC , 2007
2007
Earlier work this paper cites.
M. Fabian, K. Gjergji, W. Gerhard et al. , “Yago: A core of semantic knowledge unifying wordnet and wikipedia,” in Proc. of WWW , 2007
2007
Earlier work this paper cites.
K. D. Bollacker, C. Evans, P. K. Paritosh, T. Sturge, and J. Taylor, “Freebase: a collaboratively created graph database for structuring human knowledge,” in SIGMOD Conference , 2008
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in Proc. of CVPR , 2009
2009
Earlier work this paper cites.
N. Lao and W. W. Cohen, “Relational retrieval using a combination of path-constrained random walks,” Machine learning , 2010
2010
Earlier work this paper cites.
G. F. Lawler and V. Limic, Random walk: a modern introduction . Cambridge University Press, 2010
2010
Earlier work this paper cites.
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. Hruschka, and T. M. Mitchell, “Toward an architecture for never-ending language learning,” in Proc. of AAAI , 2010
2010
Earlier work this paper cites.
M. Nickel, V. Tresp, and H.-P. Kriegel, “A three-way model for collective learning on multi-relational data,” in ICML , 2011
2011
Earlier work this paper cites.
S. Kapetanakis, G. Samakovitis, B. Gunasekara, and M. Petridis, “Monitoring financial transaction fraud with the use of case-based reasoning,” 2012
2012
Earlier work this paper cites.
A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” Proc. of NeurIPS , 2013
2013
Earlier work this paper cites.
R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base completion,” Proc. of NeurIPS , 2013
2013
Earlier work this paper cites.
L. A. Galárraga, C. Teflioudi, K. Hose, and F. Suchanek, “Amie: association rule mining under incomplete evidence in ontological knowledge bases,” in Proc. of WWW , 2013
2013
Earlier work this paper cites.
R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base completion,” Proc. of NeurIPS , 2013
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,” Proc. of NeurIPS , 2013
2013
Earlier work this paper cites.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Proc. of AAAI , 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
A. Bordes, X. Glorot, J. Weston, and Y. Bengio, “A semantic matching energy function for learning with multi-relational data,” Machine Learning , 2014
2014
Earlier work this paper cites.
M. Gardner, P. Talukdar, J. Krishnamurthy, and T. Mitchell, “Incorporating vector space similarity in random walk inference over knowledge bases,” in Proc. of EMNLP , 2014
2014
Earlier work this paper cites.
F. Mahdisoltani, J. Biega, and F. M. Suchanek, “A knowledge base from multilingual wikipedias–yago3,” Tech. Rep., 2014
2014
Earlier work this paper cites.
D. Vrandečić and M. Krötzsch, “Wikidata: a free collaborative knowledgebase,” Communications of the ACM , 2014
2014
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 Proc. of AAAI , 2015
2015
Earlier work this paper cites.
G. Ji, S. He, L. Xu, K. Liu, and J. Zhao, “Knowledge graph embedding via dynamic mapping matrix,” in Proc. of ACL , 2015
2015
Earlier work this paper cites.
S. He, K. Liu, G. Ji, and J. Zhao, “Learning to represent knowledge graphs with gaussian embedding,” in Proceedings of the 24th ACM international on conference on information and knowledge management , 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
G. Bouchard, S. Singh, and T. Trouillon, “On approximate reasoning capabilities of low-rank vector spaces,” in Proc. of AAAI , 2015
2015
Earlier work this paper cites.
K. Toutanova and D. Chen, “Observed versus latent features for knowledge base and text inference,” in Proceedings of the 3rd workshop on continuous vector space models and their compositionality , 2015
2015
Earlier work this paper cites.
S. Guo, Q. Wang, B. Wang, L. Wang, and L. Guo, “Semantically smooth knowledge graph embedding,” in Proc. of ACL , 2015
2015
Earlier work this paper cites.
K. Toutanova and D. Chen, “Observed versus latent features for knowledge base and text inference,” in Proceedings of the 3rd workshop on continuous vector space models and their compositionality , 2015
2015
Earlier work this paper cites.
E. Boschee, J. Lautenschlager, S. O’Brien, S. Shellman, J. Starz, and M. Ward, “ICEWS Coded Event Data,” 2015. [Online]. Available: https://doi.org/10.7910/DVN/28075
2015
Earlier work this paper cites.
G. Ji, K. Liu, S. He, and J. Zhao, “Knowledge graph completion with adaptive sparse transfer matrix,” in Proc. of AAAI , 2016
2016
Earlier work this paper cites.
T. Trouillon, J. Welbl, S. Riedel, É. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in Proc. of ICML , 2016
2016
Earlier work this paper cites.
M. Nickel, L. Rosasco, and T. Poggio, “Holographic embeddings of knowledge graphs,” in Proc. of AAAI , 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo, “Jointly embedding knowledge graphs and logical rules,” in Proc. of EMNLP , 2016
2016
Earlier work this paper cites.
A. Sadeghian, M. Rodriguez, D. Z. Wang, and A. Colas, “Temporal reasoning over event knowledge graphs,” in Workshop on Knowledge Base Construction, Reasoning and Mining , 2016
2016
Earlier work this paper cites.
F. Zhang, N. J. Yuan, D. Lian, X. Xie, and W.-Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proc. of KDD , 2016
2016
Earlier work this paper cites.
R. Xie, Z. Liu, J. Jia, H. Luan, and M. Sun, “Representation learning of knowledge graphs with entity descriptions,” in Proc. of AAAI , 2016
2016
Earlier work this paper cites.
S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo, “Jointly embedding knowledge graphs and logical rules,” in Proc. of EMNLP , 2016
2016
Earlier work this paper cites.
Y. Lin, Z. Liu, and M. Sun, “Knowledge representation learning with entities, attributes and relations,” in Proc. of IJCAI , 2016
2016
Earlier work this paper cites.
M. Franco-Salvador, P. Gupta, P. Rosso, and R. E. Banchs, “Cross-language plagiarism detection over continuous-space- and knowledge graph-based representations of language,” Knowledge-Based Systems , 2016
2016
Earlier work this paper cites.
H. Liu, Y. Wu, and Y. Yang, “Analogical inference for multi-relational embeddings,” in Proc. of ICML , 2017
2017
Earlier work this paper cites.
B. Shi and T. Weninger, “Proje: Embedding projection for knowledge graph completion,” in Proc. of AAAI , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. Rocktäschel and S. Riedel, “End-to-end differentiable proving,” Proc. of NeurIPS , 2017
2017
Earlier work this paper cites.
F. Yang, Z. Yang, and W. W. Cohen, “Differentiable learning of logical rules for knowledge base reasoning,” Proc. of NeurIPS , 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 Proc. of ICML , 2017
2017
Earlier work this paper cites.
R. Xie, Z. Liu, H. Luan, and M. Sun, “Image-embodied knowledge representation learning,” in Proc. of IJCAI , 2017
2017
Earlier work this paper cites.
A. Garcia-Duran and M. Niepert, “Kblrn: End-to-end learning of knowledge base representations with latent, relational, and numerical features,” arXiv e-prints , 2017
2017
Earlier work this paper cites.
R. Speer, J. Chin, and C. Havasi, “Conceptnet 5.5: An open multilingual graph of general knowledge,” in Proc. of AAAI , 2017
2017
Earlier work this paper cites.
D. S. Himmelstein, A. Lizee, C. Hessler, L. Brueggeman, S. L. Chen, D. Hadley, A. Green, P. Khankhanian, and S. E. Baranzini, “Systematic integration of biomedical knowledge prioritizes drugs for repurposing,” eLife , 2017
2017
Earlier work this paper cites.
W. Xiong, T. Hoang, and W. Y. Wang, “Deeppath: A reinforcement learning method for knowledge graph reasoning,” in EMNLP , 2017
2017
Earlier work this paper cites.
S. Ferrada, B. Bustos, and A. Hogan, “Imgpedia: a linked dataset with content-based analysis of wikimedia images,” in Proc. of ISWC , 2017
2017
Earlier work this paper cites.
M. Rotmensch, Y. Halpern, A. Tlimat, S. Horng, and D. Sontag, “Learning a health knowledge graph from electronic medical records,” Scientific reports , 2017
2017
Earlier work this paper cites.
T. Ebisu and R. Ichise, “Toruse: Knowledge graph embedding on a lie group,” in Proc. of AAAI , 2018
2018
Earlier work this paper cites.
S. M. Kazemi and D. Poole, “Simple embedding for link prediction in knowledge graphs,” Proc. of NeurIPS , 2018
2018
Earlier work this paper cites.
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2d knowledge graph embeddings,” in Proc. of AAAI , 2018
2018
Earlier work this paper cites.
M. Schlichtkrull, T. N. Kipf, P. Bloem, R. v. d. Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in European semantic web conference , 2018
2018
Earlier work this paper cites.
Y. Shen, J. Chen, P.-S. Huang, Y. Guo, and J. Gao, “M-walk: Learning to walk in graph with monte carlo tree search,” in NIPS 2018 , 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
C. Meilicke, M. Fink, Y. Wang, D. Ruffinelli, R. Gemulla, and H. Stuckenschmidt, “Fine-grained evaluation of rule-and embedding-based systems for knowledge graph completion,” in Proc. of ISWC , 2018
2018
Earlier work this paper cites.
S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo, “Knowledge graph embedding with iterative guidance from soft rules,” in Proc. of AAAI , 2018
2018
Earlier work this paper cites.
A. Garcia-Duran, S. Dumancic, and M. Niepert, “Learning sequence encoders for temporal knowledge graph completion,” in EMNLP , 2018
2018
Earlier work this paper cites.
S. S. Dasgupta, S. N. Ray, and P. Talukdar, “Hyte: Hyperplane-based temporally aware knowledge graph embedding,” in Proc. of EMNLP , 2018
2018
Earlier work this paper cites.
A. Garca-Duran, S. Dumancic, and M. Niepert, “Learning sequence encoders for temporal knowledge graph completion,” in EMNLP , 2018
2018
Earlier work this paper cites.
Y. Seo, M. Defferrard, P. Vandergheynst, and X. Bresson, “Structured sequence modeling with graph convolutional recurrent networks,” in International conference on neural information processing . Springer, 2018, pp. 362–373
2018
Earlier work this paper cites.
Y. Zuo, Q. Fang, S. Qian, X. Zhang, and C. Xu, “Representation learning of knowledge graphs with entity attributes and multimedia descriptions,” in 2018 IEEE Fourth International Conference on Multimedia Big Data (BigMM) , 2018
2018
Earlier work this paper cites.
H. Mousselly-Sergieh, T. Botschen, I. Gurevych, and S. Roth, “A multimodal translation-based approach for knowledge graph representation learning,” in Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics , 2018
2018
Earlier work this paper cites.
P. Pezeshkpour, L. Chen, and S. Singh, “Embedding multimodal relational data for knowledge base completion,” in Proc. of EMNLP , 2018
2018
Earlier work this paper cites.
B. Shi and T. Weninger, “Open-world knowledge graph completion,” in Proc. of AAAI , 2018
2018
Earlier work this paper cites.
B. Ding, Q. Wang, B. Wang, and L. Guo, “Improving knowledge graph embedding using simple constraints,” in Proc. of ACL , 2018
2018
Earlier work this paper cites.
S. Guo, Q. Wang, L. Wang, B. Wang, and L. Guo, “Knowledge graph embedding with iterative guidance from soft rules,” in Proc. of AAAI , 2018
2018
Earlier work this paper cites.
X. Lv, L. Hou, J. Li, and Z. Liu, “Differentiating concepts and instances for knowledge graph embedding,” in Proc. of EMNLP , 2018
2018
Earlier work this paper cites.
A. García-Durán, S. Dumancic, and M. Niepert, “Learning sequence encoders for temporal knowledge graph completion,” in EMNLP , 2018
2018
Earlier work this paper cites.
I. Balazevic, C. Allen, and T. Hospedales, “Multi-relational poincaré graph embeddings,” Proc. of NeurIPS , 2019
2019
Earlier work this paper cites.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
W. Zhang, B. Paudel, W. Zhang, A. Bernstein, and H. Chen, “Interaction embeddings for prediction and explanation in knowledge graphs,” in Proc. of WSDM , 2019
2019
Cited alongside, same era.
S. Zhang, Y. Tay, L. Yao, and Q. Liu, “Quaternion knowledge graph embeddings,” Proc. of NeurIPS , 2019
H. Sun, J. Zhong, Y. Ma, Z. Han, and K. He, “Timetraveler: Reinforcement learning for temporal knowledge graph forecasting,” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
Z. Li, X. Jin, S. Guan, W. Li, J. Guo, Y. Wang, and X. Cheng, “Search from history and reason for future: Two-stage reasoning on temporal knowledge graphs,” in ACL , 2021
2021
Later among the works it cites.
Y. Zhao, X. Wang, J. Chen, Y. Wang, W. Tang, X. He, and H. Xie, “Time-aware path reasoning on knowledge graph for recommendation,” ACM Transactions on Information Systems (TOIS) , 2021
2021
Later among the works it cites.
S. Liao, S. Liang, Z. Meng, and Q. Zhang, “Learning dynamic embeddings for temporal knowledge graphs,” in Proc. of WSDM , 2021
2021
Later among the works it cites.
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2019
Cited alongside, same era.
I. Balažević, C. Allen, and T. M. Hospedales, “Hypernetwork knowledge graph embeddings,” in Proc. of ICANN , 2019
2019
Cited alongside, same era.
X. Jiang, Q. Wang, and B. Wang, “Adaptive convolution for multi-relational learning,” in Proc. of AACL , 2019
2019
Cited alongside, same era.
Z. Wang, Z. Ren, C. He, P. Zhang, and Y. Hu, “Robust embedding with multi-level structures for link prediction.” in Proc. of IJCAI , 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
P. Wang, J. Han, C. Li, and R. Pan, “Logic attention based neighborhood aggregation for inductive knowledge graph embedding,” in Proc. of AAAI , 2019
2019
Cited alongside, same era.
C. Shang, Y. Tang, J. Huang, J. Bi, X. He, and B. Zhou, “End-to-end structure-aware convolutional networks for knowledge base completion,” in Proc. of AAAI , 2019
2019
Cited alongside, same era.
L. Cai, B. Yan, G. Mai, K. Janowicz, and R. Zhu, “Transgcn: Coupling transformation assumptions with graph convolutional networks for link prediction,” in Proceedings of the 10th International Conference on Knowledge Capture , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
J. Jung, J. Jung, and U. Kang, “Learning to walk across time for interpretable temporal knowledge graph completion,” in Proc. of KDD , 2021
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark et al. , “Learning transferable visual models from natural language supervision,” in Proc. of ICML , 2021
2021
Later among the works it cites.
M. Wang, S. Wang, H. Yang, Z. Zhang, X. Chen, and G. Qi, “Is visual context really helpful for knowledge graph? a representation learning perspective,” in Proceedings of the 29th ACM International Conference on Multimedia , ser. MM ’21. New York, NY, USA: Association for Computing Machinery, 2021, p. 2735–2743. [Online]. Available: https://doi.org/10.1145/3474085.3475470
2021
Later among the works it cites.
H. Guo, J. Tang, W. Zeng, X. Zhao, and L. Liu, “Multi-modal entity alignment in hyperbolic space,” Neurocomputing , vol. 461, pp. 598–607, 2021
2021
Later among the works it cites.
J. Guo and S. Kok, “Bique: Biquaternionic embeddings of knowledge graphs,” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
X. Wang, T. Gao, Z. Zhu, Z. Zhang, Z. Liu, J. Li, and J. Tang, “Kepler: A unified model for knowledge embedding and pre-trained language representation,” Transactions of the Association for Computational Linguistics , 2021
2021
Later among the works it cites.
Z. Han, G. Zhang, Y. Ma, and V. Tresp, “Time-dependent entity embedding is not all you need: A re-evaluation of temporal knowledge graph completion models under a unified framework,” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
F. Xia, K. Sun, S. Yu, A. Aziz, L. Wan, S. Pan, and H. Liu, “Graph learning: A survey,” IEEE Transactions on Artificial Intelligence , vol. 2, no. 2, pp. 109–127, 2021
2021
Later among the works it cites.
J. Dong, Y. Cong, G. Sun, Z. Fang, and Z. Ding, “Where and how to transfer: knowledge aggregation-induced transferability perception for unsupervised domain adaptation,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2021
2021
Later among the works it cites.
Y. Lan, S. He, K. Liu, X. Zeng, S. Liu, and J. Zhao, “Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion,” BMC Medical Informatics and Decision Making , 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
H. Yuan, H. Yu, S. Gui, and S. Ji, “Explainability in graph neural networks: A taxonomic survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2022
2022
Closest in time.
K. Liang, Y. Liu, S. Zhou, X. Liu, and W. Tu, “Relational symmetry based knowledge graph contrastive learning,” 2022
2022
Closest in time.
2022
Closest in time.
Y. Chen, H. Li, H. Li, W. Liu, Y. Wu, Q. Huang, and S. Wan, “An overview of knowledge graph reasoning: Key technologies and applications,” Journal of Sensor and Actuator Networks , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
X. Zhu, Z. Li, X. Wang, X. Jiang, P. Sun, X. Wang, Y. Xiao, and N. J. Yuan, “Multi-modal knowledge graph construction and application: A survey,” ArXiv , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
D. Q. Nguyen, T. Vu, T. D. Nguyen, and D. Phung, “Quatre: Relation-aware quaternions for knowledge graph embeddings,” in Proc. of WWW , 2022
2022
Closest in time.
S. Zheng, S. Mai, Y. Sun, H. Hu, and Y. Yang, “Subgraph-aware few-shot inductive link prediction via meta-learning,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
Y. Zhang and Q. Yao, “Knowledge graph reasoning with relational digraph,” in Proceedings of the ACM Web Conference 2022 , 2022
2022
Closest in time.
H. Zha, Z. Chen, and X. Yan, “Inductive relation prediction by bert,” in Proc. of AAAI , 2022
2022
Closest in time.
Q. Lin, J. Liu, F. Xu, Y. Pan, Y. Zhu, L. Zhang, and T. Zhao, “Incorporating context graph with logical reasoning for inductive relation prediction,” in Proc. of SIGIR , 2022
2022
Closest in time.
2022
Closest in time.
Y. Pan, J. Liu, L. Zhang, T. Zhao, Q. Lin, X. Hu, and Q. Wang, “Inductive relation prediction with logical reasoning using contrastive representations,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 4261–4274. [Online]. Available: https://aclanthology.org/2022.emnlp-main.286
2022
Closest in time.
D. Zhang, Z. Yuan, H. Liu, H. Xiong et al. , “Learning to walk with dual agents for knowledge graph reasoning,” in Proc. of AAAI , 2022
2022
Closest in time.
H. Chen, Y. Li, S. Shi, S. Liu, H. Zhu, and Y. Zhang, “Graph collaborative reasoning,” in Proc. of WSDM , 2022
2022
Closest in time.
Q. Lin, J. Liu, F. Xu, Y. Pan, Y. Zhu, L. Zhang, and T. Zhao, “Incorporating context graph with logical reasoning for inductive relation prediction,” ser. SIGIR ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 893–903. [Online]. Available: https://doi-org-s.libyc.nudt.edu.cn:443/10.1145/3477495.3531996
2022
Closest in time.
Y. Xu, J. Ou, H. Xu, and L. Fu, “Temporal knowledge graph reasoning with historical contrastive learning,” in Proc. of AAAI , 2022
2022
Closest in time.
K. Liu, F. Zhao, H. Chen, Y. Li, G. Xu, and H. Jin, “Da-net: Distributed attention network for temporal knowledge graph reasoning,” in Proc. of CIKM , 2022
2022
Closest in time.
2022
Closest in time.
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 Proc. of IJCAI , 2022
2022
Closest in time.
2022
Closest in time.
Z. Ding, J. Wu, B. He, Y. Ma, Z. Han, and V. Tresp, “Few-shot inductive learning on temporal knowledge graphs using concept-aware information,” AKBC , 2022
2022
Closest in time.
L. Zhang and D. Zhou, “Temporal knowledge graph completion with approximated gaussian process embedding,” in Proc. of COLING , 2022
2022
Closest in time.
Y. Liu, Y. Ma, M. Hildebrandt, M. Joblin, and V. Tresp, “Tlogic: Temporal logical rules for explainable link forecasting on temporal knowledge graphs,” in Proc. of AAAI , 2022
2022
Closest in time.
F. Zhang, Z. Zhang, X. Ao, F. Zhuang, Y. Xu, and Q. He, “Along the time: Timeline-traced embedding for temporal knowledge graph completion,” in Proc. of CIKM , 2022
2022
Closest in time.
Z. Li, S. Guan, X. Jin, W. Peng, Y. Lyu, Y. Zhu, L. Bai, W. Li, J. Guo, and X. Cheng, “Complex evolutional pattern learning for temporal knowledge graph reasoning,” in Proc. of ACL , 2022
2022
Closest in time.
J. Messner, R. Abboud, and I. I. Ceylan, “Temporal knowledge graph completion using box embeddings,” in Proc. of AAAI , 2022
2022
Closest in time.
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 Proc. of AAAI , 2022
2022
Closest in time.
P. Shao, D. Zhang, G. Yang, J. Tao, F. Che, and T. Liu, “Tucker decomposition-based temporal knowledge graph completion,” Knowledge-Based Systems , 2022
2022
Closest in time.
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 2022 Conference on Empirical Methods in Natural Language Processing . Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 7481–7493. [Online]. Available: https://aclanthology.org/2022.emnlp-main.507
2022
Closest in time.
T. Wu, A. Khan, M. Yong, G. Qi, and M. Wang, “Efficiently embedding dynamic knowledge graphs,” Knowledge-Based Systems , 2022
2022
Closest in time.
K. Chen, Y. Wang, Y. Li, and A. Li, “Rotateqvs: Representing temporal information as rotations in quaternion vector space for temporal knowledge graph completion,” in Proc. of ACL , 2022
2022
Closest in time.
C. Yan, F. Zhao, and H. Jin, “Exkgr: Explainable multi-hop reasoning for evolving knowledge graph,” in Proc. of DASFAA , 2022
2022
Closest in time.
L. Yuan, Z. Li, J. Qu, T. Zhang, A. Liu, L. Zhao, and Z. Chen, “Trhyte: Temporal knowledge graph embedding based on temporal-relational hyperplanes,” in Proc. of DASFAA , 2022
2022
Closest in time.
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 Proc. of WSDM , 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
X. Chen, N. Zhang, L. Li, S. Deng, C. Tan, C. Xu, F. Huang, L. Si, and H. Chen, “Hybrid transformer with multi-level fusion for multimodal knowledge graph completion,” in Proc. of SIGIR , 2022
2022
Closest in time.
L. Chen, Z. Li, T. Xu, H. Wu, Z. Wang, N. J. Yuan, and E. Chen, “Multi-modal siamese network for entity alignment,” in Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining , ser. KDD ’22. New York, NY, USA: Association for Computing Machinery, 2022, p. 118–126. [Online]. Available: https://doi.org/10.1145/3534678.3539244
2022
Closest in time.
Y. Ding, J. Yu, B. Liu, Y. Hu, M. Cui, and Q. Wug, “Mukea: Multimodal knowledge extraction and accumulation for knowledge-based visual question answering,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , 2022
2022
Closest in time.
2022
Closest in time.
Y. Zhao, X. Cai, Y. Wu, H. Zhang, Y. Zhang, G. Zhao, and N. Jiang, “MoSE: Modality split and ensemble for multimodal knowledge graph completion,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing . Abu Dhabi, United Arab Emirates: Association for Computational Linguistics, Dec. 2022, pp. 10 527–10 536. [Online]. Available: https://aclanthology.org/2022.emnlp-main.719
2022
Closest in time.
Z. Cao, Q. Xu, Z. Yang, Y. He, X. Cao, and Q. Huang, “Otkge: Multi-modal knowledge graph embeddings via optimal transport,” Advances in Neural Information Processing Systems , vol. 35, pp. 39 090–39 102, 2022
2022
Closest in time.
D. Xu, T. Xu, S. Wu, J. Zhou, and E. Chen, “Relation-enhanced negative sampling for multimodal knowledge graph completion,” in Proc. of ACM MM , 2022
2022
Closest in time.
X. Cao, Y. Shi, J. Wang, H. Yu, X. Wang, and Z. Yan, “Cross-modal knowledge graph contrastive learning for machine learning method recommendation,” in Proc. of ACM MM , 2022
2022
Closest in time.
X. Lu, L. Wang, Z. Jiang, S. He, and S. Liu, “Mmkrl: A robust embedding approach for multi-modal knowledge graph representation learning,” vol. 52, no. 7, p. 7480–7497, may 2022. [Online]. Available: https://doi.org/10.1007/s10489-021-02693-9
2022
Closest in time.
Y. Cui, Y. Wang, Z. Sun, W. Liu, Y. Jiang, K. Han, and W. Hu, “Inductive knowledge graph reasoning for multi-batch emerging entities,” in Proc. of CIKM , 2022
2022
Closest in time.
T. Wu, A. Khan, M. Yong, G. Qi, and M. Wang, “Efficiently embedding dynamic knowledge graphs,” Knowledge-Based Systems , 2022
2022
Closest in time.
Y. Zhang, Z. Zhou, Q. Yao, X. Chu, and B. Han, “Learning adaptive propagation for knowledge graph reasoning,” 2022
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
F.-A. Croitoru, V. Hondru, R. T. Ionescu, and M. Shah, “Diffusion models in vision: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
Y. Zhang, B. Kang, B. Hooi, S. Yan, and J. Feng, “Deep long-tailed learning: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
P. Xu, X. Zhu, and D. A. Clifton, “Multimodal learning with transformers: A survey,” IEEE Transactions on Pattern Analysis and Machine Intelligence , 2023
2023
Closest in time.
2023
Closest in time.
J. Li, Q. Wang, and Z. Mao, “Inductive relation prediction from relational paths and context with hierarchical transformers,” in ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2023, pp. 1–5
2023
Closest in time.
K. Liu, F. Zhao, G. Xu, X. Wang, and H. Jin, “Retia: relation-entity twin-interact aggregation for temporal knowledge graph extrapolation,” in IEEE International Conference on Data Engineering . IEEE, 2023
2023
Closest in time.
K. Liang, L. Meng, M. Liu, Y. Liu, W. Tu, S. Wang, S. Zhou, and X. Liu, “Learn from relational correlations and periodic events for temporal knowledge graph reasoning,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR ’23) , 2023
2023
Closest in time.
S. Liang, A. Zhu, J. Zhang, and J. Shao, “Hyper-node relational graph attention network for multi-modal knowledge graph completion,” vol. 19, no. 2, feb 2023. [Online]. Available: https://doi.org/10.1145/3545573
2023
Closest in time.
X. Li, X. Zhao, J. Xu, Y. Zhang, and C. Xing, “Imf: Interactive multimodal fusion model for link prediction,” in Proceedings of the ACM Web Conference 2023 , ser. WWW ’23. New York, NY, USA: Association for Computing Machinery, 2023, p. 2572–2580. [Online]. Available: https://doi.org/10.1145/3543507.3583554
2023
Closest in time.
2023
Closest in time.