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Knowledge graphs (KGs) are structured representations of diversified knowledge.
F. L. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” Journal of Mathematics and Physics , 1927
1927
Earlier work this paper cites.
L. R. Tucker, “Some mathematical notes on three-mode factor analysis,” Psychometrika , 1966
1966
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.
L. Vila, “A survey on temporal reasoning in artificial intelligence,” Ai Communications , 1994
1994
Earlier work this paper cites.
Z. Dong and Q. Dong, “Hownet-a hybrid language and knowledge resource,” in International conference on natural language processing and knowledge engineering, 2003. Proceedings. 2003 , 2003
2003
Earlier work this paper cites.
J. Pustejovsky, J. M. Castano, R. Ingria, R. Sauri, R. J. Gaizauskas, A. Setzer, G. Katz, and D. R. Radev, “Timeml: Robust specification of event and temporal expressions in text.” New directions in question answering , vol. 3, pp. 28–34, 2003
2003
Earlier work this paper cites.
O. Etzioni, M. J. Cafarella, D. Downey, S. Kok, A. Popescu, T. Shaked, S. Soderland, D. S. Weld, and A. Yates, “Web-scale information extraction in knowitall: (preliminary results),” in Proc. of WWW , 2004
2004
Earlier work this paper cites.
R. C. Bunescu and R. J. Mooney, “Subsequence kernels for relation extraction,” in Proc. of NeurIPS , 2005
2005
Earlier work this paper cites.
M. Völkel, M. Krötzsch, D. Vrandecic, H. Haller, and R. Studer, “Semantic wikipedia,” in Proc. of WWW , 2006
2006
Earlier work this paper cites.
F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: a core of semantic knowledge,” in Proc. of WWW , 2007
2007
Earlier work this paper cites.
K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: a collaboratively created graph database for structuring human knowledge,” in Proc. of SIGMOD , 2008
2008
Earlier work this paper cites.
C. Fellbaum, “Wordnet,” in Theory and applications of ontology: computer applications , 2010
2010
Earlier work this paper cites.
A. Carlson, J. Betteridge, R. C. Wang, E. R. H. Jr., and T. M. Mitchell, “Coupled semi-supervised learning for information extraction,” in Proceedings of the Third International Conference on Web Search and Web Data Mining, WSDM 2010, New York, NY, USA, February 4-6, 2010 , 2010
2010
Earlier work this paper cites.
P. Jain, P. Hitzler, A. P. Sheth, K. Verma, and P. Z. Yeh, “Ontology alignment for linked open data,” in Proc. of ISWC , 2010
2010
Earlier work this paper cites.
B. Rink and S. Harabagiu, “UTD: Classifying semantic relations by combining lexical and semantic resources,” in Proc. of SemEval , 2010
2010
Earlier work this paper cites.
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. H. Jr., 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 iee-way model for collective learning on multi-relational data.” in ICML , 2011
2011
Earlier work this paper cites.
T. Steiner, R. Verborgh, R. Troncy, J. Gabarro, and R. Van de Walle, “Adding realtime coverage to the google knowledge graph,” in Proc. of ISWC , 2012
2012
Earlier work this paper cites.
Mausam, M. Schmitz, S. Soderland, R. Bart, and O. Etzioni, “Open language learning for information extraction,” in Proc. of EMNLP , 2012
2012
Earlier work this paper cites.
K. Leetaru and P. A. Schrodt, “Gdelt: Global data on events, location, and tone, 1979–2012,” in ISA annual convention , 2013
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
N. Chambers, “Event schema induction with a probabilistic entity-driven model,” in Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing , 2013, pp. 1797–1807
2013
Earlier work this paper cites.
A. Bordes, N. Usunier, A. García-Durán, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Proc. of NeurIPS , 2013
2013
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.
J. Pennington, R. Socher, and C. Manning, “GloVe: Global vectors for word representation,” in Proc. of EMNLP , 2014
2014
Earlier work this paper cites.
D. Zeng, K. Liu, S. Lai, G. Zhou, and J. Zhao, “Relation classification via convolutional deep neural network,” in Proc. of COLING , 2014
2014
Earlier work this paper cites.
X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and W. Zhang, “Knowledge vault: a web-scale approach to probabilistic knowledge fusion,” in Proc. of KDD , 2014
2014
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.
J. Lautenschlager, S. Shellman, and M. Ward, “Icews event aggregations,” 2015
2015
Earlier work this paper cites.
F. Mahdisoltani, J. Biega, and F. M. Suchanek, “YAGO3: A knowledge base from multilingual wikipedias,” in Proc. of CIDR , 2015
2015
Earlier work this paper cites.
D. Zeng, K. Liu, Y. Chen, and J. Zhao, “Distant supervision for relation extraction via piecewise convolutional neural networks,” in Proc. of EMNLP , 2015
2015
Earlier work this paper cites.
T. M. Mitchell, W. W. Cohen, E. R. H. Jr., P. P. Talukdar, J. Betteridge, A. Carlson, B. D. Mishra, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohamed, N. Nakashole, E. A. Platanios, A. Ritter, M. Samadi, B. Settles, R. C. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, and J. Welling, “Never-ending learning,” in Proc. of AAAI , 2015
2015
Earlier work this paper cites.
B. Yang, W. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in Proc. of ICLR , 2015
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 Proc. of AAAI , 2015
2015
Earlier work this paper cites.
L. Ehrlinger and W. Wöß, “Towards a definition of knowledge graphs.” SEMANTiCS (Posters, Demos, SuCCESS) , 2016
2016
Earlier work this paper cites.
M. Rospocher, M. Van Erp, P. Vossen, A. Fokkens, I. Aldabe, G. Rigau, A. Soroa, T. Ploeger, and T. Bogaard, “Building event-centric knowledge graphs from news,” Journal of Web Semantics , 2016
2016
Earlier work this paper cites.
J. Zhuo, Y. Cao, J. Zhu, B. Zhang, and Z. Nie, “Segment-level sequence modeling using gated recursive semi-Markov conditional random fields,” in Proc. of ACL , 2016
2016
Earlier work this paper cites.
T. H. Nguyen, A. Sil, G. Dinu, and R. Florian, “Toward mention detection robustness with recurrent neural networks,” 2016
2016
Earlier work this paper cites.
M. Miwa and M. Bansal, “End-to-end relation extraction using LSTMs on sequences and tree structures,” in Proc. of ACL , 2016
2016
Earlier work this paper cites.
T. H. Nguyen and R. Grishman, “Modeling skip-grams for event detection with convolutional neural networks,” in Proc. of EMNLP , 2016
2016
Earlier work this paper cites.
R. Ghaeini, X. Fern, L. Huang, and P. Tadepalli, “Event nugget detection with forward-backward recurrent neural networks,” in Proc. of ACL , 2016
2016
Earlier work this paper cites.
X. Ma and E. Hovy, “End-to-end sequence labeling via bi-directional LSTM-CNNs-CRF,” in Proc. of ACL , 2016
2016
Earlier work this paper cites.
Y. Chen, S. Liu, S. He, K. Liu, and J. Zhao, “Event extraction via bidirectional long short-term memory tensor neural networks,” in Proc. of CCL , 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.
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.
Q. Wang, J. Liu, Y. Luo, B. Wang, and C.-Y. Lin, “Knowledge base completion via coupled path ranking,” in Proc. of ACL , 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.
T. Jiang, T. Liu, T. Ge, L. Sha, S. Li, B. Chang, and Z. Sui, “Encoding temporal information for time-aware link prediction,” in Proc. of EMNLP , 2016
2016
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 Proc. of COLING , 2016
2016
Earlier work this paper cites.
Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Transactions on Knowledge and Data Engineering , 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.
Z. Li, S. Zhao, X. Ding, and T. Liu, “Eeg: knowledge base for event evolutionary principles and patterns,” in Social Media Processing: 6th National Conference, SMP 2017, Beijing, China, September 14-17, 2017, Proceedings , 2017
2017
Earlier work this paper cites.
F. Zhai, S. Potdar, B. Xiang, and B. Zhou, “Neural models for sequence chunking,” in Proc. of AAAI , 2017
2017
Earlier work this paper cites.
E. Strubell, P. Verga, D. Belanger, and A. McCallum, “Fast and accurate entity recognition with iterated dilated convolutions,” in Proc. of EMNLP , 2017
2017
Earlier work this paper cites.
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov, “Enriching word vectors with subword information,” Transactions of the Association for Computational Linguistics , 2017
2017
Earlier work this paper cites.
P.-H. Li, R.-P. Dong, Y.-S. Wang, J.-C. Chou, and W.-Y. Ma, “Leveraging linguistic structures for named entity recognition with bidirectional recursive neural networks,” in Proc. of EMNLP , 2017
2017
Earlier work this paper cites.
A. Katiyar and C. Cardie, “Going out on a limb: Joint extraction of entity mentions and relations without dependency trees,” in Proc. of ACL , 2017
2017
Earlier work this paper cites.
T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” in Proc. of ICLR , 2017
2017
Earlier work this paper cites.
F. Yang, Z. Yang, and W. W. Cohen, “Differentiable learning of logical rules for knowledge base reasoning,” in Proc. of NeurIPS , 2017
2017
Earlier work this paper cites.
V. Tresp, Y. Ma, S. Baier, and Y. Yang, “Embedding learning for declarative memories,” in The Semantic Web: 14th International Conference, ESWC 2017, Portoro, Slovenia, May 28–June 1, 2017, Proceedings, Part I 14 , 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.
M. Färber, F. Bartscherer, C. Menne, and A. Rettinger, “Linked data quality of dbpedia, freebase, opencyc, wikidata, and yago,” Semantic Web , 2018
2018
Earlier work this paper cites.
S. Gottschalk and E. Demidova, “Eventkg: A multilingual event-centric temporal knowledge graph,” in The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15 , 2018
2018
Earlier work this paper cites.
A. Žukov-Gregorič, Y. Bachrach, and S. Coope, “Named entity recognition with parallel recurrent neural networks,” in Proc. of ACL , 2018
2018
Earlier work this paper cites.
A. Akbik, D. Blythe, and R. Vollgraf, “Contextual string embeddings for sequence labeling,” in Proc. of COLING , 2018
2018
Earlier work this paper cites.
M. E. Peters, M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, “Deep contextualized word representations,” in Proc. of NAACL , 2018
2018
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
Q. Ning, B. Zhou, Z. Feng, H. Peng, and D. Roth, “Cogcomptime: A tool for understanding time in natural language,” in Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations , 2018, pp. 72–77
2018
Earlier work this paper cites.
T. H. Nguyen and R. Grishman, “Graph convolutional networks with argument-aware pooling for event detection,” in Proc. of AAAI , 2018
2018
Cited alongside, same era.
C. Wang, K. Cho, and D. Kiela, “Code-switched named entity recognition with embedding attention,” in Proceedings of the Third Workshop on Computational Approaches to Linguistic Code-Switching , 2018
2018
Cited alongside, same era.
S. M. Kazemi and D. Poole, “Simple embedding for link prediction in knowledge graphs,” in Proc. of NeurIPS , 2018
2018
Cited alongside, same era.
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
Cited alongside, same era.
——, “Knowledge graph embedding with iterative guidance from soft rules,” in Proc. of AAAI , 2018
2018
T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” in Proc. of NeurIPS , 2020
2020
Later among the works it cites.
N. Poerner, U. Waltinger, and H. Schütze, “E-BERT: Efficient-yet-effective entity embeddings for BERT,” in Proc. of EMNLP Findings , 2020
2020
Later among the works it cites.
Z. Nasar, S. W. Jaffry, and M. K. Malik, “Named entity recognition and relation extraction: State-of-the-art,” ACM Computing Surveys (CSUR) , 2021
2021
Later among the works it cites.
G. Paolini, B. Athiwaratkun, J. Krone, J. Ma, A. Achille, R. Anubhai, C. N. dos Santos, B. Xiang, and S. Soatto, “Structured prediction as translation between augmented natural languages,” in Proc. of ICLR , 2021
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Cited alongside, same era.
R. Das, S. Dhuliawala, M. Zaheer, L. Vilnis, I. Durugkar, A. Krishnamurthy, A. Smola, and A. McCallum, “Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning,” in Proc. of ICLR , 2018
2018
Cited alongside, same era.
W. Xiong, M. Yu, S. Chang, X. Guo, and W. Y. Wang, “One-shot relational learning for knowledge graphs,” in Proc. of EMNLP , 2018
2018
Cited alongside, same era.
J. Leblay and M. W. Chekol, “Deriving validity time in knowledge graph,” in Proc. of WWW , 2018
2018
Cited alongside, same era.
S. S. Dasgupta, S. N. Ray, and P. Talukdar, “HyTE: Hyperplane-based temporally aware knowledge graph embedding,” in Proc. of EMNLP , 2018
2018
Cited alongside, same era.
A. García-Durán, S. Dumančić, and M. Niepert, “Learning sequence encoders for temporal knowledge graph completion,” in Proc. of EMNLP , 2018
2018
Cited alongside, same era.
G. H. Nguyen, J. B. Lee, R. A. Rossi, N. K. Ahmed, E. Koh, and S. Kim, “Dynamic network embeddings: From random walks to temporal random walks,” in IEEE International Conference on Big Data, Big Data 2018, Seattle, WA, USA, December 10-13, 2018 , 2018
2018
Cited alongside, same era.
J. Frey, M. Hofer, D. Obraczka, J. Lehmann, and S. Hellmann, “Dbpedia flexifusion the best of wikipedia> wikidata> your data,” in Proc. of ISWC , 2019
2019
Cited alongside, same era.
2021
Later among the works it cites.
P.-L. Huguet Cabot and R. Navigli, “REBEL: Relation extraction by end-to-end language generation,” in Proc. of EMNLP Findings , 2021
2021
Later among the works it cites.
H. Ye, N. Zhang, S. Deng, M. Chen, C. Tan, F. Huang, and H. Chen, “Contrastive triple extraction with generative transformer,” in Proc. of AAAI , 2021
2021
Later among the works it cites.
K.-H. Huang, S. Tang, and N. Peng, “Document-level entity-based extraction as template generation,” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
L. Du, X. Ding, K. Xiong, T. Liu, and B. Qin, “ExCAR: Event graph knowledge enhanced explainable causal reasoning,” in Proc. of ACL , 2021
2021
Later among the works it cites.
R. Han, X. Ren, and N. Peng, “ECONET: Effective continual pretraining of language models for event temporal reasoning,” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
H. Yan, T. Gui, J. Dai, Q. Guo, Z. Zhang, and X. Qiu, “A unified generative framework for various NER subtasks,” in Proc. of ACL , 2021
2021
Later among the works it cites.
B. Wang, T. Shen, G. Long, T. Zhou, Y. Wang, and Y. Chang, “Structure-augmented text representation learning for efficient knowledge graph completion,” in Proc. of WWW , 2021
2021
Later among the works it cites.
Y. Su, X. Han, Z. Zhang, Y. Lin, P. Li, Z. Liu, J. Zhou, and M. Sun, “Cokebert: Contextual knowledge selection and embedding towards enhanced pre-trained language models,” AI Open , 2021
2021
Later among the works it cites.
J. Zhang, B. Chen, L. Zhang, X. Ke, and H. Ding, “Neural, symbolic and neural-symbolic reasoning on knowledge graphs,” AI Open , 2021
2021
Later among the works it cites.
S. Mai, S. Zheng, Y. Yang, and H. Hu, “Communicative message passing for inductive relation reasoning,” in Proc. of AAAI , 2021
2021
Later among the works it cites.
S. Liu, B. C. Grau, I. Horrocks, and E. V. Kostylev, “INDIGO: gnn-based inductive knowledge graph completion using pair-wise encoding,” in Proc. of NeurIPS , 2021
2021
Later among the works it cites.
J. Chen, H. He, F. Wu, and J. Wang, “Topology-aware correlations between relations for inductive link prediction in knowledge graphs,” in Proc. of AAAI , 2021
2021
Later among the works it cites.
H. Wang, H. Ren, and J. Leskovec, “Relational message passing for knowledge graph completion,” in Proc. of KDD , 2021
2021
Later among the works it cites.
Y. Zhang, W. Wang, W. Chen, J. Xu, A. Liu, and L. Zhao, “Meta-learning based hyper-relation feature modeling for out-of-knowledge-base embedding,” in Proc. of CIKM , 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.
J. Xu, J. Zhang, X. Ke, Y. Dong, H. Chen, C. Li, and Y. Liu, “P-INT: A path-based interaction model for few-shot knowledge graph completion,” in Proc. of EMNLP Findings , 2021
2021
Later among the works it cites.
G. Niu, Y. Li, C. Tang, R. Geng, J. Dai, Q. Liu, H. Wang, J. Sun, F. Huang, and L. Si, “Relational learning with gated and attentive neighbor aggregator for few-shot knowledge graph completion,” in Proc. of SIGIR , 2021
2021
Later among the works it cites.
S. Wang, X. Huang, C. Chen, L. Wu, and J. Li, “REFORM: error-aware few-shot knowledge graph completion,” in Proc. of CIKM , 2021
2021
Later among the works it cites.
J. Zhang, T. Wu, and G. Qi, “Gaussian metric learning for few-shot uncertain knowledge graph completion,” in Proc. of DASFAA , 2021
2021
Later among the works it cites.
C. Xu, Y.-Y. Chen, M. Nayyeri, and J. Lehmann, “Temporal knowledge graph completion using a linear temporal regularizer and multivector embeddings,” in Proc. of NAACL , 2021
2021
Later among the works it cites.
L. Cai, K. Janowicz, B. Yan, R. Zhu, and G. Mai, “Time in a box: advancing knowledge graph completion with temporal scopes,” in Proceedings of the 11th on Knowledge Capture Conference , 2021
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.
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 Proc. of ACL , 2021
2021
Later among the works it cites.
Z. Han, P. Chen, Y. Ma, and V. Tresp, “Explainable subgraph reasoning for forecasting on temporal knowledge graphs,” in Proc. of ICLR , 2021
2021
Later among the works it cites.
Z. Li, X. Jin, W. Li, S. Guan, J. Guo, H. Shen, Y. Wang, and X. Cheng, “Temporal knowledge graph reasoning based on evolutional representation learning,” in Proc. of SIGIR , 2021
2021
Later among the works it cites.
L. Chen, X. Tang, W. Chen, Y. Qian, Y. Li, and Y. Zhang, “Dacha: A dual graph convolution based temporal knowledge graph representation learning method using historical relation,” ACM Transactions on Knowledge Discovery from Data (TKDD) , 2021
2021
Later among the works it cites.
Y. He, P. Zhang, L. Liu, Q. Liang, W. Zhang, and C. Zhang, “Hip network: Historical information passing network for extrapolation reasoning on temporal knowledge graph.” in Proc. of IJCAI , 2021
2021
Later among the works it cites.
A. Haviv, J. Berant, and A. Globerson, “BERTese: Learning to speak to BERT,” in Proc. of EACL , 2021
2021
Later among the works it cites.
Z. Zhong, D. Friedman, and D. Chen, “Factual probing is [MASK]: Learning vs. learning to recall,” in Proc. of NAACL , 2021
2021
Later among the works it cites.
E. M. Bender, T. Gebru, A. McMillan-Major, and S. Shmitchell, “On the dangers of stochastic parrots: Can language models be too big?” in Proceedings of the 2021 ACM conference on fairness, accountability, and transparency , 2021
2021
Later among the works it cites.
R. Han, X. Ren, and N. Peng, “ECONET: Effective continual pretraining of language models for event temporal reasoning,” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
M. Sung, J. Lee, S. Yi, M. Jeon, S. Kim, and J. Kang, “Can language models be biomedical knowledge bases?” in Proc. of EMNLP , 2021
2021
Later among the works it cites.
B. Cao, H. Lin, X. Han, L. Sun, L. Yan, M. Liao, T. Xue, and J. Xu, “Knowledgeable or educated guess? revisiting language models as knowledge bases,” in Proc. of ACL , 2021
2021
Later among the works it cites.
S. Guan, X. Cheng, L. Bai, F. Zhang, Z. Li, Y. Zeng, X. Jin, and J. Guo, “What is event knowledge graph: a survey,” IEEE Transactions on Knowledge and Data Engineering , 2022
2022
Later among the works it cites.
M. Chen, Y. Cao, K. Deng, M. Li, K. Wang, J. Shao, and Y. Zhang, “ERGO: Event relational graph transformer for document-level event causality identification,” in Proc. of COLING , 2022
2022
Later among the works it cites.
H. Li, T. Mo, H. Fan, J. Wang, J. Wang, F. Zhang, and W. Li, “KiPT: Knowledge-injected prompt tuning for event detection,” in Proc. of COLING , 2022
2022
Later among the works it cites.
L. Du, X. Ding, Y. Zhang, T. Liu, and B. Qin, “A graph enhanced BERT model for event prediction,” in Proc. of ACL Findings , 2022
2022
Later among the works it cites.
Y. Zhou, X. Geng, T. Shen, G. Long, and D. Jiang, “Eventbert: A pre-trained model for event correlation reasoning,” in Proceedings of the ACM Web Conference 2022 , 2022
2022
Later among the works it cites.
S. Fincke, S. Agarwal, S. Miller, and E. Boschee, “Language model priming for cross-lingual event extraction,” in Proc. of AAAI , 2022
2022
Later among the works it cites.
C. Wang, X. Liu, Z. Chen, H. Hong, J. Tang, and D. Song, “DeepStruct: Pretraining of language models for structure prediction,” in Proc. of ACL Findings , 2022
2022
Later among the works it cites.
J. Ju, D. Yang, and J. Liu, “Commonsense knowledge base completion with relational graph attention network and pre-trained language model,” in Proc. of CIKM , 2022
2022
Later among the works it cites.
L. Wang, W. Zhao, Z. Wei, and J. Liu, “SimKGC: Simple contrastive knowledge graph completion with pre-trained language models,” in Proc. of ACL , 2022
2022
Later among the works it cites.
Y. Geng, J. Chen, W. Zhang, J. Z. Pan, M. Chen, H. Chen, and S. Jiang, “Relational message passing for fully inductive knowledge graph completion,” ArXiv preprint , 2022
2022
Later among the works it cites.
J. You, T. Du, and J. Leskovec, “ROLAND: graph learning framework for dynamic graphs,” in Proc. of KDD , 2022
2022
Later among the works it cites.
R. Li, Y. Cao, Q. Zhu, G. Bi, F. Fang, Y. Liu, and Q. Li, “How does knowledge graph embedding extrapolate to unseen data: A semantic evidence view,” in Proc. of AAAI , 2022
2022
Later among the works it cites.
M. Chen, W. Zhang, Z. Yao, X. Chen, M. Ding, F. Huang, and H. Chen, “Meta-learning based knowledge extrapolation for knowledge graphs in the federated setting,” ArXiv preprint , 2022
2022
Later among the works it cites.
M. Chen, W. Zhang, Y. Zhu, H. Zhou, Z. Yuan, C. Xu, and H. Chen, “Meta-knowledge transfer for inductive knowledge graph embedding,” in Proc. of SIGIR , 2022
2022
Later among the works it cites.
H. Zha, Z. Chen, and X. Yan, “Inductive relation prediction by BERT,” in Proc. of AAAI , 2022
2022
Later among the works it cites.
B. Peng, S. Liang, and M. Islam, “Bi-link: Bridging inductive link predictions from text via contrastive learning of transformers and prompts,” ArXiv preprint , 2022
2022
Later among the works it cites.
Y. Zhang, Y. Qian, Y. Ye, and C. Zhang, “Adapting distilled knowledge for few-shot relation reasoning over knowledge graphs,” in Proc. of SDM , 2022
2022
Later among the works it cites.
Y. Yao, Z. Zhang, Y. Xu, and C. Li, “Data augmentation for few-shot knowledge graph completion from hierarchical perspective,” in Proc. of COLING , 2022
2022
Later among the works it cites.
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
Later among the works it cites.
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
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 Proc. of WSDM , 2022
2022
Later among the works it cites.
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
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 Proc. of EMNLP Findings , 2022
2022
Later among the works it cites.
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 , 2022
2022
Later among the works it cites.
B. AlKhamissi, M. Li, A. Celikyilmaz, M. Diab, and M. Ghazvininejad, “A review on language models as knowledge bases,” ArXiv preprint , 2022
2022
Later among the works it cites.
B. Dhingra, J. R. Cole, J. M. Eisenschlos, D. Gillick, J. Eisenstein, and W. W. Cohen, “Time-aware language models as temporal knowledge bases,” Transactions of the Association for Computational Linguistics , 2022
2022
Later among the works it cites.
Z. Meng, F. Liu, E. Shareghi, Y. Su, C. Collins, and N. Collier, “Rewire-then-probe: A contrastive recipe for probing biomedical knowledge of pre-trained language models,” in Proc. of ACL , 2022
2022
Later among the works it cites.
D. Sui, X. Zeng, Y. Chen, K. Liu, and J. Zhao, “Joint entity and relation extraction with set prediction networks,” IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Closest in time.
J. Sun, C. Xu, L. Tang, S. Wang, C. Lin, Y. Gong, H.-Y. Shum, and J. Guo, “Think-on-graph: Deep and responsible reasoning of large language model with knowledge graph,” ArXiv preprint , 2023
2023
Closest in time.
Y. Wen, Z. Wang, and J. Sun, “Mindmap: Knowledge graph prompting sparks graph of thoughts in large language models,” ArXiv preprint , 2023
2023
Closest in time.