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Knowledge graphs play a vital role in numerous artificial intelligence tasks, yet they frequently face the issue of incompleteness.
G. A. Miller, “Wordnet: a lexical database for english,” Communications of the ACM , vol. 38, no. 11, pp. 39–41, 1995
1995
Earlier work this paper cites.
F. M. Suchanek, G. Kasneci, and G. Weikum, “Yago: a core of semantic knowledge,” in WWW . ACM, 2007, pp. 697–706
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 SIGMOD , 2008, pp. 1247–1250
2008
Earlier work this paper cites.
R. Socher, D. Chen, C. D. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base completion,” in NIPS , 2013, pp. 926–934
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 NIPS , 2013, pp. 2787–2795
2013
Earlier work this paper cites.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph and text jointly embedding,” in EMNLP , 2014
2014
Earlier work this paper cites.
Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in AAAI , 2014
2014
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 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 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 ACL , 2015, pp. 687–696
2015
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 KDD . ACM, 2016, pp. 353–362
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 AAAI , 2016
2016
Earlier work this paper cites.
Z. Wang and J.-Z. Li, “Text-enhanced representation learning for knowledge graph.” in IJCAI , 2016, pp. 1293–1299
2016
Earlier work this paper cites.
H. Xiao, M. Huang, and X. Zhu, “TransG: A generative model for knowledge graph embedding,” in ACL , vol. 1, 2016, pp. 2316–2325
2016
Earlier work this paper cites.
G. Ji, K. Liu, S. He, and J. Zhao, “Knowledge graph completion with adaptive sparse transfer matrix,” in AAAI , 2016
2016
Earlier work this paper cites.
W. Cui, Y. Xiao, H. Wang, Y. Song, S.-w. Hwang, and W. Wang, “KBQA: learning question answering over qa corpora and knowledge bases,” Proceedings of the VLDB Endowment , vol. 10, no. 5, pp. 565–576, 2017
2017
Cited alongside, same era.
Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE TKDE , vol. 29, no. 12, pp. 2724–2743, 2017
2017
Cited alongside, same era.
H. Xiao, M. Huang, L. Meng, and X. Zhu, “SSP: semantic space projection for knowledge graph embedding with text descriptions,” in AAAI , 2017
2017
Cited alongside, same era.
J. Xu, X. Qiu, K. Chen, and X. Huang, “Knowledge graph representation with jointly structural and textual encoding,” in IJCAI , 2017, pp. 1318–1324
2017
Cited alongside, same era.
B. An, B. Chen, X. Han, and L. Sun, “Accurate text-enhanced knowledge graph representation learning,” in NAACL , 2018, pp. 745–755
2021
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 Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 4281–4294
2022
Later among the works it cites.
J. Lovelace and C. Rose, “A framework for adapting pre-trained language models to knowledge graph completion,” in Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing , 2022, pp. 5937–5955
2022
Later among the works it cites.
Z. Du, Y. Qian, X. Liu, M. Ding, J. Qiu, Z. Yang, and J. Tang, “Glm: General language model pretraining with autoregressive blank infilling,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 320–335
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2018
Cited alongside, same era.
T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2d knowledge graph embeddings,” in AAAI , 2018, pp. 1811–1818
2018
Cited alongside, same era.
D. Q. Nguyen, D. Q. Nguyen, T. D. Nguyen, and D. Phung, “A convolutional neural network-based model for knowledge base completion and its application to search personalization,” Semantic Web , 2018
2018
Cited alongside, same era.
Z. Zhang, F. Zhuang, M. Qu, F. Lin, and Q. He, “Knowledge graph embedding with hierarchical relation structure,” in EMNLP , 2018, pp. 3198–3207
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in NAACL , 2019, pp. 4171–4186
2019
Cited alongside, same era.
D. Nathani, J. Chauhan, C. Sharma, and M. Kaul, “Learning attention-based embeddings for relation prediction in knowledge graphs,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2019, pp. 4710–4723
2019
Cited alongside, same era.
2022
Later among the works it cites.
A. Saxena, A. Kochsiek, and R. Gemulla, “Sequence-to-sequence knowledge graph completion and question answering,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) , 2022, pp. 2814–2828
2022
Later among the works it cites.
C. Chen, Y. Wang, B. Li, and K.-Y. Lam, “Knowledge is flat: A seq2seq generative framework for various knowledge graph completion,” in Proceedings of the 29th International Conference on Computational Linguistics , 2022, pp. 4005–4017
2022
Later among the works it cites.
J. Youn and I. Tagkopoulos, “KGLM: Integrating knowledge graph structure in language models for link prediction,” in Proceedings of the 12th Joint Conference on Lexical and Computational Semantics (*SEM 2023) , A. Palmer and J. Camacho-collados, Eds. Toronto, Canada: Association for Computational Linguistics, Jul. 2023, pp. 217–224. [Online]. Available: https://aclanthology.org/2023.starsem-1.20
2023
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2023
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OpenAI, “Gpt-4 technical report,” 2023
2023
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2023
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X. Xie, Z. Li, X. Wang, Y. Zhu, N. Zhang, J. Zhang, S. Cheng, B. Tian, S. Deng, F. Xiong, and H. Chen, “Lambdakg: A library for pre-trained language model-based knowledge graph embeddings,” 2023
2023
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Y. Zhu, X. Wang, J. Chen, S. Qiao, Y. Ou, Y. Yao, S. Deng, H. Chen, and N. Zhang, “Llms for knowledge graph construction and reasoning: Recent capabilities and future opportunities,” 2023
2023
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Y. Wei, Q. Huang, Y. Zhang, and J. Kwok, “Kicgpt: Large language model with knowledge in context for knowledge graph completion,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 8667–8683
2023
Closest in time.