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Recently, ChatGPT, a representative large language model (LLM), has gained considerable attention due to its powerful emergent abilities.
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, and A. Askell, “Language models are few-shot learners,” in Adv. Neural Inform. Process. Syst. , 2020, pp. 1877–1901
1901
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 Adv. Neural Inf. Process. Syst. , 2013
2013
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” in Proc. 31st Conf. Neural Inform. Process. Syst. , 2017
2017
Earlier work this paper cites.
P. F. Christiano, J. Leike, T. Brown, M. Martic, S. Legg, and D. Amodei, “Deep reinforcement learning from human preferences,” in Adv. Neural Inf. Process. Syst. , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, I. Sutskever et al. , “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
D. Seyler, T. Dembelova, L. Del Corro, J. Hoffart, and G. Weikum, “A study of the importance of external knowledge in the named entity recognition task,” in Proc. 56th Ann. Meet. Assoc. Comput. Linguistics. , 2018, pp. 241–246
2018
Earlier work this paper cites.
R. Lu, X. Jin, S. Zhang, M. Qiu, and X. Wu, “A study on big knowledge and its engineering issues,” IEEE Trans. Knowl. Data Eng. , vol. 31, no. 9, pp. 1630–1644, 2019
2019
Earlier work this paper cites.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” in Proc. of the 17th Annu. Conf. of the North Amer. Chapter of the Assoc. for Comput. Linguistics: Hum. Lang. Technol. , 2019, pp. 4171–4186
2019
Earlier work this paper cites.
F. Petroni, T. Rocktäschel, P. Lewis, A. Bakhtin, Y. Wu, A. H. Miller, and S. Riedel, “Language models as knowledge bases?” in Proc. 2019 Conf. Empirical Methods Nat. Lang. Process. and 9th Int. Joint Conf. Nat. Lang. Process. , 2019, pp. 2463–2473
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
Z. Zhang, X. Han, Z. Liu, X. Jiang, M. Sun, and Q. Liu, “ERNIE: Enhanced language representation with informative entities,” in Proc. 57th Ann. Meet. Assoc. Comput. Linguistics. , 2019, pp. 1441–1451
2019
Earlier work this paper cites.
A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever et al. , “Language models are unsupervised multitask learners,” OpenAI blog , vol. 1, no. 8, pp. 1–9, 2019
2019
Earlier work this paper cites.
Z. Yang, Z. Dai, Y. Yang, J. Carbonell, R. R. Salakhutdinov, and Q. V. Le, “Xlnet: Generalized autoregressive pretraining for language understanding,” in Advances in neural information processing systems , 2019
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
R. Logan, N. F. Liu, M. E. Peters, M. Gardner, and S. Singh, “Barack’s wife hillary: Using knowledge graphs for fact-aware language modeling,” in Proc. 57th Annu. Meet. Assoc. Comput. Linguist. , 2019, pp. 5962–5971
2019
Earlier work this paper cites.
M. Sap, R. Le Bras, E. Allaway, C. Bhagavatula, N. Lourie, H. Rashkin, B. Roof, N. A. Smith, and Y. Choi, “Atomic: An atlas of machine commonsense for if-then reasoning,” in Proc. 33rd AAAI Conf. Artif. Intell. & 31st Innov. Appl. Artif. Intell. Conf. & 9th AAAI Symp. Educ. Adv. Artif. Intell. , 2019, p. 3027–3035
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
P. Zhong, D. Wang, and C. Miao, “Knowledge-enriched transformer for emotion detection in textual conversations,” in Proc. 2019 Conf. Empir. Methods Nat. Lang. Process. & 9th Int. Joint Conf. Nat. Lang. Process. , 2019, pp. 165–176
2019
Earlier work this paper cites.
M. E. Peters, M. Neumann, R. Logan, R. Schwartz, V. Joshi, S. Singh, and N. A. Smith, “Knowledge enhanced contextual word representations,” in Proc. 2019 Conf. Empir. Methods Nat. Lang. Process. & 9th Int. Joint Conf. Nat. Lang. Process. , 2019, pp. 43–54
2019
Earlier work this paper cites.
B. Y. Lin, X. Chen, J. Chen, and X. Ren, “KagNet: Knowledge-aware graph networks for commonsense reasoning,” in Proc. 2019 Conf. Empir. Methods Nat. Lang. Process. & 9th Int. Joint Conf. Nat. Lang. Process. , 2019, pp. 2829–2839
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
C. Wang, S. Liang, Y. Zhang, X. Li, and T. Gao, “Does it make sense? and why? a pilot study for sense making and explanation,” in Proc. 57th Ann. Meet. Assoc. Comput. Linguistics. , 2019, pp. 4020–4026
2019
Earlier work this paper cites.
T. Pires, E. Schlinger, and D. Garrette, “How multilingual is multilingual BERT?” in Proc. 57th Ann. Meet. Assoc. Comput. Linguistics. , 2019, pp. 4996–5001
2019
Earlier work this paper cites.
A. Bosselut, H. Rashkin, M. Sap, C. Malaviya, A. Celikyilmaz, and Y. Choi, “COMET: Commonsense transformers for automatic knowledge graph construction,” in Proc. 57th Ann. Meet. Assoc. Comput. Linguistics. , 2019, pp. 4762–4779
2019
Earlier work this paper cites.
C. Raffel, N. Shazeer, A. Roberts, K. Lee, S. Narang, M. Matena, Y. Zhou, W. Li, and P. J. Liu, “Exploring the limits of transfer learning with a unified text-to-text transformer,” J. of Mach. Learn. Res. , vol. 21, no. 140, pp. 1–67, 2020
2020
Earlier work this paper cites.
C. Wang, X. Liu, and D. Song, “Language models are open knowledge graphs,” arXiv:2010.11967 , 2020
2020
Earlier work this paper cites.
K. Clark, M.-T. Luong, Q. V. Le, and C. D. Manning, “ELECTRA: Pre-training text encoders as discriminators rather than generators,” in Proc. 8th Int. Conf. Learn. Representations , 2020
2020
Earlier work this paper cites.
M. Joshi, D. Chen, Y. Liu, D. S. Weld, L. Zettlemoyer, and O. Levy, “SpanBERT: Improving pre-training by representing and predicting spans,” Trans. Assoc. Comput. Linguist. , vol. 8, pp. 64–77, 2020
2020
Earlier work this paper cites.
Y. Wang, C. Sun, Y. Wu, J. Yan, P. Gao, and G. Xie, “Pre-training entity relation encoder with intra-span and inter-span information,” in Proc. 2020 Conf. Empirical Methods Nat. Lang. Process. , 2020, pp. 1692–1705
2020
Earlier work this paper cites.
M. Lewis, Y. Liu, N. Goyal, M. Ghazvininejad, A. Mohamed, O. Levy, V. Stoyanov, and L. Zettlemoyer, “BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension,” in Proc. 58th Ann. Meet. Assoc. Comput. Linguistics. , 2020, pp. 7871–7880
2020
Earlier work this paper cites.
T. Shen, Y. Mao, P. He, G. Long, A. Trischler, and W. Chen, “Exploiting structured knowledge in text via graph-guided representation learning,” in Proc. 2020 Conf. Empirical Methods Nat. Lang. Process. , 2020, p. 8980–8994
2020
Earlier work this paper cites.
S. Gehman, S. Gururangan, M. Sap, Y. Choi, and N. A. Smith, “RealToxicityPrompts: Evaluating neural toxic degeneration in language models,” in Find. Assoc. Comput. Linguist.: EMNLP 2020 , 2020, pp. 3356–3369
2020
Earlier work this paper cites.
W. Liu, P. Zhou, Z. Zhao, Z. Wang, Q. Ju, H. Deng, and P. Wang, “K-bert: Enabling language representation with knowledge graph,” in Proc. AAAI Conf. Artif. Intell. , 2020, pp. 2901–2908
2020
Earlier work this paper cites.
T. Sun, Y. Shao, X. Qiu, Q. Guo, Y. Hu, X. Huang, and Z. Zhang, “CoLAKE: Contextualized language and knowledge embedding,” in Proc. 28th Int. Conf. Comput. Linguistics , 2020, pp. 3660–3670
2020
Earlier work this paper cites.
Y. Zhang, J. Lin, Y. Fan, P. Jin, Y. Liu, and B. Liu, “Cn-hit-it. nlp at semeval-2020 task 4: Enhanced language representation with multiple knowledge triples,” in Proc. 14th Workshop Semant. Eval. , 2020, pp. 494–500
2020
Earlier work this paper cites.
I. Yamada, A. Asai, H. Shindo, H. Takeda, and Y. Matsumoto, “LUKE: Deep contextualized entity representations with entity-aware self-attention,” in Proc. 2020 Conf. Empir. Methods Nat. Lang. Process. , 2020, pp. 6442–6454
2020
Earlier work this paper cites.
N. Poerner, U. Waltinger, and H. Schütze, “E-BERT: Efficient-yet-effective entity embeddings for BERT,” in Find. Assoc. Comput. Linguist.: EMNLP 2020 , 2020, pp. 803–818
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
W. Chen, Y. Su, X. Yan, and W. Y. Wang, “KGPT: Knowledge-grounded pre-training for data-to-text generation,” in Proc. 2020 Conf. Empir. Methods Nat. Lang. Process. , 2020, pp. 8635–8648
2020
Earlier work this paper cites.
B. He, D. Zhou, J. Xiao, X. Jiang, Q. Liu, N. J. Yuan, and T. Xu, “BERT-MK: Integrating graph contextualized knowledge into pre-trained language models,” in Find. Assoc. Comput. Linguist.: EMNLP 2020 , 2020, pp. 2281–2290
2020
Earlier work this paper cites.
A. Lauscher, O. Majewska, L. F. R. Ribeiro, I. Gurevych, N. Rozanov, and G. Glavaš, “Common sense or world knowledge? investigating adapter-based knowledge injection into pretrained transformers,” in Proc. DeeLIO: 1st Workshop Knowl. Extract. Integr. Deep Learn. Archit. , 2020, pp. 43–49
2020
Earlier work this paper cites.
Y. Levine, B. Lenz, O. Dagan, O. Ram, D. Padnos, O. Sharir, S. Shalev-Shwartz, A. Shashua, and Y. Shoham, “SenseBERT: Driving some sense into BERT,” in Proc. 58th Annu. Meet. Assoc. Comput. Linguist. , 2020, pp. 4656–4667
2020
Earlier work this paper cites.
P. Ke, H. Ji, S. Liu, X. Zhu, and M. Huang, “SentiLARE: Sentiment-aware language representation learning with linguistic knowledge,” in Proc. 2020 Conf. Empir. Methods Nat. Lang. Process. , Online, 2020, pp. 6975–6988
2020
Earlier work this paper cites.
Y. Sun, S. Wang, Y. Li, S. Feng, H. Tian, H. Wu, and H. Wang, “Ernie 2.0: A continual pre-training framework for language understanding,” in Proc. AAAI Conf. Artif. Intell. , 2020, pp. 8968–8975
2020
Earlier work this paper cites.
H. Hayashi, Z. Hu, C. Xiong, and G. Neubig, “Latent relation language models,” in Proc. AAAI Conf. Artif. Intell. , 2020, pp. 7911–7918
2020
Earlier work this paper cites.
T.-Y. Chang, Y. Liu, K. Gopalakrishnan, B. Hedayatnia, P. Zhou, and D. Hakkani-Tur, “Incorporating commonsense knowledge graph in pretrained models for social commonsense tasks,” in Proc. DeeLIO: 1st Workshop Knowl. Extract. Integr. Deep Learn. Archit. , Nov. 2020, pp. 74–79
2020
Earlier work this paper cites.
Q. He, L. Wu, Y. Yin, and H. Cai, “Knowledge-graph augmented word representations for named entity recognition,” in Proc. AAAI Conf. Artif. Intell. , 2020, pp. 7919–7926
2020
Earlier work this paper cites.
J. Zhou, J. Tian, R. Wang, Y. Wu, W. Xiao, and L. He, “SentiX: A sentiment-aware pre-trained model for cross-domain sentiment analysis,” in Proc. 28th Int. Conf. Comput. Linguist. , 2020, pp. 568–579
2020
Cited alongside, same era.
A. Ghanbarpour and H. Naderi, “An attribute-specific ranking method based on language models for keyword search over graphs,” IEEE Trans. Knowl. Data Eng. , vol. 32, no. 1, pp. 12–25, 2020
2020
Cited alongside, same era.
J. Guan, F. Huang, Z. Zhao, X. Zhu, and M. Huang, “A knowledge-enhanced pretraining model for commonsense story generation,” Trans. Assoc. Comput. Linguist. , vol. 8, pp. 93–108, 2020
2020
Cited alongside, same era.
H. Ji, P. Ke, S. Huang, F. Wei, X. Zhu, and M. Huang, “Language generation with multi-hop reasoning on commonsense knowledge graph,” in Proc. 2020 Conf. Empir. Methods Nat. Lang. Process. , 2020, pp. 725–736
2020
Cited alongside, same era.
2022
Later among the works it cites.
X. Liu, D. Yin, J. Zheng, X. Zhang, P. Zhang, H. Yang, Y. Dong, and J. Tang, “Oag-bert: Towards a unified backbone language model for academic knowledge services,” in Proc. 28th ACM SIGKDD Conf. Knowl. Discov. Data Min. , 2022, p. 3418–3428
2022
Later among the works it cites.
T. Zhang, C. Wang, N. Hu, M. Qiu, C. Tang, X. He, and J. Huang, “Dkplm: Decomposable knowledge-enhanced pre-trained language model for natural language understanding,” in Proc. AAAI Conf. Artif. Intell. , 2022, pp. 11 703–11 711
2022
Later among the works it cites.
Y. Sun, Q. Shi, L. Qi, and Y. Zhang, “JointLK: Joint reasoning with language models and knowledge graphs for commonsense question answering,” in Proc. 2022 Conf. North Am. Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol. , 2022, pp. 5049–5060
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X. Yang and I. Tiddi, “Creative storytelling with language models and knowledge graphs,” in Proc. CIKM 2020 Workshops , 2020
2020
Cited alongside, same era.
A. Talmor, Y. Elazar, Y. Goldberg, and J. Berant, “olmpics-on what language model pre-training captures,” Trans. Assoc. Comput. Linguist. , vol. 8, pp. 743–758, 2020
2020
Cited alongside, same era.
B. Heinzerling and K. Inui, “Language models as knowledge bases: On entity representations, storage capacity, and paraphrased queries,” in Proc. 16th Conf. Eur. Chapter Assoc. Comput. Linguist. , 2021, pp. 1772–1791
2021
Cited alongside, same era.
C. Wang, P. Liu, and Y. Zhang, “Can generative pre-trained language models serve as knowledge bases for closed-book qa?” in Proc. 59th Annu. Meet. Assoc. Comput. Linguist. and 11th Int. Joint Conf. Nat. Lang. Process. , 2021, pp. 3241–3251
2021
Cited alongside, same era.
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. 59th Annu. Meet. Assoc. Comput. Linguist. and 11th Int. Joint Conf. Nat. Lang. Process. , 2021, pp. 1860–1874
2021
Cited alongside, same era.
2021
Cited alongside, same era.
P. He, X. Liu, J. Gao, and W. Chen, “Deberta: Decoding-enhanced bert with disentangled attention,” in International Conference on Learning Representations , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
2022
Later among the works it cites.
Q. Liu, D. Yogatama, and P. Blunsom, “Relational memory-augmented language models,” Trans. Assoc. Comput. Linguist. , vol. 10, pp. 555–572, 2022
2022
Later among the works it cites.
D. Yu, C. Zhu, Y. Yang, and M. Zeng, “Jaket: Joint pre-training of knowledge graph and language understanding,” in Proc. AAAI Conf. Artif. Intell. , 2022, pp. 11 630–11 638
2022
Later among the works it cites.
M. Yasunaga, A. Bosselut, H. Ren, X. Zhang, C. D. Manning, P. S. Liang, and J. Leskovec, “Deep bidirectional language-knowledge graph pretraining,” in Adv. Neural Inform. Process. Syst. , 2022, pp. 37 309–37 323
2022
Later among the works it cites.
M. Kang, J. Baek, and S. J. Hwang, “KALA: knowledge-augmented language model adaptation,” in Proc. 2022 Conf. North Am. Chapter Assoc. Comput. Linguist.: Hum. Lang. Technol. , 2022, pp. 5144–5167
2022
Later among the works it cites.
Q. Xie, J. A. Bishop, P. Tiwari, and S. Ananiadou, “Pre-trained language models with domain knowledge for biomedical extractive summarization,” Knowl. Based Syst. , vol. 252, p. 109460, 2022
2022
Later among the works it cites.
B. R. Andrus, Y. Nasiri, S. Cui, B. Cullen, and N. Fulda, “Enhanced story comprehension for large language models through dynamic document-based knowledge graphs,” in Proc. AAAI Conf. Artif. Intell. , 2022, pp. 10 436–10 444
2022
Later among the works it cites.
J. Wang, W. Huang, Q. Shi, H. Wang, M. Qiu, X. Li, and M. Gao, “Knowledge prompting in pre-trained language model for natural language understanding,” in Proc. 2022 Conf. Empir. Methods Nat. Lang. Process. , 2022, pp. 3164–3177
2022
Later among the works it cites.
2022
Later among the works it cites.
J. Li, Y. Katsis, T. Baldwin, H.-C. Kim, A. Bartko, J. McAuley, and C.-N. Hsu, “Spot: Knowledge-enhanced language representations for information extraction,” in Proc. 31st ACM Int. Conf. Inf. Knowl. Manage. , 2022, p. 1124–1134
2022
Later among the works it cites.
Z. Hu, Y. Xu, W. Yu, S. Wang, Z. Yang, C. Zhu, K.-W. Chang, and Y. Sun, “Empowering language models with knowledge graph reasoning for open-domain question answering,” in Proc. 2022 Conf. Empir. Methods Nat. Lang. Process. , 2022, pp. 9562–9581
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
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2022
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2022
Later among the works it cites.
Q. Wang, Y. Li, R. Zhang, K. Shu, Z. Zhang, and A. Zhou, “A scalable query-aware enormous database generator for database evaluation,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 5, pp. 4395–4410, 2023
2023
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2023
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2023
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2023
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L. Hu, Z. Liu, Z. Zhao, L. Hou, L. Nie, and J. Li, “A survey of knowledge enhanced pre-trained language models,” IEEE Trans. Knowl. Data Eng. , pp. 1–19, 2023
2023
Closest in time.
X. Liu, F. Zhang, Z. Hou, L. Mian, Z. Wang, J. Zhang, and J. Tang, “Self-supervised learning: Generative or contrastive,” IEEE Trans. on Knowl. Data Eng. , vol. 35, no. 1, pp. 857–876, 2023
2023
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2023
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R. Taori, I. Gulrajani, T. Zhang, Y. Dubois, X. Li, C. Guestrin, P. Liang, and T. B. Hashimoto, “Stanford alpaca: An instruction-following llama model,” 2023
2023
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OpenAI, “Gpt-4 technical report,” arXiv:2303.08774 , 2023
2023
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2023
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2023
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J.-W. Lu, C. Guo, X.-Y. Dai, Q.-H. Miao, X.-X. Wang, J. Yang, and F.-Y. Wang, “The chatgpt after: Opportunities and challenges of very large scale pre-trained models,” Acta Autom. Sin. , vol. 49, no. 4, pp. 705–717, 2023
2023
Closest in time.
P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing,” ACM Comput. Surv. , vol. 55, no. 9, pp. 1–35, 2023
2023
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2023
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2023
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2023
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2023
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2023
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2023
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R. Ding, X. Han, and L. Wang, “A unified knowledge graph augmentation service for boosting domain-specific nlp tasks,” in Find. Assoc. Comput. Linguist.: ACL 2023 , 2023, pp. 353–369
2023
Closest in time.
J. Baek, A. F. Aji, and A. Saffari, “Knowledge-augmented language model prompting for zero-shot knowledge graph question answering,” in Proc. 1st Workshop Nat. Lang. Reasoning Struct. Expl. , 2023, pp. 78–106
2023
Closest in time.
G. Lu, H. Yu, Z. Yan, and Y. Xue, “Commonsense knowledge graph-based adapter for aspect-level sentiment classification,” Neurocomput. , vol. 534, pp. 67–76, 2023
2023
Closest in time.
2023
Closest in time.
Y. Song, W. Zhang, Y. Ye, C. Zhang, and K. Zhang, “Knowledge-enhanced relation extraction in chinese emrs,” in Proc. 2022 5th Int. Conf. Mach. Learn. Nat. Lang. Process. , 2023, p. 196–201
2023
Closest in time.
Q. Wang, X. Cao, J. Wang, and W. Zhang, “Knowledge-aware collaborative filtering with pre-trained language model for personalized review-based rating prediction,” IEEE Trans. Knowl. Data Eng. , pp. 1–13, 2023
2023
Closest in time.
S. Liang, J. Shao, D. Zhang, J. Zhang, and B. Cui, “Drgi: Deep relational graph infomax for knowledge graph completion,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 3, pp. 2486–2499, 2023
2023
Closest in time.
Q. Lin, R. Mao, J. Liu, F. Xu, and E. Cambria, “Fusing topology contexts and logical rules in language models for knowledge graph completion,” Inf. Fusion , vol. 90, pp. 253–264, 2023
2023
Closest in time.
W. Li, R. Peng, and Z. Li, “Knowledge graph completion by jointly learning structural features and soft logical rules,” IEEE Trans. Knowl. Data Eng. , vol. 35, no. 3, pp. 2724–2735, 2023
2023
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2023
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2023
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2023
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R. Zhao, F. Zhao, G. Xu, S. Zhang, and H. Jin, “Can language models serve as temporal knowledge bases?” in Find. Assoc. Comput. Linguist.: EMNLP 2022 , 2022, pp. 2024–2037
2037
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