A Condensed Transition Graph Framework for Zero-shot Link Prediction with Large Language Models

D. A. Lindberg, B. L. Humphreys, and A. T. McCray, “The unified medical language system,” Yearbook of medical informatics , vol. 2, no. 01, pp. 41–51, 1993

1993

Earlier work this paper cites.

S. Kok and P. Domingos, “Statistical predicate invention,” in Proceedings of the 24th international conference on Machine learning , 2007, pp. 433–440

2007

Earlier work this paper cites.

A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” Advances in neural information processing systems , vol. 26, 2013

2013

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,” arXiv preprint arXiv:1412.6575 , 2014

Original

2014

Earlier work this paper cites.

T. N. Kipf and M. Welling, “Semi-supervised classification with graph convolutional networks,” arXiv preprint arXiv:1609.02907 , 2016

Original

2016

Earlier work this paper cites.

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,” arXiv preprint arXiv:1711.05851 , 2017

Original

2017

Earlier work this paper cites.

S. M. Kazemi and D. Poole, “Simple embedding for link prediction in knowledge graphs,” Advances in neural information processing systems , vol. 31, 2018

2018

Earlier work this paper cites.

I. Balažević, C. Allen, and T. M. Hospedales, “Tucker: Tensor factorization for knowledge graph completion,” arXiv preprint arXiv:1901.09590 , 2019

Original

2019

Earlier work this paper cites.

L. Yao, C. Mao, and Y. Luo, “Kg-bert: Bert for knowledge graph completion,” arXiv preprint arXiv:1909.03193 , 2019

Original

2019

Earlier work this paper cites.

C. Fu, T. Chen, M. Qu, W. Jin, and X. Ren, “Collaborative policy learning for open knowledge graph reasoning,” arXiv preprint arXiv:1909.00230 , 2019

Original

2019

Earlier work this paper cites.

N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” arXiv preprint arXiv:1908.10084 , 2019

Original

2019

Earlier work this paper cites.

M. Li, Z. Zhou, and Y. Wang, “Multi-fusion chinese wordnet (mcw): Compound of machine learning and manual correction,” arXiv preprint arXiv:2002.01761 , 2020

Original

2020

Cited alongside, same era.

P. Qin, X. Wang, W. Chen, C. Zhang, W. Xu, and W. Y. Wang, “Generative adversarial zero-shot relational learning for knowledge graphs,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 34, no. 05, 2020, pp. 8673–8680

2020

Cited alongside, same era.

W. Zeng, X. Zhao, W. Wang, J. Tang, and Z. Tan, “Degree-aware alignment for entities in tail,” in Proceedings of the ACM SIGIR , 2020, pp. 811–820

2020

Cited alongside, same era.

M. Wang, L. Qiu, and X. Wang, “A survey on knowledge graph embeddings for link prediction,” Symmetry , vol. 13, no. 3, p. 485, 2021

2021

Cited alongside, same era.

C. Ling, X. Zhao, J. Lu, C. Deng, C. Zheng, J. Wang, T. Chowdhury, Y. Li, H. Cui, T. Zhao et al. , “Domain specialization as the key to make large language models disruptive: A comprehensive survey,” arXiv preprint arXiv:2305.18703 , vol. 2305, 2023

Original

2023

Later among the works it cites.

R. Ye, C. Zhang, R. Wang, S. Xu, and Y. Zhang, “Natural language is all a graph needs,” arXiv preprint arXiv:2308.07134 , 2023

Original

2023

Later among the works it cites.

G. Michel, G. Nikolentzos, J. F. Lutzeyer, and M. Vazirgiannis, “Path neural networks: Expressive and accurate graph neural networks,” in International Conference on Machine Learning . PMLR, 2023, pp. 24 737–24 755

2023

Later among the works it cites.

C. Ling, X. Zhang, X. Zhao, Y. Liu, W. Cheng, M. Oishi, T. Osaki, K. Matsuda, H. Chen, and L. Zhao, “Open-ended commonsense reasoning with unrestricted answer candidates,” in Findings of the Association for Computational Linguistics: EMNLP 2023 , 2023, pp. 8035–8047

2023