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[61] Li JL, Tang TY, Zhao WX, Wei ZC, Yuan NJ, Wen JR. Few-shot knowledge graph-to-text generation with pretrained language models.
In: Proc. of the 2021 Association for Computational Linguistics: ACL-IJCNLP 2021. Association for Computational Linguistics, 2021.
1558–1568. [doi: 10.18653/v1/2021.findings-acl.136]
[62] Zhao ZH, Wallace E, Feng S, Klein D, Singh S. Calibrate before use: Improving few-shot performance of language models. In: Proc. of
the 38th Int’l Conf. on Machine Learning. JMLR.org, 2021. 12697–12706.
[63] Li Q, Chen Z, Ji C, Jiang SQ, Li JX. LLM-based multi-level knowledge generation for few-shot knowledge graph completion. In: Proc.
of the 33rd Int’l Joint Conf. on Artificial Intelligence. Jeju: IJCAI, 2024. 2135–2143.
[64] Nickel M, Tresp V, Kriegel HP. A three-way model for collective learning on multi-relational data. In: Proc. of the 28th Int’l Conf. on
Machine Learning. Bellevue: Omnipress, 2011. 809–816.
[65] Yang BS, Yih WT, He XD, Gao JF, Deng L. Embedding entities and relations for learning and inference in knowledge bases. In: Proc.
of the 3rd Int’l Conf. on Learning Representations. San Diego, 2015.
[66] Trouillon T, Welbl J, Riedel S, Gaussier É, Bouchard G. Complex embeddings for simple link prediction. In: Proc. of the 33rd Int’l
Conf. on Machine Learning. New York: JMLR.org, 2016. 2071–2080.
[67] Zhang S, Tay Y, Yao Y, Li Q. Quaternion knowledge graph embeddings. In: Proc. of the 33rd Int’l Conf. on Neural Information
Processing Systems. Curran Associates Inc., 2019. 2731–2741.
[68] Balazevic I, Allen C, Hospedales T. TuckER: Tensor factorization for knowledge graph completion. In: Proc. of the 2019 Conf. on
Empirical Methods in Natural Language Processing and the 9th Int’l Joint Conf. on Natural Language Processing (EMNLP-IJCNLP).
Hong Kong: Association for Computational Linguistics, 2019. 5184–5193. [doi: 10.18653/v1/D19-1522]
[69] Liu HX, Wu YX, Yang YM. Analogical inference for multi-relational embeddings. In: Proc. of the 34th Int’l Conf. on Machine
Learning. Sydney: JMLR.org, 2017. 2168–2178.
[70] Dettmers T, Minervini P, Stenetorp P, Riedel S. Convolutional 2D knowledge graph embeddings. In: Proc. of the 32nd AAAI Conf. on
Artificial Intelligence. New Orleans: AAAI, 2018. 1811–1818. [doi: 10.1609/aaai.v32i1.11573]
[71] Vashishth S, Sanyal S, Nitin V, Agrawal N, Talukdar P. InteractE: Improving convolution-based knowledge graph embeddings by
increasing feature interactions. In: Proc. of the 34th AAAI Conf. on Artificial Intelligence. New York: AAAI, 2020. 3009–3016. [doi: 10.
1609/aaai.v34i03.5694]
[72] Nguyen DQ, Nguyen TD, Nguyen DQ, Phung D. A novel embedding model for knowledge base completion based on convolutional
neural network. In: Proc. of the 2018 Conf. of the North American Chapter of the Association for Computational Linguistics: Human
Language Technologies. New Orleans: Association for Computational Linguistics, 2018. 327–333. [doi: 10.18653/v1/N18-2053]
[73] Jiang XT, Wang Q, Wang B. Adaptive convolution for multi-relational learning. In: Proc. of the 2019 Conf. of the North American
Chapter of the Association for Computational Linguistics: Human Language Technologies. Minneapolis: Association for Computational
Linguistics, 2019. 978–987. [doi: 10.18653/v1/N19-1103]
[74] Schlichtkrull M, Kipf TN, Bloem P, Van den Berg R, Titov I, Welling M. Modeling relational data with graph convolutional networks.
In: Proc. of the 15th Int’l Conf. on the Semantic Web. Heraklion: Springer, 2018. 593–607. [doi: 10.1007/978-3-319-93417-4_38]
[75] Cai L, Yan B, Mai GC, Janowicz K, Zhu R. TransGCN: Coupling transformation assumptions with graph convolutional networks for
link prediction. In: Proc. of the 10th Int’l Conf. on Knowledge Capture. Marina: ACM, 2019. 131–138. [doi: 10.1145/3360901.3364441]
[76] Shang C, Tang Y, Huang J, Bi JB, He XD, Zhou BW. End-to-end structure-aware convolutional networks for knowledge base
completion. In: Proc. of the 33rd AAAI Conf. on Artificial Intelligence. Honolulu: AAAI, 2019. 3060–3067. [doi: 10.1609/aaai.v33i01.
33013060]
[77] Vashishth S, Sanyal S, Nitin V, Talukdar P. Composition-based multi-relational graph convolutional networks. In: Proc. of the 8th Int’l
Conf. on Learning Representations. Addis Ababa: OpenReview.net, 2020.
[78] Zhang TC, Tian X, Sun XH, Yu MH, Sun YH, Yu G. Overview on knowledge graph embedding technology research. Ruan Jian Xue
Bao/Journal of Software, 2023, 34(1): 277–311 (in Chinese with English abstract). http://www.jos.org.cn/1000-9825/6429.htm [doi: 10.
13328/j.cnki.jos.006429]
[79] Bordes A, Usunier N, García-Durán A, Weston J, Yakhnenko O. Translating embeddings for modeling multi-relational data. In: Proc. of
the 27th Int’l Conf. on Neural Information Processing Systems. Lake Tahoe: Curran Associates Inc., 2013. 2787–2795.
[80] Wang Z, Zhang JW, Feng JL, Chen Z. Knowledge graph embedding by translating on hyperplanes. In: Proc. of the 28th AAAI Conf. on
Artificial Intelligence. Québec City: AAAI, 2014. 1112–1119. [doi: 10.1609/aaai.v28i1.8870]
[81] Lin YK, Liu ZY, Sun MS, Liu Y, Zhu X. Learning entity and relation embeddings for knowledge graph completion. In: Proc. of the 29th
AAAI Conf. on Artificial Intelligence. Austin: AAAI, 2015. 2181–2187. [doi: 10.1609/aaai.v29i1.9491]
[82] Ji GL, He SZ, Xu LH, Liu K, Zhao J. Knowledge graph embedding via dynamic mapping matrix. In: Proc. of the 53rd Annual Meeting

