[1] Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for embeddings for modeling multi-relational data[C]//Proceedings of the 26th International Conference on Neural Information Processing Systems 2013. Red Hook, NY, USA: Curran Associates Inc., 2013: 2782- 2795.
[2] Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes[C]//Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence 2014. Québec City, Québec, Canada: AAAI Press, 2014: 1112-1119.
[3] Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion[C]//Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence 2015. Austin, Texas: AAAI Press, 2015: 2181-2187.
[4] Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix[C]//Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing 2015. Beijing, China: Association for Computational Linguistics, 2015: 687-696.
[5] Ji G, Liu K, He S, et al. Knowledge graph completion with adaptive sparse transfer matrix[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence 2016. Phoenix, Arizona: AAAI Press, 2016: 985-991.
[6] Fan M, Zhou Q, Chang E, et al. Transition-based knowledge graph embedding with relational mapping properties[C]//Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing 2014. Chulalongkorn University, Phuket, Thailand: Department of Linguis⁃tics, 2014: 328-337.
[7] Xiao H, Huang M, Zhu X. From one point to a manifold: Knowledge graph embedding for precise link prediction[C]//Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence 2016. New York, USA: AAAI Press, 2016: 1315-1321.
[8] Feng J, Huang M, Wang M, et al. Knowledge graph embedding by flexible translation[C]//Proceedings of the Fifteenth International Conference on Principles of Knowledge Representation and Reasoning 2016. Cape Town, South Africa: AAAI Press, 2016: 557-560.
[9] Xiao H, Huang M, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding[J]. arXiv preprint, arXiv:1509.05490, 2015.
[10] He S, Liu K, Ji G, et al. Learning to represent knowledge graphs with gaussian embedding[C]//Proceedings of the 24th ACM International on Conference on Information and Knowledge Management 2015. NY, USA: Association for Computing Machinery, New York, 2015: 623-632.
[11] Xiao H, Huang M, Zhu X. TransG: A generative model for knowledge graph embedding[C]//Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics 2016. Berlin, Germany: Association for Computational Linguistics, 2016: 2316-2325.
[12] Sun Z Q, Deng Z H, Nie J Y, et al. Rotate: Knowledge graph embedding by relational rotation in complex space[C]//In International Conference on Learning Representations, Ernest N 2019. New Orleans: Morial Convention Center, 2019: 1-18.
[13] Zhang Z Q, Cai J Y, Zhang Y D, et al. Learning hierarchy-aware knowledge graph embeddings for link prediction[C]//The Thirty-Fourth AAAI Conference on Artificial Intelligence 2020. New York, USA: AAAI Press, 2020: 3065-3072.
[14] Nickel M, Tresp V, Kriegel H P. A three-way model for collective learning on multi-relational data[C]//Proceedings of the 28th International Conference on International Conference on Machine Learning 2011. Madison, WI, USA: Omnipress, 2011: 809-816.
[15] García-Durán A, Bordes A, Usunier N. Effective blending of two and three-way interactions for modeling multi-relational data[C]//Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Berlin, Heidelberg: Springer, 2014: 434-449.
[16] Yang B, Yih W, He X, et al. Embedding entities and relations for learning and inference in knowledge bases[C]//International Conference on Learning Representations 2015. San Diego, CA, USA: Conference Track Proceedings, 2015: 141-153.
[17] Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs[C]//Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence 2016. Phoenix, Arizona: AAAI Press, 2016: 1955-1961.
[18] Trouillon T, Welbl J, Riedel S, et al. Complex Embeddings for Simple Link Prediction[C]//Proceedings of the 33rd International Conference on International Conference on Machine Learning-Volume 48 2016. New York, USA: JMLR.org, 2016: 2071-2080.
[19] Bordes A, Glorot X, Weston J, et al. A semantic matching energy function for learning with multi-relational data[J]. Machine Learning, 2014, 94(5): 233-259.
[20] Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion[C]//Twenty-seventh Conference on Neural Information Processing Systems 2013. Lake Tahoe, Nevada, USA: Curran Associates, 2013: 926-934.
[21] Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion[C]//Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2014. New York, USA: Association for Computing Machinery, 2014: 601-610.
[22] Liu Q, Jiang H, Evdokimov A, et al. Probabilistic reasoning via deep learning: Neural association models[C]//25th International Joint Conference on Artificial Intelligence 2016. New York, USA: Deep Learning for Artificial Intelligence, 2016: 271-278.
[23] Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2D knowledge graph embeddings[C]//32nd AAAI Conference on Artificial Intelligence, AAAI 2018. New Orleans, Louisiana USA: AAAI Publications, 2018: 1811-1818.
[24] Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling relational data with graph convolutional networks[C]//European Semantic Web Conference. Cham: Springer, 2018:593-607.
[25] Shang C, Tang Y, Huang J, et al. End-to-end structureaware convolutional networks for knowledge base completion[C]. The Thirty-Third AAAI Conference on Artificial Intelligence. Honolulu, Hawaii, USA: AAAI Press, 2019, 33: 3060-3067.
[26] Vashishth S, Sanyal S, Nitin V, et al. Compositionbased multi-relational graph convolutional networks[J]. arXiv preprint, arXiv:1911.03082, 2019.
[27] Carl A, Ivana Balažević, Timothy H. Interpreting knowledge graph relation representation from word embeddings[C]//The Ninth International Conference on Learning Representations 2021. USA: Virtual Conference, 2021: 1-16.
[28] 方阳, 赵翔, 谭真, 等 . 一种改进的基于翻译的知识图谱表示方法[J]. 计算机研究与发展, 2018, 55(1): 139-150.
[29] 彭敏, 黄婷, 田纲, 等 . 聚合邻域信息的联合知识表示模型[J]. 中文信息学报, 2021, 35(5): 46-54.
[30] 李鑫超, 李培峰, 朱巧明. 一种基于改进向量投影距离的知识图谱表示方法[J]. 计算机科学, 2020, 47(4): 189-193.
[31] 文洋, 张茂元, 周礼全, 等 . 基于实体相似性的知识表示学习方法[J]. 计算机应用研究, 2021, 38(4): 1008-1012.
[32] 陈恒, 王维美, 李冠宇, 等 . 四元数关系旋转的知识图谱补全模型[J]. 计算机科学, 2021, 48(5): 225-231.
