مرور روش‌های جاسازی گراف‌های دانش

[1] A. Singhal, “Introducing the Knowledge Graph: Things, not strings,” Official Google Blog, 2012, [Online]. Available: https://blog.google/products/search/introducing-knowledge-graph-things-not/
[2] X. Wang et al., “Learning intents behind interactions with knowledge graph for recommendation,” in Proc. Assoc. Comput. Mach. (ACM), New York, NY, USA, 2021, doi: 10.1145/3442381.3450133.
[3] X. Chen, S. Jia, and Y. Xiang, “A review: Knowledge reasoning over knowledge graph,” Expert Syst. Appl., vol. 141, 2020, doi: 10.1016/j.eswa.2019.112948.
[4] X. Li et al., “Entity-relation extraction as multi-turn question answering,” in Proc. 57th Annu. Meeting Assoc. Comput. Linguistics (ACL), Florence, Italy, 2019, doi: 10.18653/v1/P19-1129.
[5] F. Zhang, N.J. Yuan, D. Lian, X. Xie, and W.Y. Ma, “Collaborative knowledge base embedding for recommender systems,” in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, New York, NY, USA, 2016, doi: 10.1145/2939672.2939673.
[6] T. Shen et al., “Exploiting structured knowledge in text via graph-guided representation learning,” in Proc. 2020 Conf. Empirical Methods Nat. Lang. Process. (EMNLP), Abu Dhabi, UAE, 2020, doi: 10.48550/arXiv.2004.14224.
[7] X. Zou, “A survey on application of knowledge graph,” in Proc. 4th Int. Conf. Control Eng. Artif. Intell., Singapore, 2020, doi: 10.1088/1742-6596/1487/1/012016.
[8] M. Chavoshi and S.M. Babamir, “Extracting Rules from Specifications and Their Modeling using Colored Fuzzy Petri-Nets,” Soft Comput. J., vol. 11, no. 1, pp. 22-47, 2022, doi: 10.22052/scj.2022.246562.1078 [In Persian].
[9] A. Keypour, “Link Prediction in Social Networks through classifiers combination,” Soft Comput. J., vol. 4, no. 2, pp. 2-17, 2016, dor: 20.1001.1.23223707.1394.4.2.54.4 [In Persian].
[10] S.M. Vahidipour and R. Karami, “Link prediction framework based on graph neural networks using subgraphs,” Soft Comput. J., vol. 14, no. 1, pp. 18-35, 2025, doi: 10.22052/scj.2024.253458.1179 [In Persian].
[11] X. Wang and L. Fu, “AceMap: Knowledge discovery through academic graph,” arXiv preprint arXiv:2403.02576, 2024, doi: 10.48550/arXiv.2403.02576.
[12] S. Ji, S. Pan, E. Cambria, P. Marttinen, and P.S. Yu, “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE Trans. Neural Netw. Learn. Syst., vol. 33, no. 2, 2022, doi: 10.1109/TNNLS.2021.3070843.
[13] Welcome to Wikidata, Jan. 22, 2023. [Online]. Available: https://www.wikidata.org/wiki/Wikidata:Main_Page.
[14] W. Hu, M. Fey, M. Zitnik, and J. Leskovec, “Open graph benchmark: Datasets for machine learning on graphs,” in Proc. 34th Conf. Neural Inf. Process. Syst. (NeurIPS), Vancouver, BC, Canada, 2020, doi: 10.48550/arXiv.2005.00687.
[15] Z. Chen et al., “Knowledge graph completion: A review,” IEEE Access, vol. 8, 2020, doi: 10.1109/ACCESS.2020.3030076.
[16] Y. Dai, S. Wan, N.N. Xiong, and W. Guo, “A survey on knowledge graph embedding: Approaches, applications and benchmarks,” Electronics, vol. 9, no. 5, 2020, doi: 10.3390/electronics9050750.
[17] A. Bordes, N. Usunier, and A. Garcia-Duran, “Translating embeddings for modeling multi-relational data,” in Proc. Neural Inf. Process. Syst. (NIPS), 2013, pp. 2787–2795.
[18] F. Bianchi, G. Rossiello, L. Costabello, M. Palmonari, and P. Minervini, “Knowledge graph embeddings and explainable AI,” arXiv preprint arXiv:2004.14843, 2020, doi: 10.48550/arXiv.2004.14843.
[19] Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Proc. 28th AAAI Conf. Artif. Intell., 2014, doi: 10.1609/aaai.v28i1.8870.
[20] X. Wang, W. Li, and H Tong, "A comparative study of TransE and TransH algorithms in spatial address representation learning," Biomed. J. Sci. Tech. Res., vol. 26, no. 5, 2020, doi: 10.26717/BJSTR.2020.26.004427.
[21] Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proc. 29th AAAI Conf. Artif. Intell., 2015, doi: 10.1609/aaai.v29i1.9491.
[22] Z. Sun, Z.H. Deng, J.Y. Nie, and J. Tang, “RotatE: Knowledge graph embedding by relational rotation in complex space,” in Proc. Int. Conf. Learn. Representations (ICLR), 2019, doi: 10.48550/arXiv.1902.10197.
[23] L. Chao, J. He, T. Wang, and W. Chu, “PairRE: Knowledge graph embeddings via paired relation vectors,” arXiv preprint arXiv:2011.03798, 2021, doi: 10.48550/arXiv.2011.03798.
[24] L. Yu et al., “TripleRE: Knowledge graph embeddings via triple relation vectors,” arXiv preprint arXiv:2209.08271, 2022, doi: 10.48550/arXiv.2209.08271.
[25] B. Yang, W. Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in Proc. Int. Conf. Learn. Representations (ICLR), San Diego, CA, USA, 2015, doi: 10.48550/arXiv.1412.6575.
[26] F. Hitchcock, “The expression of a tensor or a polyadic as a sum of products,” J. Math. Phys., vol. 6, pp. 164-189, 1927.
[27] T. Trouillon, J. Welbl, S. Riedel, E. Gaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in Proc. Int. Conf. Mach. Learn. (ICML), New York, NY, USA, 2016, doi: 10.48550/arXiv.1606.06357.
[28] S. Zhang, Y. Tay, L. Yao, and Q. Liu, “Quaternion knowledge graph embeddings,” in Proc. 33rd Conf. Neural Inf. Process. Syst. (NeurIPS), Vancouver, BC, Canada, 2019, doi: 10.48550/arXiv.1904.10281.
[29] H. Liu, Y. Wu, and Y. Yang, “Analogical inference for multi-relational embeddings,” in Proc. 34th Int. Conf. Mach. Learn. (ICML), 2017, doi: 10.48550/arXiv.1705.02426.
[30] S.M. Kazemi and D. Poole, “SimplE embedding for link prediction in knowledge,” in Proc. 32nd Conf. Neural Inf. Process. Syst. (NeurIPS), Montreal, QC, Canada, 2018, doi: 10.48550/arXiv.1802.04868.
[31] A. Rossi, D. Barbosa, D. Firmani, A. Matinata, and P. Merialdo, “Knowledge graph embedding for link prediction: A comparative analysis,” ACM Trans. Knowl. Discovery Data, vol. 15, no. 2, 2021, doi: 10.1145/3424672.
[32] Q. Xu et al., “MlpE: Knowledge graph embedding with multilayer perceptron networks,” in IEEE SmartWorld Ubiquit. Intell. Comput. Scalable Comput. Commun. Digit. Twin Priv. Comput. Metaverse Auton. Trusted Veh. (SmartWorld/UIC/ScalCom/DigitalTwin/PriComp/Meta), Haikou, China, 2022, pp. 856-863, doi: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00130..
[33] T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolution 2D knowledge graph embeddings,” in Proc. 32nd AAAI Conf. Artif. Intell., 2018, doi: 10.48550/arXiv.1707.01476.
[34] Y. Zhang, Q. Yao, and L. Chen, “Interstellar: Searching recurrent architecture for knowledge graph embedding,” Adv. Neural Inf. Process. Syst., vol. 33, 2020, doi: 10.48550/arXiv.1911.07132.
[35] Y. Zhang, W. Dai, Q. Yao, and L. Chen, “AutoSF: Searching scoring functions for knowledge graph embedding,” in Proc. Int. Conf. Data Eng. (ICDE), 2020, doi: 10.48550/arXiv.1904.11682.
[36] “Leaderboards for link property prediction,” 2024, [Online]. Available: https://ogb.stanford.edu/docs/leader_linkprop/#ogbl-biokg
[37] Y. Zhang, Q. Yao, and J.T.Y. Kwok, “Bilinear scoring function search for knowledge graph learning,” arXiv preprint arXiv:2107.00184, 2021, doi: 10.48550/arXiv.2107.00184.
[38] F. Hutter, L. Kotthoff, and J. Vanschoren, Automated Machine Learning: Methods, Systems, Challenges. Springer Nature, 2019.
[39] L. Guo, Z. Sun, and W. Hu, “Learning to exploit long-term relational dependencies in knowledge graphs,” in Proc. 36th Int. Conf. Mach. Learn. (ICML), 2019.
[40] X.L. Dong et al., “Knowledge vault: A web-scale approach to probabilistic knowledge fusion,” in Proc. 20th ACM SIGKDD Int. Conf. Knowl. Discovery Data Mining, 2014, doi: 10.48550/arXiv.1905.04914.
[41] M. Nickel, V. Tresp, and H.P. Kriegel, “A three-way model for collective learning on multi-relational data,” in Proc. 28th Int. Conf. Mach. Learn. (ICML), 2011, doi: 10.1145/2623330.2623623.
[42] T. Shen, F. Zhang, and J. Cheng, “A comprehensive overview of knowledge graph completion,” Knowl.-Based Syst., vol. 255, 2022, doi: 10.1016/j.knosys.2022.109597.
[43] R. Socher, D. Chen, C. Manning, and A. Ng, “Reasoning with neural tensor networks for knowledge base completion,” Adv. Neural Inf. Process. Syst., vol. 26, 2013. 
[44] M. Wang, L. Qiu, and X. Wang, “A survey on knowledge graph embeddings for link prediction,” Symmetry, vol. 13, no. 3, 2021, doi: 10.3390/sym13030485.
[45] Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Trans. Knowl. Data Eng., vol. 29, no. 12, 2017, doi: 10.1109/TKDE.2017.2754499.