[1] H. Yu, H. Li, D. Mao, and Q. Cai, “A domain knowledge graph construction method based on Wikipedia,” J. Inf. Sci., vol. 47, no. 6, pp. 783–793, 2021
[2] M. Yahya, J. G. Breslin, and M. I. Ali, “Semantic web and knowledge graphs for industry 4.0,” Appl. Sci., vol. 11, no. 11, pp. 5110, 2021.
[3] A. Bordes, N. Usunier, A. Garcia-Durán, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” in Advances in Neural Information Processing Systems, Neural information processing systems foundation, 2013.
[4] M. Shamsfard and Y. Ghazanfari, “Augmenting farsnet with new relations and structures for verbs,” in Proceedings of the 8th Global WordNet Conference, GWC 2016, 2016, pp. 377–383.
[5] M. Asgari-Bidhendi, A. Hadian, and B. Minaei-Bidgoli, “FarsBase: The Persian knowledge graph,” Semant. Web, vol. 10, no. 6, pp. 1169–1196, 2019.
[6] S. Auer, C. Bizer, G. Kobilarov, J. Lehmann, R. Cyganiak, and Z. Ives, “DBpedia: A Nucleus for a Web of Open Data,” in The Semantic Web, K. Aberer, K.-S. Choi, N. Noy, D. Allemang, K.-I. Lee, L. Nixon, J. Golbeck, P. Mika, D. Maynard, R. Mizoguchi, G. Schreiber, and P. Cudré-Mauroux, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 722–735, 2007.
[7] K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor, “Freebase: a collaboratively created graph database for structuring human knowledge,” in In SIGMOD Conference, pp. 1247–1250, 2008.
[8] R. Navigli and S. P. Ponzetto, “BabelNet: The automatic construction, evaluation and application of a wide-coverage multilingual semantic network,” Artif. Intell., vol. 193, pp. 217–250, 2012.
[9] D. Vrandečić and M. Krötzsch, “Wikidata: A free collaborative knowledgebase,” Commun. ACM, vol. 57, no. 10, pp. 78–85, 2014.
[10] T. Pellissier Tanon, G. Weikum, and F. Suchanek, “YAGO 4: A Reason-able Knowledge Base,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) , pp. 583–596, 2020.
[11] A. Uyar and F. M. Aliyu, “Evaluating search features of Google Knowledge Graph and Bing Satori: Entity types, list searches and query interfaces,” Online Inf. Rev., vol. 39, no. 2, pp. 197–213, 2015.
[12] Z. Wang et al., “XLore: A large-scale English-Chinese bilingual knowledge graph,” in CEUR Workshop Proceedings, pp. 121–124, 2013.
[13] E. Montiel-Ponsoda and V. Rodr’iguez-Doncel, “Lynx: Building the legal knowledge graph for smart compliance services in multilingual Europe,” in Proceedings of the 1st workshop on LREC (language resources and technologies for the legal knowledge graph) workshop, pp. 19–22, 2018.
[14] J.-P. Prost, “Holinet: Holistic knowledge graph for french,” Journées Jointes des Group. Rech. Linguist. Informatique, Formelle Terrain, vol. 1, pp. 123–130, 2022.
[15] S. Y. Yoo and O. R. Jeong, “Automating the expansion of a knowledge graph,” Expert Syst. Appl., vol. 141, p. 112965, 2020.
[16] M. Mirzapour et al., “Introducing RezoJDM16k: a French KnowledgeGraph DataSet for link prediction,” in Proceedings of the Thirteenth Language Resources and Evaluation Conference, pp. 5163–5169, 2022.
[17] H. Alberts et al., “VisualSem: A High-quality Knowledge Graph for Vision & Language,” in MRL 2021 - 1st Workshop on Multilingual Representation Learning, Proceedings of the Conference, pp. 138–152, 2021.
[18] Y. Lin, Z. Liu, M. Sun, Y. Liu, and X. Zhu, “Learning entity and relation embeddings for knowledge graph completion,” in Proceedings of the National Conference on Artificial Intelligence, AI Access Foundation, pp. 2181–2187, 2015.
[19] Z. Wang, J. Zhang, J. Feng, and Z. Chen, “Knowledge graph embedding by translating on hyperplanes,” in Proceedings of the National Conference on Artificial Intelligence, AI Access Foundation, pp. 1112–1119, 2014.
[20] Z. Sun, Z. H. Deng, J. Y. Nie, and J. Tang, “Rotate: Knowledge graph embedding by relational rotation in complex space,” in 7th International Conference on Learning Representations, ICLR 2019, pp. 1-18, 2019.
[21] M. Nickel, V. Tresp, and H. P. Kriegel, “A three-way model for collective learning on multi-relational data,” in Proceedings of the 28th International Conference on Machine Learning, ICML 2011, pp. 809–816, 2011.
[22] M. Nickel, L. Rosasco, and T. Poggio, “Holographic embeddings of knowledge graphs,” in 30th AAAI Conference on Artificial Intelligence, AAAI 2016, pp. 1955–1961, 2016.
[23] T. Trouillon, J. Welbl, S. Riedel, E. Ciaussier, and G. Bouchard, “Complex embeddings for simple link prediction,” in 33rd International Conference on Machine Learning, ICML 2016, pp. 3021–3032, 2016.
[24] B. Shi and T. Weninger, “ProjE: Embedding projection for knowledge graph completion,” in 31st AAAI Conference on Artificial Intelligence, AAAI 2017, pp. 1236–1242, 2017.
[25] D. Q. Nguyen, D. Q. Nguyen, T. D. Nguyen, and D. Phung, “A convolutional neural network-based model for knowledge base completion and its application to search personalization,” Semant. Web, vol. 10, no. 5, pp. 947–960, 2019.
[26] T. Dettmers, P. Minervini, P. Stenetorp, and S. Riedel, “Convolutional 2D knowledge graph embeddings,” in 32nd AAAI Conference on Artificial Intelligence, AAAI 2018, pp. 1811–1818, 2018.
[27] C. Shang, Y. Tang, J. Huang, J. Bi, X. He, and B. Zhou, “End-to-end structure-aware convolutional networks for knowledge base completion,” in 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, pp. 3060–3067, 2019.
[28] B. Yang, W. tau Yih, X. He, J. Gao, and L. Deng, “Embedding entities and relations for learning and inference in knowledge bases,” in 3rd International Conference on Learning Representations, ICLR 2015 Conference Track Proceedings, pp. 1-12, 2015.
[29] X. Han et al., “OpenKE: An open toolkit for knowledge embedding,” in EMNLP 2018 - Conference on Empirical Methods in Natural Language Processing: System Demonstrations, Proceedings, pp. 139–144, 2018.
[30] S. Broscheit, K. Gashteovski, Y. Wang, and R. Gemulla, “Can we predict new facts with open knowledge graph embeddings? A benchmark for open link prediction,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics, pp. 2296–2308, 2020.
[31] C. Fellbaum, “WordNet,” in Theory and Applications of Ontology: Computer Applications, pp. 231–243, 2010.
[32] M. Wang, L. Qiu, and X. Wang, “A survey on knowledge graph embeddings for link prediction,” Symmetry (Basel)., vol. 13, no. 3, 2021.
[33] P. Rosso, D. Yang, and P. Cudré-Mauroux, “Beyond Triplets: Hyper-Relational Knowledge Graph Embedding for Link Prediction,” in The Web Conference 2020 - Proceedings of the World Wide Web Conference, WWW 2020, pp. 1885–1896, 2020.