Citation: | Binhao HU, Jianpeng ZHANG, and Hongchang CHEN, “Knowledge Graph Completion Method of Combining Structural Information with Semantic Information,” Chinese Journal of Electronics, vol. 33, no. 6, pp. 1412–1420, 2024 doi: 10.23919/cje.2022.00.299 |
[1] |
S. X. Ji, S. R. Pan, E. Cambria, et al., “A survey on knowledge graphs: Representation, acquisition, and applications,” IEEE Transactions on Neural Networks and Learning Systems, vol. 33, no. 2, pp. 494–514, 2021. doi: 10.1109/TNNLS.2021.3070843
|
[2] |
S. Auer, C. Bizer, G. Kobilarov, et al., “DBpedia: A nucleus for A web of open data,” in Proceedings of the 6th International Semantic Web Conference on the Semantic Web, Busan, Korea, pp. 722–735, 2007.
|
[3] |
T. P. Tanon, G. Weikum, and F. Suchanek, “YAGO 4: A reason-able knowledge base,” in Proceedings of the 17th International Conference on the Semantic Web, Heraklion, Crete, pp. 583–596, 2020.
|
[4] |
A. Bordes, N. Usunier, A. Garcia-Durán, et al., “Translating embeddings for modeling multi-relational data,” in Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, pp. 2787–2795, 2013.
|
[5] |
J. W. Zhang, H. P. Zhang, C. Y. Xia, et al., “Graph-Bert: Only attention is needed for learning graph representations,” arXiv preprint, arXiv: 2001.05140, 2020.
|
[6] |
L. Yao, C. S. Mao, and Y. Luo, “KG-BERT: BERT for knowledge graph completion,” arXiv preprint, arXiv: 1909.03193, 2019.
|
[7] |
Z. Q. Sun, Z. H. Deng, J. Y. Nie, et al., “RotatE: Knowledge graph embedding by relational rotation in complex space,” in Proceedings of the 7th International Conference on Learning Representations, New Orleans, LA, USA, vol. 214, 2019.
|
[8] |
I. Balažević, C. Allen, and T. Hospedales, “Multi-relational Poincaré graph embeddings,” in Proceedings of the 33rd International Conference on Neural Information Processing Systems, Vancouver, BC, Canada, article no. 401, 2019.
|
[9] |
B. S. Yang, W. T. Yi, X. D. He, et al., “Embedding entities and relations for learning and inference in knowledge bases,” in Proceedings of the 3rd International Conference on Learning Representations, San Diego, CA, USA, 2014.
|
[10] |
T. Trouillon, J. Welbl, S. Riedel, et al., “Complex embeddings for simple link prediction,” in Proceedings of the 33rd International Conference on International Conference on Machine Learning, New York, NY, USA, pp. 2071–2080, 2016.
|
[11] |
M. M. Bronstein, J. Bruna, Y. Lecun, et al., “Geometric deep learning: Going beyond Euclidean data,” IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 18–42, 2017. doi: 10.1109/MSP.2017.2693418
|
[12] |
B. Wang, T. Shen, G. D. Long, et al., “Structure-augmented text representation learning for efficient knowledge graph completion,” in Proceedings of Web Conference 2021, Ljubljana, Slovenia, pp. 1737–1748, 2021.
|
[13] |
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 International Conference on Machine Learning, Bellevue, WA, USA, pp. 809–816, 2011.
|
[14] |
S. Z. He, K. Liu, G. L. Ji, et al., “Learning to represent knowledge graphs with Gaussian embedding,” in Proceedings of the 24th ACM International on Conference on Information and Knowledge Management, Melbourne, Australia, pp. 623–632, 2015.
|
[15] |
R. Socher, D. Q. Chen, C. D. Manning, et al., “Reasoning with neural tensor networks for knowledge base completion,” in Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, pp. 926–934, 2013.
|
[16] |
Z. Wang, J. W. Zhang, J. L. Feng, et al., “Knowledge graph and text jointly embedding,” in Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Doha, Qatar, pp. 1591–1601, 2014.
|
[17] |
H. Xiao, M. L. Huang, L. Meng, et al., “SSP: Semantic space projection for knowledge graph embedding with text descriptions,” in Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA, pp. 3104–3110, 2017.
|
[18] |
Z. G. Wang, J. Z. Li, Z. Y. Liu, et al., “SSP: Semantic space projection for knowledge graph embedding with text descriptions,” in Proceedings of International Joint Conference on Artificial Intelligent (IJCAI), pp. 4–17, 2016.
|
[19] |
J. Devlin, M. W. Chang, K. Lee, et al., “BERT: Pre-training of deep bidirectional transformers for language understanding,” in Proceedings of 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1, Minneapolis, MN, USA, pp. 4171–4186, 2018.
|
[20] |
A. Vaswani, N. Shazeer, N. Parmar, et al., “Attention is all you need,” in Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, CA, USA, pp. 6000–6010, 2017.
|
[21] |
I. Chami, A. Wolf, D. C. Juan, et al., “Low-dimensional hyperbolic knowledge graph embeddings,” in Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, Virtual Event, pp. 6901–6914, 2020.
|
[22] |
A. B. Adcock, B. D. Sullivan, and M. W. Mahoney, “Tree-like structure in large social and information networks,” in Proceedings of the IEEE 13th International Conference on Data Mining, Dallas, TX, USA, pp. 1–10, 2013.
|
[23] |
O. E. Ganea, G. Bécigneul, and T. Hofmann, “Hyperbolic neural networks,” in Proceedings of the 32nd International Conference on Neural Information Processing Systems, Montréal, Canada, pp. 5350–5360, 2018.
|
[24] |
K. Toutanova, D. Q Chen, P. Pantel, et al., “Representing text for joint embedding of text and knowledge bases,” in Proceedings of 2015 Conference on Empirical Methods in Natural Language Processing, Lisbon, Portugal, pp. 1499–1509, 2015.
|
[25] |
T. Dettmers, P. Minervini, P. Stenetorp, et al., “Convolutional 2D knowledge graph embeddings,” in Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, New Orleans, LA, USA, pp. 1811–1818, 2018.
|
[26] |
C. Fellbaum and G. Miller, WordNet: An Electronic Lexical Database. MIT Press, Cambridge, MA, USA, 1998.
|
[27] |
D. Nathani, J. Chauhan, C. Sharma, et al., “Learning attention-based embeddings for relation prediction in knowledge graphs,” in Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, pp. 4710–4723, 2019.
|