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Knowledge Representation Learning (KRL) is crucial for enabling applications of symbolic knowledge from Knowledge Graphs (KGs) to downstream tasks by projecting knowledge facts into vector spaces.
1907
Earlier work this paper cites.
1909
Earlier work this paper cites.
1910
Earlier work this paper cites.
Kok, S., Domingos, P.: Statistical predicate invention. In: Proceedings of the 24th International Conference on Machine Learning, pp. 433–440 (2007)
2007
Earlier work this paper cites.
Nickel, M., Tresp, V., Kriegel, H.-P., et al
2011
Earlier work this paper cites.
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Socher, R., Chen, D., Manning, C.D., Ng, A.: Reasoning with neural tensor networks for knowledge base completion. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 2335–2344 (2014)
2014
Earlier work this paper cites.
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (volume 1: Long Papers), pp. 687–696 (2015)
2015
Earlier work this paper cites.
Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29 (2015)
2015
Earlier work this paper cites.
Ling, X., Singh, S., Weld, D.S.: Design challenges for entity linking. Transactions of the Association for Computational Linguistics 3
2015
Earlier work this paper cites.
Toutanova, K., Chen, D., Pantel, P., Poon, H., Choudhury, P., Gamon, M.: Representing text for joint embedding of text and knowledge bases. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1499–1509 (2015)
2015
Earlier work this paper cites.
Zeng, D., Liu, K., Chen, Y., Zhao, J.: Distant supervision for relation extraction via piecewise convolutional neural networks. In: Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pp. 1753–1762 (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)
2016
Earlier work this paper cites.
Xie, R., Liu, Z., Sun, M., et al
2016
Earlier work this paper cites.
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080 (2016). PMLR
2016
Earlier work this paper cites.
Vaswani, A.: Attention is all you need. Advances in Neural Information Processing Systems (2017)
2017
Earlier work this paper cites.
Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Conference on Empirical Methods in Natural Language Processing (2017)
2017
Earlier work this paper cites.
Zhang, Y., Zhong, V., Chen, D., Angeli, G., Manning, C.D.: Position-aware attention and supervised data improve slot filling. In: Conference on Empirical Methods in Natural Language Processing (2017)
2017
Earlier work this paper cites.
Xiao, H., Huang, M., Meng, L., Zhu, X.: Ssp: semantic space projection for knowledge graph embedding with text descriptions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
2017
Earlier work this paper cites.
Shi, B., Weninger, T.: Proje: Embedding projection for knowledge graph completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Schlichtkrull, M., Kipf, T.N., Bloem, P., Van Den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15, pp. 593–607 (2018). Springer
2018
Earlier work this paper cites.
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2d knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3060–3067 (2019)
2019
Cited alongside, same era.
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: Burstein, J., Doran, C., Solorio, T. (eds.) Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pp. 4171–4186. Association for Computational Linguistics, Minneapolis, Minnesota (2019). https://doi.org/10.18653/v1/N19-1423 . https://aclanthology.org/N19-1423
2019
Cited alongside, same era.
2019
Cited alongside, same era.
2021
Later among the works it cites.
Bai, Y., Ying, Z., Ren, H., Leskovec, J.: Modeling heterogeneous hierarchies with relation-specific hyperbolic cones. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Song, T., Luo, J., Huang, L.: Rot-pro: Modeling transitivity by projection in knowledge graph embedding. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
2021
Later among the works it cites.
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2019
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Nguyen, D.Q., Nguyen, D.Q., Nguyen, T.D., Phung, D.: A convolutional neural network-based model for knowledge base completion and its application to search personalization. Semantic Web 10
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Zhang, S., Tay, Y., Yao, L., Liu, Q.: Quaternion knowledge graph embeddings. Advances in neural information processing systems 32
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Wang, R., Li, B., Hu, S., Du, W., Zhang, M.: Knowledge graph embedding via graph attenuated attention networks. IEEE access 8
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Zhu, Z., Zhang, Z., Xhonneux, L.-P., Tang, J.: Neural bellman-ford networks: A general graph neural network framework for link prediction. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Lv, X., Lin, Y., Cao, Y., Hou, L., Li, J., Liu, Z., Li, P., Zhou, J.: Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach. (2022). Association for Computational Linguistics
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Xie, X., Zhang, N., Li, Z., Deng, S., Chen, H., Xiong, F., Chen, M., Chen, H.: From discrimination to generation: Knowledge graph completion with generative transformer. In: Companion Proceedings of the Web Conference 2022, pp. 162–165 (2022)
2022
Later among the works it cites.
2022
Later among the works it cites.
2022
Later among the works it cites.
Alam, M.M., Rony, M.R.A.H., Nayyeri, M., Mohiuddin, K., Akter, M.M., Vahdati, S., Lehmann, J.: Language model guided knowledge graph embeddings. IEEE Access 10
2022
Later among the works it cites.
Lv, X., Lin, Y., Cao, Y., Hou, L., Li, J., Liu, Z., Li, P., Zhou, J.: Do pre-trained models benefit knowledge graph completion? a reliable evaluation and a reasonable approach. (2022). Association for Computational Linguistics
2022
Later among the works it cites.
Zhu, C., Yang, Z., Xia, X., Li, N., Zhong, F., Liu, L.: Multimodal reasoning based on knowledge graph embedding for specific diseases. Bioinformatics 38
2022
Later among the works it cites.
Yu, P., Ji, H.: Shorten the long tail for rare entity and event extraction. In: Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics, pp. 1339–1350 (2023)
2023
Later among the works it cites.
Biswas, R., Kaffee, L.-A., Cochez, M., Dumbrava, S., Jendal, T.E., Lissandrini, M., Lopez, V., Mencía, E.L., Paulheim, H., Sack, H., et al
2023
Later among the works it cites.
Li, D., Zhu, B., Yang, S., Xu, K., Yi, M., He, Y., Wang, H.: Multi-task pre-training language model for semantic network completion. ACM Transactions on Asian and Low-Resource Language Information Processing 22
2023
Later among the works it cites.
Choi, B., Ko, Y.: Knowledge graph extension with a pre-trained language model via unified learning method. Knowledge-Based Systems 262
2023
Later among the works it cites.
Wang, P., Xie, X., Wang, X., Zhang, N.: Reasoning through memorization: Nearest neighbor knowledge graph embeddings. In: CCF International Conference on Natural Language Processing and Chinese Computing, pp. 111–122 (2023). Springer
2023
Later among the works it cites.
2023
Later among the works it cites.
Yu, D., Yang, Y.: Retrieval-enhanced generative model for large-scale knowledge graph completion. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 2334–2338 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
Wu, H., He, Y., Chen, Y., Bai, Y., Shi, X.: Improving few-shot relation extraction through semantics-guided learning. Neural Networks 169
2023
Later among the works it cites.
Ge, X., Wang, Y.C., Wang, B., Kuo, C.-C.J., et al.: Knowledge graph embedding: An overview. APSIPA Transactions on Signal and Information Processing 13
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Cao, J., Fang, J., Meng, Z., Liang, S.: Knowledge graph embedding: A survey from the perspective of representation spaces. ACM Computing Surveys 56
2024
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Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., Wu, X.: Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering (2024)
2024
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2024
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2024
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