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Traditional knowledge graph (KG) completion models learn embeddings to predict missing facts.
Miller, G.A.: WordNet: A lexical database for English. Commun. ACM 38
1995
Earlier work this paper cites.
Bollacker, K.D., Evans, C., Paritosh, P.K., Sturge, T., Taylor, J.: Freebase: A collaboratively created graph database for structuring human knowledge. In: SIGMOD. pp. 1247–1250 (2008)
2008
Earlier work this paper cites.
Bordes, A., Usunier, N., García-Durán, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: NIPS. pp. 2787–2795 (2013)
2013
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: EMNLP. pp. 1499–1509 (2015)
2015
Earlier work this paper cites.
Galárraga, L., Razniewski, S., Amarilli, A., Suchanek, F.M.: Predicting completeness in knowledge bases. In: WSDM. pp. 375–383 (2017)
2017
Earlier work this paper cites.
Yang, F., Yang, Z., Cohen, W.W.: Differentiable learning of logical rules for knowledge base reasoning. In: NeurPS (2017)
2017
Earlier work this paper cites.
Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: AAAI. pp. 1811–1818 (2018)
2018
Earlier work this paper cites.
Xie, R., Liu, Z., Lin, F., Lin, L.: Does William Shakespeare really write Hamlet? Knowledge representation learning with confidence. In: AAAI. pp. 4954–4961 (2018)
2018
Earlier work this paper cites.
Balazevic, I., Allen, C., Hospedales, T.M.: TuckER: Tensor factorization for knowledge graph completion. In: EMNLP-IJCNLP. pp. 5185–5194 (2019)
2019
Earlier work this paper cites.
Devlin, J., Chang, M., Lee, K., Toutanova, K.: BERT: Pre-training of deep bidirectional transformers for language understanding. In: NAACL. pp. 4171–4186 (2019)
2019
Earlier work this paper cites.
Sun, Z., Deng, Z., Nie, J., Tang, J.: RotatE: Knowledge graph embedding by relational rotation in complex space. In: ICLR (2019)
2019
Earlier work this paper cites.
Yao, L., Mao, C., Luo, Y.: KG-BERT: BERT for knowledge graph completion. arXiv 1909.03193
2019
Earlier work this paper cites.
Lewis, M., Liu, Y., Goyal, N., Ghazvininejad, M., Mohamed, A., Levy, O., Stoyanov, V., Zettlemoyer, L.: BART: Denoising sequence-to-sequence pre-training for natural language generation, translation, and comprehension. In: ACL. pp. 7871–7880 (2020)
2020
Earlier work this paper cites.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P.J.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21
2020
Cited alongside, same era.
Saxena, A., Tripathi, A., Talukdar, P.P.: Improving multi-hop question answering over knowledge graphs using knowledge base embeddings. In: ACL. pp. 4498–4507 (2020)
2020
Cited alongside, same era.
Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.P.: Composition-based multi-relational graph convolutional networks. In: ICLR (2020)
2020
Cited alongside, same era.
Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., Ji, Y.: HittER: Hierarchical transformers for knowledge graph embeddings. In: EMNLP. pp. 10395–10407 (2021)
2021
Cited alongside, same era.
Choi, B., Jang, D., Ko, Y.: MEM-KGC: Masked entity model for knowledge graph completion with pre-trained language model. IEEE Access 9
Liu, Y., Sun, Z., Li, G., Hu, W.: I know what you do not know: Knowledge graph embedding via co-distillation learning. In: CIKM. pp. 1329–1338 (2022)
2022
Later among the works it cites.
Nayyeri, M., Vahdati, S., Khan, M.T., Alam, M.M., Wenige, L., Behrend, A., Lehmann, J.: Dihedron algebraic embeddings for spatio-temporal knowledge graph completion. In: ESWC (2022)
2022
Later among the works it cites.
Saxena, A., Kochsiek, A., Gemulla, R.: Sequence-to-sequence knowledge graph completion and question answering. In: ACL. pp. 2814–2828 (2022)
2022
Later among the works it cites.
Wang, L., Zhao, W., Wei, Z., Liu, J.: SimKGC: Simple contrastive knowledge graph completion with pre-trained language models. In: ACL. pp. 4281–4294 (2022)
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: WWW. pp. 162–165 (2022)
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2021
Cited alongside, same era.
Qu, M., Chen, J., Xhonneux, L.A.C., Bengio, Y., Tang, J.: RNNLogic: Learning logic rules for reasoning on knowledge graphs. In: ICLR (2021)
2021
Cited alongside, same era.
Wang, B., Shen, T., Long, G., Zhou, T., Wang, Y., Chang, Y.: Structure-augmented text representation learning for efficient knowledge graph completion. In: WWW. pp. 1737–1748 (2021)
2021
Cited alongside, same era.
Zhu, Z., Zhang, Z., Xhonneux, L.A.C., Tang, J.: Neural Bellman-Ford networks: A general graph neural network framework for link prediction. In: NeurIPS. pp. 29476–29490 (2021)
2021
Cited alongside, same era.
Chen, C., Wang, Y., Li, B., Lam, K.: Knowledge is flat: A seq2seq generative framework for various knowledge graph completion. In: COLING. pp. 4005–4017 (2022)
2022
Cited alongside, same era.
Du, H., Le, Z., Wang, H., Chen, Y., Yu, J.: COKG-QA: Multi-hop question answering over COVID-19 knowledge graphs. Data Intell. 4
2022
Cited alongside, same era.
Guo, Q., Zhuang, F., Qin, C., Zhu, H., Xie, X., Xiong, H., He, Q.: A survey on knowledge graph-based recommender systems. IEEE Trans. Knowl. Data Eng. 34
2022
Cited alongside, same era.
Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., Chen, W.: LoRA: Low-rank adaptation of large language models. In: ICLR (2022)
2022
Cited alongside, same era.
2022
Later among the works it cites.
Cheng, K., Ahmed, N.K., Sun, Y.: Neural compositional rule learning for knowledge graph reasoning. In: ICLR (2023)
2023
Later among the works it cites.
Dettmers, T., Pagnoni, A., Holtzman, A., Zettlemoyer, L.: QLoRA: Efficient finetuning of quantized LLMs. arXiv 2305.14314
2023
Later among the works it cites.
Omeliyanenko, J., Zehe, A., Hotho, A., Schlör, D.: CapsKG: Enabling continual knowledge integration in language models for automatic knowledge graph completion. In: ISWC (2023)
2023
Later among the works it cites.
Touvron, H., Lavril, T., Izacard, G., Martinet, X., Lachaux, M., Lacroix, T., Rozière, B., Goyal, N., Hambro, E., Azhar, F., Rodriguez, A., Joulin, A., Grave, E., Lample, G.: LLaMA: Open and efficient foundation language models. arXiv 2302.13971
2023
Later among the works it cites.
Wei, Y., Huang, Q., Zhang, Y., Kwok, J.T.: KICGPT: Large language model with knowledge in context for knowledge graph completion. In: EMNLP-Findings. pp. 8667–8683 (2023)
2023
Later among the works it cites.
Yang, Y., Ye, Z., Zhao, H., Meng, L.: A novel link prediction framework based on gravitational field. Data Sci. Eng. 8
2023
Later among the works it cites.
Yao, L., Peng, J., Mao, C., Luo, Y.: Exploring large language models for knowledge graph completion. arXiv 2308.13916
2023
Later among the works it cites.
Zhu, Y., Wang, X., Chen, J., Qiao, S., Ou, Y., Yao, Y., Deng, S., Chen, H., Zhang, N.: LLMs for knowledge graph construction and reasoning: Recent capabilities and future opportunities. arXiv 2305.13168
2023
Later among the works it cites.