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Knowledge graphs have garnered significant research attention and are widely used to enhance downstream applications.
Language models are few-shot learners
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HyTE: Hyperplane-based temporally aware knowledge graph embedding, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 2001–2011
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Semi-supervised classification with graph convolutional networks, in: 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings, OpenReview.net, Toulon, France
Kipf, T.N., Welling, M., 2017 · 2017
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The neural hawkes process: A neurally self-modulating multivariate point process, in: Guyon, I., von Luxburg, U., Bengio, S., Wallach, H.M., Fergus, R., Vishwanathan, S.V.N., Garnett, R. (Eds.), Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA, Curran Associates, Inc., New York, NY, USA. pp. 6754–6764
Mei, H., Eisner, J., 2017 · 2017
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Know-evolve: Deep temporal reasoning for dynamic knowledge graphs, in: Precup, D., Teh, Y.W. (Eds.), Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017, PMLR, Cambridge, MA, USA. pp. 3462–3471
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Attention is all you need, in: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I., 2017 · 2017
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Utilizing knowledge graphs for text-centric information retrieval, in: The 41st International ACM SIGIR Conference on Research and Development in Information Retrieval, Association for Computing Machinery, New York, NY, USA. p. 1387–1390
Dietz, L., Kotov, A., Meij, E., 2018 · 2018
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Learning sequence encoders for temporal knowledge graph completion, in: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 4816–4821
García-Durán, A., Dumancic, S., Niepert, M., 2018 · 2018
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Simple embedding for link prediction in knowledge graphs, in: Bengio, S., Wallach, H., Larochelle, H., Grauman, K., Cesa-Bianchi, N., Garnett, R. (Eds.), Advances in Neural Information Processing Systems, Curran Associates, Inc., New York, NY, USA
Kazemi, S.M., Poole, D., 2018 · 2018
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Deriving validity time in knowledge graph, in: Companion Proceedings of the The Web Conference 2018, International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, CHE. p. 1771–1776
Leblay, J., Chekol, M.W., 2018 · 2018
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Modeling relational data with graph convolutional networks, in: Gangemi, A., Navigli, R., Vidal, M., Hitzler, P., Troncy, R., Hollink, L., Tordai, A., Alam, M. (Eds.), The Semantic Web - 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3-7, 2018, Proceedings, Springer, Berlin, GER. pp. 593–607
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Graph attention networks, in: 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings, OpenReview.net, Vancouver, BC, Canada
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TuckER: Tensor factorization for knowledge graph completion, in: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 5185–5194
Balazevic, I., Allen, C., Hospedales, T., 2019 · 2019
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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, NAACL-HLT 2019, Minneapolis, MN, USA, June 2-7, 2019, Volume 1 (Long and Short Papers), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 4171–4186
Devlin, J., Chang, M., Lee, K., Toutanova, K., 2019 · 2019
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A capsule network-based embedding model for knowledge graph completion and search personalization, in: 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), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 2180–2189
Nguyen, D.Q., Vu, T., Nguyen, T.D., Nguyen, D.Q., Phung, D., 2019 · 2019
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End-to-end structure-aware convolutional networks for knowledge base completion, in: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI 2019, The Thirty-First Innovative Applications of Artificial Intelligence Conference, IAAI 2019, The Ninth AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, Honolulu, Hawaii, USA, January 27 - February 1, 2019, AAAI Press, Palo Alto, CA, USA. pp. 3060–3067
Learning from history: Modeling temporal knowledge graphs with sequential copy-generation networks, in: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, AAAI Press, Palo Alto, CA, USA. pp. 4732–4740
Zhu, C., Chen, M., Fan, C., Cheng, G., Zhang, Y., 2021 · 2021
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A simple temporal information matching mechanism for entity alignment between temporal knowledge graphs, in: Proceedings of the 29th International Conference on Computational Linguistics, International Committee on Computational Linguistics, Gyeongju, Republic of Korea. pp. 2075–2086
Cai, L., Mao, X., Ma, M., Yuan, H., Zhu, J., Lan, M., 2022b · 2022
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RotateQVS: Representing temporal information as rotations in quaternion vector space for temporal knowledge graph completion, in: Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 5843–5857
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Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B., 2019 · 2019
Cited alongside, same era.
Rotate: Knowledge graph embedding by relational rotation in complex space, in: 7th International Conference on Learning Representations, ICLR 2019, OpenReview.net, New Orleans, LA, USA
Sun, Z., Deng, Z., Nie, J., Tang, J., 2019 · 2019
Cited alongside, same era.
Boxe: A box embedding model for knowledge base completion, in: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (Eds.), Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual
Abboud, R., Ceylan, İ.İ., Lukasiewicz, T., Salvatori, T., 2020 · 2020
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Dynamic knowledge graph based multi-event forecasting, in: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery; Data Mining, Association for Computing Machinery, New York, NY, USA. p. 1585–1595
Deng, S., Rangwala, H., Ning, Y., 2020 · 2020
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Diachronic embedding for temporal knowledge graph completion, in: The Thirty-Fourth AAAI Conference on Artificial Intelligence, AAAI 2020, New York, NY, USA, February 7-12, 2020, AAAI Press, Palo Alto, CA, USA. pp. 3988–3995
Goel, R., Kazemi, S.M., Brubaker, M.A., Poupart, P., 2020 · 2020
Cited alongside, same era.
Graph hawkes neural network for forecasting on temporal knowledge graphs, in: Das, D., Hajishirzi, H., McCallum, A., Singh, S. (Eds.), Conference on Automated Knowledge Base Construction, AKBC 2020, Virtual, June 22-24, 2020, OpenReview.net, Virtual
Han, Z., Ma, Y., Wang, Y., Günnemann, S., Tresp, V., 2020b · 2020
Cited alongside, same era.
Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs, in: Webber, B., Cohn, T., He, Y., Liu, Y. (Eds.), Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020, Online, November 16-20, 2020, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 6669–6683
Jin, W., Qu, M., Jin, X., Ren, X., 2020 · 2020
Cited alongside, same era.
Tensor decompositions for temporal knowledge base completion, in: 8th International Conference on Learning Representations, ICLR 2020, OpenReview.net, Addis Ababa, Ethiopia
Lacroix, T., Obozinski, G., Usunier, N., 2020 · 2020
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Tensor decomposition-based temporal knowledge graph embedding, in: 32nd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2020, Baltimore, MD, USA, November 9-11, 2020, IEEE, Baltimore, MD, USA. pp. 969–975
Lin, L., She, K., 2020 · 2020
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Composition-based multi-relational graph convolutional networks, in: 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26-30, 2020, OpenReview.net, Addis Ababa, Ethiopia
Vashishth, S., Sanyal, S., Nitin, V., Talukdar, P.P., 2020 · 2020
Cited alongside, same era.
Chen, K., Wang, Y., Li, Y., Li, A., 2022 · 2022
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TempCaps: A capsule network-based embedding model for temporal knowledge graph completion, in: Proceedings of the Sixth Workshop on Structured Prediction for NLP, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 22–31
Fu, G., Meng, Z., Han, Z., Ding, Z., Ma, Y., Schubert, M., Tresp, V., Wattenhofer, R., 2022 · 2022
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Integrative few-shot learning for classification and segmentation, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, IEEE. pp. 9969–9980
Kang, D., Cho, M., 2022 · 2022
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Tirgn: Time-guided recurrent graph network with local-global historical patterns for temporal knowledge graph reasoning, in: Raedt, L.D. (Ed.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, IJCAI 2022, Vienna, Austria, 23-29 July 2022, ijcai.org, San Francisco, CA, USA. pp. 2152–2158
Li, Y., Sun, S., Zhao, J., 2022a · 2022
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Complex evolutional pattern learning for temporal knowledge graph reasoning, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 290–296
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Liu, X., Zhang, Y., Shan, D., 2023 · 2022
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Temporal knowledge graph completion using box embeddings, in: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, AAAI Press. pp. 7779–7787
Messner, J., Abboud, R., Ceylan, İ.İ., 2022 · 2022
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Improving time sensitivity for question answering over temporal knowledge graphs, in: Muresan, S., Nakov, P., Villavicencio, A. (Eds.), Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), ACL 2022, Dublin, Ireland, May 22-27, 2022, Association for Computational Linguistics, Stroudsburg, PA, USA. pp. 8017–8026
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MetaTKG: Learning evolutionary meta-knowledge for temporal knowledge graph reasoning, in: Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Association for Computational Linguistics, Abu Dhabi, United Arab Emirates. pp. 7230–7240
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Xu, C., Nayyeri, M., Chen, Y.Y., Lehmann, J., 2023 · 2022
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Xu, C., Su, F., Xiong, B., Lehmann, J., 2022 · 2022
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Along the time: Timeline-traced embedding for temporal knowledge graph completion, in: Proceedings of the 31st ACM International Conference on Information & Knowledge Management, Association for Computing Machinery, New York, NY, USA. p. 2529–2538
Zhang, F., Zhang, Z., Ao, X., Zhuang, F., Xu, Y., He, Q., 2022 · 2022
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Zero-shot and few-shot learning with knowledge graphs: A comprehensive survey
Chen, J., Geng, Y., Chen, Z., Pan, J.Z., He, Y., Zhang, W., Horrocks, I., Chen, H., 2023a · 2023
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Meta-learning based knowledge extrapolation for temporal knowledge graph, in: Proceedings of the ACM Web Conference 2023, Association for Computing Machinery, New York, NY, USA. p. 2433–2443
Chen, Z., Xu, C., Su, F., Huang, Z., Dou, Y., 2023b · 2023
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Ding, Z., Cai, H., Wu, J., Ma, Y., Liao, R., Xiong, B., Tresp, V., 2023 · 2023
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Temporal-relational matching network for few-shot temporal knowledge graph completion, in: Database Systems for Advanced Applications: 28th International Conference, DASFAA 2023, Tianjin, China, April 17–20, 2023, Proceedings, Part II, Springer-Verlag, Berlin, Heidelberg. p. 768–783
Gong, X., Qin, J., Chai, H., Ding, Y., Jia, Y., Liao, Q., 2023 · 2023
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Ecola: Enhanced temporal knowledge embeddings with contextualized language representations
Han, Z., Liao, R., Gu, J., Zhang, Y., Ding, Z., Gu, Y., Köppl, H., Schütze, H., Tresp, V., 2023 · 2023
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Temporal knowledge graph forecasting without knowledge using in-context learning
Lee, D.H., Ahrabian, K., Jin, W., Morstatter, F., Pujara, J., 2023 · 2023
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Gentkg: Generative forecasting on temporal knowledge graph
Liao, R., Jia, X., Ma, Y., Tresp, V., 2023 · 2023
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OpenAI, 2023 · 2023
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Biqcap: A biquaternion and capsule network-based embedding model for temporal knowledge graph completion, in: Wang, X., Sapino, M.L., Han, W., Abbadi, A.E., Dobbie, G., Feng, Z., Shao, Y., Yin, H. (Eds.), Database Systems for Advanced Applications - 28th International Conference, DASFAA 2023, Tianjin, China, April 17-20, 2023, Proceedings, Part II, Springer. pp. 673–688
Zhang, S., Liang, X., Li, Z., Feng, J., Zheng, X., Wu, B., 2023b · 2023
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A survey of large language models
Zhao, W.X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., Min, Y., Zhang, B., Zhang, J., Dong, Z., Du, Y., Yang, C., Chen, Y., Chen, Z., Jiang, J., Ren, R., Li, Y., Tang, X., Liu, Z., Liu, P., Nie, J.Y., Wen, J.R., 2023 · 2023
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Chain of history: Learning and forecasting with llms for temporal knowledge graph completion
Luo, R., Gu, T., Li, H., Li, J., Lin, Z., Li, J., Yang, Y., 2024 · 2024
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Complex embeddings for simple link prediction, in: Proceedings of The 33rd International conference on machine learning, PMLR, New York, NY, USA. pp. 2071–2080
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G., 2016 · 2080
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