Fetching the paper…
Reading the bibliography…
The inclusion of temporal scopes of facts in knowledge graph embedding (KGE) presents significant opportunities for improving the resulting embeddings, and consequently for increased performance in downstream applications.
Adamic, L.A., Adar, E.: Friends and neighbors on the web. Soc. Networks 25
2003
Earlier work this paper cites.
Liben-Nowell, D., Kleinberg, J.M.: The link-prediction problem for social networks. J. Assoc. Inf. Sci. Technol. 58
2007
Earlier work this paper cites.
van der Maaten, L., Hinton, G.: Visualizing data using t-sne. Journal of Machine Learning Research 9
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: 27th Annual Conference on Neural Information Processing Systems 2013. pp. 2787–2795 (2013)
2013
Earlier work this paper cites.
Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth Conference on Artificial Intelligence, AAAI 2014. pp. 1112–1119. AAAI Press (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. In: 3rd International Conference on Learning Representations, ICLR 2015 (2015)
2015
Earlier work this paper cites.
Jiang, T., Liu, T., Ge, T., Sha, L., Li, S., Chang, B., Sui, Z.: Encoding temporal information for time-aware link prediction. In: Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing, EMNLP, 2016. pp. 2350–2354. ACL (2016)
2016
Earlier work this paper cites.
Nickel, M., Murphy, K., Tresp, V., Gabrilovich, E.: A review of relational machine learning for knowledge graphs. Proc. IEEE 104
2016
Earlier work this paper cites.
Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of the 33nd International Conference on Machine Learning, ICML 2016. JMLR Workshop and Conference Proceedings, vol. 48, pp. 2071–2080. JMLR.org (2016)
2016
Earlier work this paper cites.
Aminikhanghahi, S., Cook, D.J.: A survey of methods for time series change point detection. Knowl. Inf. Syst. 51
2017
Cited alongside, same era.
Garreau, D.: Change-point detection and kernel methods. Theses, Université Paris sciences et lettres (Oct 2017)
2017
Cited alongside, same era.
Dasgupta, S.S., Ray, S.N., Talukdar, P.P.: Hyte: Hyperplane-based temporally aware knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 2001–2011. ACL (2018)
2018
Cited alongside, same era.
García-Durán, A., Dumancic, S., Niepert, M.: Learning sequence encoders for temporal knowledge graph completion. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. pp. 4816–4821. ACL (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
2020
Later among the works it cites.
Goel, R., Kazemi, S.M., Brubaker, M., Poupart, P.: Diachronic embedding for temporal knowledge graph completion. In: Proeceedings of the Thirty-Fourth Conference on Artificial Intelligence, AAAI. pp. 3988–3995. AAAI Press (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Leblay, J., Chekol, M.W.: Deriving validity time in knowledge graph. In: Companion of the The Web Conference 2018 on The Web Conference 2018, WWW. pp. 1771–1776. ACM (2018)
2018
Cited alongside, same era.
Costabello, L., Pai, S., Van, C.L., McGrath, R., McCarthy, N., Tabacof, P.: AmpliGraph: a Library for Representation Learning on Knowledge Graphs (Mar 2019)
2019
Cited alongside, same era.
Huang, X., Zhang, J., Li, D., Li, P.: Knowledge graph embedding based question answering. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, WSDM. pp. 105–113. ACM (2019)
2019
Cited alongside, same era.
Sun, Z., Deng, Z., Nie, J., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. In: 7th International Conference on Learning Representations, ICLR 2019. OpenReview.net (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Jin, W., Qu, M., Jin, X., Ren, X.: Recurrent event network: Autoregressive structure inferenceover temporal knowledge graphs. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing, EMNLP. pp. 6669–6683. ACL (2020)
2020
Later among the works it cites.
Lacroix, T., Obozinski, G., Usunier, N.: Tensor decompositions for temporal knowledge base completion. In: 8th International Conference on Learning Representations, ICLR 2020. OpenReview.net (2020)
2020
Later among the works it cites.
Tang, X., Yuan, R., Li, Q., Wang, T., Yang, H., Cai, Y., Song, H.: Timespan-aware dynamic knowledge graph embedding by incorporating temporal evolution. IEEE Access 8
2020
Later among the works it cites.
Truong, C., Oudre, L., Vayatis, N.: Selective review of offline change point detection methods. Signal Process. 167
2020
Later among the works it cites.