Fetching the paper…
Reading the bibliography…
Temporal knowledge bases associate relational (s,r,o) triples with a set of times (or a single time instant) when the relation is valid.
Recurrent event network for reasoning over temporal knowledge graphs
Woojeong Jin, Changlin Zhang, Pedro A. Szekely, and Xiang Ren. 2019 · 1904
Earlier work this paper cites.
Temporal knowledge graph completion based on time series gaussian embedding
Mojtaba Nayyeri, Fouad Alkhoury, Hamed Yazdi, and Jens Lehmann. 2020 · 1911
Earlier work this paper cites.
Holographic embeddings of knowledge graphs
Maximilian Nickel, Lorenzo Rosasco, Tomaso A Poggio, et al. 2016 · 1961
Earlier work this paper cites.
Software engineering event modeling using relative time in temporal knowledge graphs
Kian Ahrabian, Daniel Tarlow, Hehuimin Cheng, and Jin L. C. Guo. 2020 · 2007
Earlier work this paper cites.
Probabilistic Graphical Models: Principles and Techniques
Daphne Koller and Nir Friedman. 2009 · 2009
Earlier work this paper cites.
HyTE: Hyperplane-based temporally aware knowledge graph embedding
Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha P. Talukdar. 2018 · 2011
Earlier work this paper cites.
Overview of the TAC2011 knowledge base population (KBP) track
Heng Ji, Ralph Grishman, Hoa Trang Dang, and Kira Griffitt. 2011 · 2011
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
Earlier work this paper cites.
YAGO2: A spatially and temporally enhanced knowledge base from wikipedia: Extended abstract
Johannes Hoffart, Fabian M. Suchanek, Klaus Berberich, and Gerhard Weikum. 2013 · 2013
Earlier work this paper cites.
Overview of the TAC2013 knowledge base population evaluation: English slot filling and temporal slot filling
Mihai Surdeanu. 2013 · 2013
Cited alongside, same era.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
Cited alongside, same era.
Towards time-aware knowledge graph completion
Tingsong Jiang, Tianyu Liu, Tao Ge, Lei Sha, Baobao Chang, Sujian Li, and Zhifang Sui. 2016 · 2016
Cited alongside, same era.
Know-evolve: Deep temporal reasoning for dynamic knowledge graphs
Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017 · 2017
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
Cited alongside, same era.
TEQUILA: temporal question answering over knowledge bases
Zhen Jia, Abdalghani Abujabal, Rishiraj Saha Roy, Jannik Strötgen, and Gerhard Weikum. 2018 · 2018
Later among the works it cites.
Simple embedding for link prediction in knowledge graphs
Seyed Mehran Kazemi and David Poole. 2018 · 2018
Later among the works it cites.
Canonical tensor decomposition for knowledge base completion
Timothée Lacroix, Nicolas Usunier, and Guillaume Obozinski. 2018 · 2018
Later among the works it cites.
Generalized intersection over union: A metric and a loss for bounding box regression
Hamid Rezatofighi, Nathan Tsoi, JunYoung Gwak, Amir Sadeghian, Ian D. Reid, and Silvio Savarese. 2019 · 2019
Later among the works it cites.
Diachronic embedding for temporal knowledge graph completion
Rishab Goel, Seyed Mehran Kazemi, Marcus Brubaker, and Pascal Poupart. 2020 · 2020
Closest in time.
Tensor decompositions for temporal knowledge base completion
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Alberto García-Durán, Sebastijan Dumancic, and Mathias Niepert. 2018 · 2018
Cited alongside, same era.
Kblrn: End-to-end learning of knowledge base representations with latent, relational, and numerical features
Alberto García-Durán and Mathias Niepert. 2018 · 2018
Cited alongside, same era.
Type-sensitive knowledge base inference without explicit type supervision
Prachi Jain, Pankaj Kumar, Mausam, and Soumen Chakrabarti. 2018a · 2018
Cited alongside, same era.
Prachi Jain, Shikhar Murty, Mausam, and Soumen Chakrabarti. 2018b
Cited in the paper.
Timothée Lacroix, Guillaume Obozinski, and Nicolas Usunier. 2020 · 2020
Closest in time.
You CAN teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla. 2020 · 2020
Closest in time.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard. 2016 · 2080
Closest in time.