CoChat
Sign Up
Cited by 34

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Learning Dynamic Embeddings for Temporal Knowledge Graphs

Liao, Siyuan, Liang, Shangsong, Meng, Zaiqiao, Zhang, Qiang·2021·Proceedings of the 14th ACM International Conference on Web Search and Data Mining
DOI 10.1145/3437963.3441741
Open this paper in CoChat — free account

Abstract

Representation learning for temporal knowledge graphs has attracted increasing attention in recent years. In this paper, we study the problem of learning dynamic embeddings for temporal knowledge graphs. We address this problem by proposing a Dynamic Bayesian Knowledge Graphs Embedding model (DBKGE), which is able to dynamically track the semantic representations of entities over time in a joint metric space and make predictions for the future. Unlike other temporal knowledge graph embedding methods, DBKGE is a novel probabilistic representation learning method that aims at inferring dynamic embeddings of entities in a streaming scenario. To obtain high-quality embeddings and model their uncertainty, our DBKGE embeds entities with means and variances of Gaussian distributions. Based on amortized inference, an online inference algorithm is proposed to jointly learn the latent representations of entities and smooth their changes across time. Experiments on Yago and Wiki datasets demonstrate that our proposed algorithm outperforms the state-of-the-art static and temporal knowledge graph embedding models.

Overview

Integrity signals

Registry

Crossref · 10.1145/3437963.3441741

Cites

2 of 2 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Verification

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • In [Liao et al., 2021], relations at different timestamps are regarded as independent because they normally denote time-insensitive actions in datasets.

    “Relations at different timestamps are regarded as independent since they normally denote time-insensitive actions in datasets.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Liao et al., 2021

  • The expressive evolution patterns learned by [Liao et al., 2021]'s model are effective for TKGC in both offline and online scenarios.

    “The expressive evolution patterns learned by the model are shown to be effective for TKGC in both offline and online scenarios.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Liao et al., 2021

  • [Liao et al., 2021] adopts probabilistic entity representations based on variational Bayesian inference to jointly model entity features and uncertainty.

    “In contrast to deterministic approaches, [Liao et al., 2021] adopts probabilistic entity representations, based on variational Bayesian inference [Kingma and Welling, 2013], to jointly model the entity features and the uncertainty.”

    From Temporal Knowledge Graph Completion: A Survey (2023) · cites this paper as Liao et al., 2021

Citations

Cites

Flagged first, then by citations

Research works printed but not linked: 32

  1. Robert Bamler and Stephan Mandt. 2017. Dynamic word embeddings. In ICML. JMLR. org 380--389. Robert Bamler and Stephan Mandt. 2017. Dynamic word embeddings. In ICML. JMLR. org 380--389.
  2. Kurt Bollacker Colin Evans Praveen Paritosh Tim Sturge and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD. 1247--1250. Kurt Bollacker Colin Evans Praveen Paritosh Tim Sturge and Jamie Taylor. 2008. Freebase: a collaboratively created graph database for structuring human knowledge. In SIGMOD. 1247--1250. · DOI · Paper page
  3. Antoine Bordes , Sumit Chopra , and Jason Weston . 2014. Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 ( 2014 ). Antoine Bordes, Sumit Chopra, and Jason Weston. 2014. Question answering with subgraph embeddings. arXiv preprint arXiv:1406.3676 (2014). · arXiv
  4. Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems. 2787--2795. Antoine Bordes Nicolas Usunier Alberto Garcia-Duran Jason Weston and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In Advances in neural information processing systems. 2787--2795.
  5. Wenhu Chen , Wenhan Xiong , Xifeng Yan , and William Wang . 2018. Variational knowledge graph reasoning. arXiv preprint arXiv:1803.06581 ( 2018 ). Wenhu Chen, Wenhan Xiong, Xifeng Yan, and William Wang. 2018. Variational knowledge graph reasoning. arXiv preprint arXiv:1803.06581 (2018). · arXiv
  6. Shib Sankar Dasgupta , Swayambhu Nath Ray, and Partha Talukdar . 2018 . Hyte : Hyperplane-based temporally aware knowledge graph embedding. In EMNLP. 2001--2011. Shib Sankar Dasgupta, Swayambhu Nath Ray, and Partha Talukdar. 2018. Hyte: Hyperplane-based temporally aware knowledge graph embedding. In EMNLP. 2001--2011.
  7. Alberto Garcia-Duran , Sebastijan Dumanvc ić, and Mathias Niepert . 2018 . Learning Sequence Encoders for Temporal Knowledge Graph Completion. In EMNLP. 4816--4821. Alberto Garcia-Duran, Sebastijan Dumanvc ić, and Mathias Niepert. 2018. Learning Sequence Encoders for Temporal Knowledge Graph Completion. In EMNLP. 4816--4821.
  8. Shizhu He Kang Liu Guoliang Ji and Jun Zhao. 2015. Learning to represent knowledge graphs with gaussian embedding. In CIKM. 623--632. Shizhu He Kang Liu Guoliang Ji and Jun Zhao. 2015. Learning to represent knowledge graphs with gaussian embedding. In CIKM. 623--632.
  9. 10.1145/1963192.1963296 · DOI
  10. Woojeong Jin , Changlin Zhang , Pedro Szekely , and Xiang Ren . 2019. Recurrent event network for reasoning over temporal knowledge graphs. arXiv preprint arXiv:1904.05530 ( 2019 ). Woojeong Jin, Changlin Zhang, Pedro Szekely, and Xiang Ren. 2019. Recurrent event network for reasoning over temporal knowledge graphs. arXiv preprint arXiv:1904.05530 (2019). · arXiv
  11. Durk P Kingma , Shakir Mohamed , Danilo Jimenez Rezende, and Max Welling . 2014 . Semi-supervised learning with deep generative models. In Advances in neural information processing systems. 3581--3589. Durk P Kingma, Shakir Mohamed, Danilo Jimenez Rezende, and Max Welling. 2014. Semi-supervised learning with deep generative models. In Advances in neural information processing systems. 3581--3589.
  12. Diederik P Kingma and Max Welling . 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 ( 2013 ). Diederik P Kingma and Max Welling. 2013. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013). · arXiv
  13. Thomas N Kipf and Max Welling . 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 ( 2016 ). Thomas N Kipf and Max Welling. 2016. Variational graph auto-encoders. arXiv preprint arXiv:1611.07308 (2016). · arXiv
  14. 10.1145/3184558.3191639 · DOI · Paper page
  15. Shangsong Liang. 2019. Unsupervised Semantic Generative Adversarial Networks for Expert Retrieval. In WWW. 1039--1050. Shangsong Liang. 2019. Unsupervised Semantic Generative Adversarial Networks for Expert Retrieval. In WWW. 1039--1050. · DOI
  16. Yankai Lin Zhiyuan Liu Maosong Sun Yang Liu and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI . Yankai Lin Zhiyuan Liu Maosong Sun Yang Liu and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI .
  17. Yupeng Luo Shangsong Liang and Zaiqiao Meng. 2019. Constrained Co-embedding for User Profile in Community Question Answering. In CIKM. 439--448. Yupeng Luo Shangsong Liang and Zaiqiao Meng. 2019. Constrained Co-embedding for User Profile in Community Question Answering. In CIKM. 439--448.
  18. Zaiqiao Meng Shangsong Liang Jinyuan Fang and Teng Xiao. 2019. Semi-supervisedly Co-embedding Attributed Networks. In NeurIPS. 4507--4516. Zaiqiao Meng Shangsong Liang Jinyuan Fang and Teng Xiao. 2019. Semi-supervisedly Co-embedding Attributed Networks. In NeurIPS. 4507--4516. · DOI
  19. Zaiqiao Meng , Shangsong Liang , Xiangliang Zhang , Richard McCreadie , and Iadh Ounis . 2020. Jointly Learning Representations of Nodes and Attributes for Attributed Networks. ACM Transactions on Information Systems ( 2020 ). Zaiqiao Meng, Shangsong Liang, Xiangliang Zhang, Richard McCreadie, and Iadh Ounis. 2020. Jointly Learning Representations of Nodes and Attributes for Attributed Networks. ACM Transactions on Information Systems (2020).
  20. Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119. Tomas Mikolov Ilya Sutskever Kai Chen Greg S Corrado and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems. 3111--3119.
  21. 10.1145/219717.219748 · DOI · Paper page
  22. Maximilian Nickel Lorenzo Rosasco and Tomaso Poggio. 2016. Holographic embeddings of knowledge graphs. In AAAI . Maximilian Nickel Lorenzo Rosasco and Tomaso Poggio. 2016. Holographic embeddings of knowledge graphs. In AAAI . · DOI · Paper page
  23. Maximilian Nickel , Volker Tresp , and Hans-Peter Kriegel . 2011 . A three-way model for collective learning on multi-relational data .. In ICML , Vol. 11. 809 -- 816 . Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A three-way model for collective learning on multi-relational data.. In ICML, Vol. 11. 809--816.
  24. Rajesh Ranganath , Sean Gerrish , and David M Blei . 2014 . Black Box Variational Inference .. In International Conference on Artificial Intelligence and Statistics . Rajesh Ranganath, Sean Gerrish, and David M Blei. 2014. Black Box Variational Inference.. In International Conference on Artificial Intelligence and Statistics .
  25. Wei Shen Jianyong Wang Ping Luo and Min Wang. 2013. Linking named entities in tweets with knowledge base via user interest modeling. In SIGKDD. 68--76. Wei Shen Jianyong Wang Ping Luo and Min Wang. 2013. Linking named entities in tweets with knowledge base via user interest modeling. In SIGKDD. 68--76. · DOI
  26. Richard Socher Danqi Chen Christopher D Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In Advances in neural information processing systems. 926--934. Richard Socher Danqi Chen Christopher D Manning and Andrew Ng. 2013. Reasoning with neural tensor networks for knowledge base completion. In Advances in neural information processing systems. 926--934.
  27. Zhiqing Sun , Zhi-Hong Deng , Jian-Yun Nie , and Jian Tang . 2019 . Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019). Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019). · arXiv
  28. Rakshit Trivedi , Hanjun Dai , Yichen Wang , and Le Song . 2017 . Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In ICML. JMLR. org, 3462--3471. Rakshit Trivedi, Hanjun Dai, Yichen Wang, and Le Song. 2017. Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In ICML. JMLR. org, 3462--3471.
  29. Teng Xiao Jiaxin Ren Zaiqiao Meng Huan Sun and Shangsong Liang. 2019. Dynamic Bayesian Metric Learning for Personalized Product Search. In CIKM. 1693--1702. Teng Xiao Jiaxin Ren Zaiqiao Meng Huan Sun and Shangsong Liang. 2019. Dynamic Bayesian Metric Learning for Personalized Product Search. In CIKM. 1693--1702. · DOI
  30. Mohamed Yahya Klaus Berberich Shady Elbassuoni and Gerhard Weikum. 2013. Robust question answering over the web of linked data. In CIKM. 1107--1116. Mohamed Yahya Klaus Berberich Shady Elbassuoni and Gerhard Weikum. 2013. Robust question answering over the web of linked data. In CIKM. 1107--1116. · DOI
  31. Bishan Yang , Wen-tau Yih, Xiaodong He , Jianfeng Gao , and Li Deng . 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 ( 2014 ). Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2014. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014). · arXiv
  32. Biao Zhang , Deyi Xiong , Jinsong Su , Hong Duan , and Min Zhang . 2016. Variational neural machine translation. arXiv preprint arXiv:1605.07869 ( 2016 ). Biao Zhang, Deyi Xiong, Jinsong Su, Hong Duan, and Min Zhang. 2016. Variational neural machine translation. arXiv preprint arXiv:1605.07869 (2016). · arXiv

Cited by

Sorted by citations

Metadata

Authors
Liao, S., Liang, S., Meng, Z., & Zhang, Q.
Affiliations
  • Sun Yat-sen University, Guangzhou, China
  • University of Cambridge, Cambridge, United Kingdom
  • University College London, London, United Kingdom
Venue
Proceedings of the 14th ACM International Conference on Web Search and Data Mining
Publisher
ACM
Published
2021-03-08
Pages
535-543
DOI
10.1145/3437963.3441741
Cited by
34
References
2
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Funders
National Natural Science Foundation of China (61906219)

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review