Article

Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding

Authors:

Mojtaba Nayyeri,

Fouad Alkhoury,

Jens LehmannAuthors Info & Claims

The Semantic Web – ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part I

Pages 654 - 671

https://doi.org/10.1007/978-3-030-62419-4_37

Published: 02 November 2020 Publication History

Abstract

Knowledge Graph (KG) embedding has attracted more attention in recent years. Most KG embedding models learn from time-unaware triples. However, the inclusion of temporal information besides triples would further improve the performance of a KGE model. In this regard, we propose ATiSE, a temporal KG embedding model which incorporates time information into entity/relation representations by using Additive Time Series decomposition. Moreover, considering the temporal uncertainty during the evolution of entity/relation representations over time, we map the representations of temporal KGs into the space of multi-dimensional Gaussian distributions. The mean of each entity/relation embedding at a time step shows the current expected position, whereas its covariance (which is temporally stationary) represents its temporal uncertainty. Experimental results show that ATiSE significantly outperforms the state-of-the-art KGE models and the existing temporal KGE models on link prediction over four temporal KGs.

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Cited By

Zhang BLi JDai YSerra ESpezzano F(2024)Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph EmbeddingsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679896(4248-4252)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679896
Fang ZLei SZhu XYang CZhang SYin XQin JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge GraphProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657706(70-79)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657706
Hu HGuo WLiu XLiu YTang RZhang RKan MAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)User Behavior Enriched Temporal Knowledge Graphs for Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635762(266-275)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635762
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Index Terms

Temporal Knowledge Graph Completion Based on Time Series Gaussian Embedding
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
2. Information systems
  1. Information systems applications

Index terms have been assigned to the content through auto-classification.

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Information & Contributors

Information

Published In

cover image Guide Proceedings

The Semantic Web – ISWC 2020: 19th International Semantic Web Conference, Athens, Greece, November 2–6, 2020, Proceedings, Part I

Nov 2020

728 pages

ISBN:978-3-030-62418-7

DOI:10.1007/978-3-030-62419-4

Editors:
Jeff Z. Pan
University of Edinburgh, Edinburgh, UK
,
Valentina Tamma
University of Liverpool, Liverpool, UK
,
Claudia d’Amato
University of Bari, Bari, Italy
,
Krzysztof Janowicz
University of California, Santa Barbara, Santa Barbara, CA, USA
,
Bo Fu
California State University, Long Beach, Long Beach, CA, USA
,
Axel Polleres
Vienna University of Economics and Business, Vienna, Austria
,
Oshani Seneviratne
Rensselaer Polytechnic Institute, Troy, NY, USA
,
Lalana Kagal
Massachusetts Institute of Technology, Cambridge, MA, USA

© Springer Nature Switzerland AG 2020.

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 02 November 2020

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Zhang BLi JDai YSerra ESpezzano F(2024)Distilling Knowledge Based on Curriculum Learning for Temporal Knowledge Graph EmbeddingsProceedings of the 33rd ACM International Conference on Information and Knowledge Management10.1145/3627673.3679896(4248-4252)Online publication date: 21-Oct-2024
https://dl.acm.org/doi/10.1145/3627673.3679896
Fang ZLei SZhu XYang CZhang SYin XQin JHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Transformer-based Reasoning for Learning Evolutionary Chain of Events on Temporal Knowledge GraphProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657706(70-79)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657706
Hu HGuo WLiu XLiu YTang RZhang RKan MAngélica LLattanzi SMuñoz Medina AAkoglu LGionis AVassilvitskii S(2024)User Behavior Enriched Temporal Knowledge Graphs for Sequential RecommendationProceedings of the 17th ACM International Conference on Web Search and Data Mining10.1145/3616855.3635762(266-275)Online publication date: 4-Mar-2024
https://dl.acm.org/doi/10.1145/3616855.3635762
Wang JCui ZWang BPan SGao JYin BGao WChua TNgo CKa-Wei Lee RKumar RLauw H(2024)IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph CompletionProceedings of the ACM Web Conference 202410.1145/3589334.3645361(1954-1962)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645361
Cai BXiang YGao LZhang HLi YLi JElkind E(2023)Temporal knowledge graph completionProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/734(6545-6553)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/734
Chen ZXu CSu FHuang ZDou Y(2023)Meta-Learning Based Knowledge Extrapolation for Temporal Knowledge GraphProceedings of the ACM Web Conference 202310.1145/3543507.3583279(2433-2443)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583279
Liang KMeng LLiu MLiu YTu WWang SZhou SLiu XChen HDuh WHuang HKato MMothe JPoblete B(2023)Learn from Relational Correlations and Periodic Events for Temporal Knowledge Graph ReasoningProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591711(1559-1568)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591711
Chen ZXu CSu FHuang ZDou YChen HDuh WHuang HKato MMothe JPoblete B(2023)Incorporating Structured Sentences with Time-enhanced BERT for Fully-inductive Temporal Relation PredictionProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591700(889-899)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591700
Bai LMa XMeng XRen XKe Y(2023)RoANEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106308123:PBOnline publication date: 1-Aug-2023
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Vu TNgo HNguyen NLe T(2023)TouriER: Temporal Knowledge Graph Completion by Leveraging Fourier TransformsIntegrated Uncertainty in Knowledge Modelling and Decision Making10.1007/978-3-031-46781-3_7(67-78)Online publication date: 2-Nov-2023
https://dl.acm.org/doi/10.1007/978-3-031-46781-3_7
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