research-article

Constructing and Embedding Abstract Event Causality Networks from Text Snippets

Authors:

ChengXiang ZhaiAuthors Info & Claims

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

Pages 335 - 344

https://doi.org/10.1145/3018661.3018707

Published: 02 February 2017 Publication History

Abstract

In this paper, we formally define the problem of representing and leveraging abstract event causality to power downstream applications. We propose a novel solution to this problem, which build an abstract causality network and embed the causality network into a continuous vector space. The abstract causality network is generalized from a specific one, with abstract event nodes represented by frequently co-occurring word pairs. To perform the embedding task, we design a dual cause-effect transition model. Therefore, the proposed method can obtain general, frequent, and simple causality patterns, meanwhile, simplify event matching. Given the causality network and the learned embeddings, our model can be applied to a wide range of applications such as event prediction, event clustering and stock market movement prediction. Experimental results demonstrate that 1) the abstract causality network is effective for discovering high-level causality rules behind specific causal events; 2) the embedding models perform better than state-of-the-art link prediction techniques in predicting events; and 3) the event causality embedding is an easy-to-use and sophisticated feature for downstream applications such as stock market movement prediction.

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Cited By

Mu FLi W(2025)Enhancing Narrative Commonsense Reasoning With Multilevel Causal KnowledgeIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338085136:3(5601-5613)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3380851
Yu XSmith J(2024)Causal chain event graphs for remedial maintenanceRisk Analysis10.1111/risa.14308Online publication date: 23-Apr-2024
https://doi.org/10.1111/risa.14308
Zhang SLi YSheng D(2024)An information support framework for chain reaction of major emergencies based on causality eventic graphData Technologies and Applications10.1108/DTA-01-2024-0048Online publication date: 17-Dec-2024
https://doi.org/10.1108/DTA-01-2024-0048
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Index Terms

Constructing and Embedding Abstract Event Causality Networks from Text Snippets
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
    2. Natural language processing
      1. Information extraction
2. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Expert systems

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cover image ACM Conferences

WSDM '17: Proceedings of the Tenth ACM International Conference on Web Search and Data Mining

February 2017

868 pages

ISBN:9781450346757

DOI:10.1145/3018661

General Chairs:
Maarten de Rijke
University of Amsterdam
,
Milad Shokouhi
Microsoft
,
Program Chairs:
Andrew Tomkins
Google
,
Min Zhang
Tsinghua University

Copyright © 2017 ACM.

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Published: 02 February 2017

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National Natural Science Foundation of China
National Key Basic Research Program of China

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WSDM 2017

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WSDM 2017: Tenth ACM International Conference on Web Search and Data Mining

February 6 - 10, 2017

Cambridge, United Kingdom

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WSDM '17 Paper Acceptance Rate 80 of 505 submissions, 16%;

Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Mu FLi W(2025)Enhancing Narrative Commonsense Reasoning With Multilevel Causal KnowledgeIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2024.338085136:3(5601-5613)Online publication date: Mar-2025
https://doi.org/10.1109/TNNLS.2024.3380851
Yu XSmith J(2024)Causal chain event graphs for remedial maintenanceRisk Analysis10.1111/risa.14308Online publication date: 23-Apr-2024
https://doi.org/10.1111/risa.14308
Zhang SLi YSheng D(2024)An information support framework for chain reaction of major emergencies based on causality eventic graphData Technologies and Applications10.1108/DTA-01-2024-0048Online publication date: 17-Dec-2024
https://doi.org/10.1108/DTA-01-2024-0048
Zhang YBai RKong LWang X(2024)2SCE-4SL: a 2-stage causality extraction framework for scientific literatureScientometrics10.1007/s11192-023-04817-z129:11(7175-7195)Online publication date: 1-Nov-2024
https://dl.acm.org/doi/10.1007/s11192-023-04817-z
Hu JShu XXie XNi XYang YShen S(2024)Construction and Application of Knowledge Graph for Building FireFire Technology10.1007/s10694-024-01544-660:3(1711-1739)Online publication date: 21-Feb-2024
https://doi.org/10.1007/s10694-024-01544-6
Shirai SBhattacharjya DHassanzadeh O(2023)Event Prediction using Case-Based Reasoning over Knowledge GraphsProceedings of the ACM Web Conference 202310.1145/3543507.3583201(2383-2391)Online publication date: 30-Apr-2023
https://dl.acm.org/doi/10.1145/3543507.3583201
Duan JTian LLi K(2023)Event Predictability: A Uniform Form for IoT-Based Nondeterministic Social SystemsIEEE Internet of Things Journal10.1109/JIOT.2023.324772610:14(12496-12507)Online publication date: 15-Jul-2023
https://doi.org/10.1109/JIOT.2023.3247726
Kobayashi RMurayama YIzumi K(2023)Impact Analysis of Social Events on Industries through Narrative Causal Search2023 IEEE International Conference on Big Data (BigData)10.1109/BigData59044.2023.10386410(2845-2854)Online publication date: 15-Dec-2023
https://doi.org/10.1109/BigData59044.2023.10386410
Yuan XGuan RZuo WZhang Y(2023)The Causal Strength Bank: A New Benchmark for Causal Strength ClassificationAdvances in Knowledge Discovery and Data Mining10.1007/978-3-031-33374-3_9(111-122)Online publication date: 27-May-2023
https://doi.org/10.1007/978-3-031-33374-3_9
Yang CWu QWen QZhou ZSun LYan JKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Towards out-of-distribution sequential event predictionProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3601916(22656-22670)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3601916
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