research-article

CauseNet: Towards a Causality Graph Extracted from the Web

Authors:

Stefan Heindorf,

Henning Wachsmuth,

Axel-Cyrille Ngonga Ngomo,

Martin PotthastAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 3023 - 3030

https://doi.org/10.1145/3340531.3412763

Published: 19 October 2020 Publication History

Abstract

Causal knowledge is seen as one of the key ingredients to advance artificial intelligence. Yet, few knowledge bases comprise causal knowledge to date, possibly due to significant efforts required for validation. Notwithstanding this challenge, we compile CauseNet, a large-scale knowledge base of claimed causal relations between causal concepts. By extraction from different semi- and unstructured web sources, we collect more than 11 million causal relations with an estimated extraction precision of 83% and construct the first large-scale and open-domain causality graph. We analyze the graph to gain insights about causal beliefs expressed on the web and we demonstrate its benefits in basic causal question answering. Future work may use the graph for causal reasoning, computational argumentation, multi-hop question answering, and more.

Supplementary Material

MP4 File (3340531.3412763.mp4)

Presentation Video

Download
21.34 MB

References

[1]

Eugene Agichtein and Luis Gravano. 2000. Snowball: Extracting Relations from Large Plain-Text Collections. In ACM DL. ACM, 85--94.

Digital Library

[2]

Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018. Contextual String Embeddings for Sequence Labeling. In COLING. ACL, 1638--1649.

[3]

Sö ren Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary G. Ives. 2007. DBpedia: A Nucleus for a Web of Open Data. In ISWC/ASWC, Vol. 4825. Springer, 722--735.

Digital Library

[4]

Payal Bajaj, Daniel Campos, Nick Craswell, Li Deng, Jianfeng Gao, Xiaodong Liu, Rangan Majumder, Andrew McNamara, Bhaskar Mitra, Tri Nguyen, Mir Rosenberg, Xia Song, Alina Stoica, Saurabh Tiwary, and Tong Wang. 2018. MS MARCO: A Human Generated MAchine Reading COmprehension Dataset. CoRR, Vol. abs/1611.09268v3 (2018).

[5]

Daniel Berenberg and James P. Bagrow. 2018. Efficient Crowd Exploration of Large Networks: The Case of Causal Attribution. PACMHCI, Vol. 2, CSCW (2018), 24:1--24:25.

[6]

Sergey Brin. 1998. Extracting Patterns and Relations from the World Wide Web. In WebDB (LNCS, Vol. 1590). Springer, 172--183.

[7]

Merry Bullock and Rochel Gelman. 1979. Preschool Children's Assumptions about Cause and Effect: Temporal Ordering. Child Development, Vol. 50, 1 (1979), 89--96.

[8]

Razvan C. Bunescu and Raymond J. Mooney. 2005. A Shortest Path Dependency Kernel for Relation Extraction. In HLT/EMNLP. ACL, 724--731.

Digital Library

[9]

Aron Culotta and Jeffrey S. Sorensen. 2004. Dependency Tree Kernels for Relation Extraction. In ACL. ACL, 423--429.

[10]

Tirthankar Dasgupta, Rupsa Saha, Lipika Dey, and Abir Naskar. 2018. Automatic Extraction of Causal Relations from Text using Linguistically Informed Deep Neural Networks. In SIGDIAL Conference. ACL, 306--316.

[11]

Roxana Girju. 2003. Automatic Detection of Causal Relations for Question Answering. In Proceedings of the ACL 2003 workshop on Multilingual summarization and question answering-Volume 12. ACL, 76--83.

Digital Library

[12]

Sonal Gupta and Christopher D. Manning. 2014. Improved Pattern Learning for Bootstrapped Entity Extraction. In CoNLL. ACL, 98--108.

[13]

Chikara Hashimoto. 2019. Weakly Supervised Multilingual Causality Extraction from Wikipedia. In EMNLP/IJCNLP. ACL, 2986--2997.

[14]

Oktie Hassanzadeh, Debarun Bhattacharjya, Mark Feblowitz, Kavitha Srinivas, Michael Perrone, Shirin Sohrabi, and Michael Katz. 2019. Answering Binary Causal Questions Through Large-Scale Text Mining: An Evaluation Using Cause-Effect Pairs from Human Experts. In IJCAI. 5003--5009.

[15]

Iris Hendrickx, Su Nam Kim, Zornitsa Kozareva, Preslav Nakov, Diarmuid Ó Sé aghdha, Sebastian Padó, Marco Pennacchiotti, Lorenza Romano, and Stan Szpakowicz. 2010. SemEval-2010 Task 8: Multi-Way Classification of Semantic Relations between Pairs of Nominals. In SemEval@ACL. ACL, 33--38.

[16]

Christopher Hitchcock. 2020. Causal Models. In The Stanford Encyclopedia of Philosophy summer 2020 ed.), Edward N. Zalta (Ed.). Metaphysics Research Lab, Stanford University.

[17]

Menno Hulswit. 2004. A Short History of Causation. SEED Journal (Semiotics, Evolution, Energy, and Development), Vol. 4, 3 (2004), 16--42.

[18]

Ashwin Ittoo and Gosse Bouma. 2013. Minimally-Supervised Learning of Domain-specific Causal Relations Using an Open-Domain Corpus as Knowledge Base. Data Knowl. Eng., Vol. 88 (2013), 142--163.

Digital Library

[19]

Sophia Katrenko and Pieter W. Adriaans. 2006. Learning Relations from Biomedical Corpora Using Dependency Trees. In KDECB (LNCS, Vol. 4366). Springer, 61--80.

[20]

Zornitsa Kozareva. 2012. Cause-Effect Relation Learning. In TextGraphs@ACL. ACL, 39--43.

[21]

Volodymyr Kuleshov, Jialin Ding, Christopher Vo, Braden Hancock, Alexander Ratner, Yang Li, Christopher Ré, Serafim Batzoglou, and Michael Snyder. 2019. A Machine-compiled Database of Genome-wide Association Studies. Nature communications, Vol. 10, 1 (2019), 3341.

[22]

Pengfei Li and Kezhi Mao. 2019. Knowledge-oriented Convolutional Neural Network for Causal Relation Extraction from Natural Language Texts. Expert Syst. Appl., Vol. 115 (2019), 512--523.

[23]

Zhaoning Li, Qi Li, Xiaotian Zou, and Jiangtao Ren. 2019. Causality Extraction based on Self-Attentive BiLSTM-CRF with Transferred Embeddings. CoRR, Vol. abs/1904.07629 (2019).

[24]

Farzaneh Mahdisoltani, Joanna Biega, and Fabian M. Suchanek. 2015. YAGO3: A Knowledge Base from Multilingual Wikipedias. In CIDR.

[25]

Pablo N. Mendes, Max Jakob, André s Garc'i a-Silva, and Christian Bizer. 2011. DBpedia Spotlight: Shedding Light on the Web of Documents. In I-SEMANTICS. ACM, 1--8.

[26]

George A. Miller. 1995. WordNet: A Lexical Database for English. Communications of the ACM, Vol. 38, 11 (1995), 39--41.

Digital Library

[27]

Mike Mintz, Steven Bills, Rion Snow, and Daniel Jurafsky. 2009. Distant Supervision for Relation Extraction Without Labeled Data. In ACL/IJCNLP. ACL, 1003--1011.

[28]

Mohamed Morsey, Jens Lehmann, Sö ren Auer, Claus Stadler, and Sebastian Hellmann. 2012. DBpedia and the Live Extraction of Structured Data from Wikipedia. Program, Vol. 46, 2 (2012), 157--181.

[29]

Roberto Navigli and Simone Paolo Ponzetto. 2010. BabelNet: Building a Very Large Multilingual Semantic Network. In ACL. ACL, 216--225.

[30]

Patrick Pantel and Marco Pennacchiotti. 2006. Espresso: Leveraging Generic Patterns for Automatically Harvesting Semantic Relations. In ACL. ACL.

Digital Library

[31]

Judea Pearl. 2000. Causality: Models, Reasoning, and Inference. Cambridge University Press, USA.

Digital Library

[32]

Peng Qi, Timothy Dozat, Yuhao Zhang, and Christopher D. Manning. 2018. Universal Dependency Parsing from Scratch. In CoNLL Shared Task. ACL, 160--170.

[33]

Kira Radinsky, Sagie Davidovich, and Shaul Markovitch. 2012. Learning Causality for News Events Prediction. In WWW. ACM, 909--918.

[34]

Sebastian Schuster and Christopher D. Manning. 2016. Enhanced English Universal Dependencies: An Improved Representation for Natural Language Understanding Tasks. In LREC. ELRA.

[35]

Rebecca Sharp, Mihai Surdeanu, Peter Jansen, Peter Clark, and Michael Hammond. 2016. Creating Causal Embeddings for Question Answering with Minimal Supervision. In EMNLP. ACL, 138--148.

[36]

Ashadullah Shawon, Syed Tauhid Zuhori, Firoz Mahmud, and Md Jamil-Ur Rahman. 2018. Website Classification Using Word Based Multiple N-Gram Models and Random Search Oriented Feature Parameters. In ICCIT. IEEE, 1--6.

[37]

Robyn Speer, Joshua Chin, and Catherine Havasi. 2017. ConceptNet 5.5: An Open Multilingual Graph of General Knowledge. In AAAI. 4444--4451.

[38]

Denny Vrandecic and Markus Krö tzsch. 2014. Wikidata: A Free Collaborative Knowledgebase. Communications of the ACM, Vol. 57, 10 (2014), 78--85.

Digital Library

[39]

Ryen W. White and Eric Horvitz. 2009. Cyberchondria: Studies of the Escalation of Medical Concerns in Web Search. ACM Transactions on Information Systems, Vol. 27, 4 (2009), 23:1--23:37.

Digital Library

Cited By

Markakis MZhang ZShahout RGao TLiu CSabek ICafarella M(2024)Press ECCS to Doubt (Your Causal Graph)Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI10.1145/3665601.3669842(6-15)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3665601.3669842
Jin LZhao FYan CGui X(2024)Causal Graph Representation Learning for Outcome-Oriented Link Prediction2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651266(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651266
Xu ZTakamura HIchise R(2024)A Framework to Construct Financial Causality Knowledge Graph from Text2024 IEEE 18th International Conference on Semantic Computing (ICSC)10.1109/ICSC59802.2024.00015(57-64)Online publication date: 5-Feb-2024
https://doi.org/10.1109/ICSC59802.2024.00015
Show More Cited By

Index Terms

CauseNet: Towards a Causality Graph Extracted from the Web
1. Information systems

Recommendations

Granular Causality Applications: Using Part-of Relations for Discovering Causality

Causal markers, syntactic structures and connectives have been the sole identifying features for automatically extracting causal relations in natural language discourse. However, various connectives such as "and", prepositions such as "as", and other ...
Explaining Disease: Correlations, Causes, and Mechanisms

Why do people get sick? I argue that a disease explanation is best thought of as causal network instantiation, where a causal network describes the interrelations among multiple factors, and instantiation consists of observational or hypothetical ...
Time-varying causality between research output and economic growth in US

This main purpose of this paper is to investigate the causal relationship between knowledge (research output) and economic growth in US over 1981–2011. To overcome the issues of ignoring possible instability and hence, falsely assuming a constant ...

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 October 2020

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

CIKM '20

Sponsor:

CIKM '20: The 29th ACM International Conference on Information and Knowledge Management

October 19 - 23, 2020

Virtual Event, Ireland

Acceptance Rates

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

Upcoming Conference

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

21
Total Citations
View Citations
725
Total Downloads

Downloads (Last 12 months)172
Downloads (Last 6 weeks)22

Reflects downloads up to 06 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Markakis MZhang ZShahout RGao TLiu CSabek ICafarella M(2024)Press ECCS to Doubt (Your Causal Graph)Proceedings of the Conference on Governance, Understanding and Integration of Data for Effective and Responsible AI10.1145/3665601.3669842(6-15)Online publication date: 9-Jun-2024
https://dl.acm.org/doi/10.1145/3665601.3669842
Jin LZhao FYan CGui X(2024)Causal Graph Representation Learning for Outcome-Oriented Link Prediction2024 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN60899.2024.10651266(1-8)Online publication date: 30-Jun-2024
https://doi.org/10.1109/IJCNN60899.2024.10651266
Xu ZTakamura HIchise R(2024)A Framework to Construct Financial Causality Knowledge Graph from Text2024 IEEE 18th International Conference on Semantic Computing (ICSC)10.1109/ICSC59802.2024.00015(57-64)Online publication date: 5-Feb-2024
https://doi.org/10.1109/ICSC59802.2024.00015
Huang HVidal M(2024)CauseKG: A Framework Enhancing Causal Inference With Implicit Knowledge Deduced From Knowledge GraphsIEEE Access10.1109/ACCESS.2024.339513412(61810-61827)Online publication date: 2024
https://doi.org/10.1109/ACCESS.2024.3395134
Gao JDing XLi ZLiu TQin B(2024)Event Causality Identification via Competitive-Cooperative Cognition NetworksKnowledge-Based Systems10.1016/j.knosys.2024.112139300(112139)Online publication date: Sep-2024
https://doi.org/10.1016/j.knosys.2024.112139
Shou XBhattacharjya DGao TSubramanian DHassanzadeh OBennett KOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Pairwise causality guided transformers for event sequencesProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3668140(46520-46533)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3668140
Shou XBhattacharjya DGao TSubramanian DHassanzadeh OBennett KKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Probabilistic attention-to-influence neural models for event sequencesProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619720(31657-31674)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619720
Youngmann BCafarella MSalimi BZeng A(2023)Causal Data IntegrationProceedings of the VLDB Endowment10.14778/3603581.360360216:10(2659-2665)Online publication date: 1-Jun-2023
https://dl.acm.org/doi/10.14778/3603581.3603602
Zhang JIlievski FMa KKollaa AFrancis JOltramari ASingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)A Study of Situational Reasoning for Traffic UnderstandingProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599246(3262-3272)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599246
Kayesh HIslam MWang J(2023)Answering Binary Causal Questions Using Role-Oriented Concept EmbeddingIEEE Transactions on Artificial Intelligence10.1109/TAI.2022.32042454:6(1426-1436)Online publication date: Dec-2023
https://doi.org/10.1109/TAI.2022.3204245
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents