research-article

Differential Causal Rules Mining in Knowledge Graphs

Authors:

Nathalie Pernelle,

Rallou ThomopoulosAuthors Info & Claims

K-CAP '21: Proceedings of the 11th Knowledge Capture Conference

Pages 105 - 112

https://doi.org/10.1145/3460210.3493584

Published: 02 December 2021 Publication History

Abstract

In recent years, keen interest towards Knowledge Graphs has increased in both academia and the industry which has led to the creation of various datasets and the development of different research topics. In this paper, we present an approach that discovers differential causal rules in Knowledge Graphs. Such rules express that for two different class instances, a different treatment leads to different outcomes. Discovering causal rules is often the key of experiments, independently of their domain. The proposed approach is based on semantic matching relying on community detection and strata that can be defined as complex sub-classes. An experimental evaluation on two datasets shows that such mined rules can help gain insights into various domains.

References

[1]

José J. Ramasco Andrea Lancichinetti, Filippo Radicchi and Santo Fortunato. 2011. Finding statistically significant communities in networks. PLoS One (2011).

[2]

Vincent D Blondel, Jean-Loup Guillaume, Renaud Lambiotte, and Etienne Lefebvre. 2008. Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, Vol. 2008, 10 (Oct 2008), P10008.

[3]

Luis Galárraga, Christina Teflioudi, Katja Hose, and Fabian Suchanek. 2013. AMIE: Association rule mining under incomplete evidence in ontological knowledge bases. WWW 2013 - Proceedings of the 22nd International Conference on World Wide Web, 413--422.

Digital Library

[4]

Stefano M. Iacus, Gary King, and Giuseppe Porro. 2012. Causal inference without balance checking: Coarsened exact matching . Political Analysis, Vol. 20, 1 (2012), 1--24.

[5]

Georgi Kobilarov, Christian Bizer, Sören Auer, and Jens Lehmann. 2009. DBpedia - A Linked Data Hub and Data Source for Web Applications and Enterprises.

[6]

Jiuyong Li, Thuc Duy Le, Lin Liu, Jixue Liu, Zhou Jin, and Bingyu Sun. 2013. Mining causal association rules . Proceedings - IEEE 13th International Conference on Data Mining Workshops, ICDMW 2013 (2013), 114--123.

Digital Library

[7]

Melanie Munch, Juliette Dibie, Pierre-Henri Wuillemin, and Cristina E. Manfredotti. 2019. Towards Interactive Causal Relation Discovery Driven by an Ontology. In International Florida Artificial Intelligence Research Society Conference .

[8]

Petra Kralj Novak, Nada Lavravc, and Geoffrey I. Webb. 2009. Supervised Descriptive Rule Discovery: A Unifying Survey of Contrast Set, Emerging Pattern and Subgroup Mining. J. Mach. Learn. Res., Vol. 10 (June 2009), 377--403.

[9]

Stefano Ortona, Venkata Vamsikrishna Meduri, and Paolo Papotti. 2018. RuDiK: Rule discovery in knowledge bases . Proceedings of the VLDB Endowment, Vol. 11 (2018), 1946--1949.

Digital Library

[10]

Judea Pearl. 2009. Causality .Cambridge University Press.

[11]

Alina Petrova, Evgeny Sherkhonov, Bernardo Cuenca Grau, and Ian Horrocks. 2017. Entity comparison in RDF graphs ., Vol. 10587 LNCS (2017), 526--541.

[12]

Joe Raad, Nathalie Pernelle, and Fatiha Saïs. 2017. Detection of contextual identity links in a knowledge base. In Proceedings of the Knowledge Capture Conference, K-CAP 2017 .

Digital Library

[13]

Rubin D. B. 1974. Estimating causal effects of treatment in randomized and nonrandomized studies . Journal of Educational Psychology, Vol. 66, 5 (1974), 688--701.

[14]

Nicolas Salliou, Patrick Taillandier, and Rallou Thomopoulos. 2019. VITAMIN project (VegetarIan Transition Argument ModellINg) .

[15]

Elizabeth A. Stuart. 2010. Matching methods for causal inference: A review and a look forward. Statistical science : a review journal of the Institute of Mathematical Statistics, Vol. 25, 1 (2010), 1--21.

[16]

Magdalena Szumilas. 2010. Explaining odds ratios. Journal of the Canadian Academy of Child and Adolescent Psychiatry l'adolescent, Vol. 19 (2010), 227.

[17]

Rallou Thomopoulos, Nicolas Salliou, Patrick Taillandier, and Alberto Tonda. 2020. Consumers' Motivations towards Environment-Friendly Dietary Changes: An Assessment of Trends Related to the Consumption of Animal Products . In Handbook of Climate Change Across the Food Supply Chain.

[18]

Clifford H. Wagner. 1982. Simpson's Paradox in Real Life. The American Statistician, Vol. 36, 1 (1982), 46--48.

[19]

Po-Wei Wang, Daria Stepanova, Csaba Domokos, and J. Zico Kolter. 2020. Differentiable learning of numerical rules in knowledge graphs. In 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia, April 26--30, 2020 .

Cited By

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
Simonne LPernelle NSaïs FThomopoulos R(2022)Discovering Causal Rules in Knowledge Graphs using Graph Embeddings2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00023(95-102)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00023
Simonne LPernelle NSaïs F(2022)Counter Effect Rules Mining in Knowledge GraphsKnowledge Engineering and Knowledge Management10.1007/978-3-031-17105-5_12(167-173)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17105-5_12

Index Terms

Differential Causal Rules Mining in Knowledge Graphs
1. Computing methodologies
  1. Artificial intelligence
    1. Knowledge representation and reasoning
      1. Causal reasoning and diagnostics
2. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

K-CAP '21: Proceedings of the 11th Knowledge Capture Conference

December 2021

300 pages

ISBN:9781450384575

DOI:10.1145/3460210

General Chair:
Anna Lisa Gentile
IBM Research Almaden, USA
,
Program Chair:
Rafael Gonçalves
Center for Computational Biomedicine, Harvard Medical School, USA

Copyright © 2021 ACM.

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Publication History

Published: 02 December 2021

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K-CAP '21: Knowledge Capture Conference

December 2 - 3, 2021

Virtual Event, USA

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Cited By

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
Simonne LPernelle NSaïs FThomopoulos R(2022)Discovering Causal Rules in Knowledge Graphs using Graph Embeddings2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)10.1109/WI-IAT55865.2022.00023(95-102)Online publication date: Nov-2022
https://doi.org/10.1109/WI-IAT55865.2022.00023
Simonne LPernelle NSaïs F(2022)Counter Effect Rules Mining in Knowledge GraphsKnowledge Engineering and Knowledge Management10.1007/978-3-031-17105-5_12(167-173)Online publication date: 26-Sep-2022
https://dl.acm.org/doi/10.1007/978-3-031-17105-5_12

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