Using SPARQL to express Causality in Explainable Cyber-Physical Systems

Peb R. Aryan; Matthias Deimel; Fajar J. Ekaputra; Marta Sabou

doi:10.1109/ICAICTA53211.2021.9640268

Using SPARQL to express Causality in Explainable Cyber-Physical Systems

Peb R. Aryan
, Matthias Deimel
, Fajar J. Ekaputra
, Marta Sabou

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

Abstract

Causality knowledge is an essential component of an explainable cyber-physical systems. Since this knowledge mainly come from domain experts, manual annotation of causality is prone to errors especially if the system is large and complex. This paper describes how SPARQL queries can minimize domain expert work by (1)partitioning causality as two level of abstraction: abstract and concrete causality, and (2)inferring concrete causality from query result. We illustrate the usefulness of this method by showing the use of SPARQL in a smart grid scenario.

Originalsprache	Englisch
Titel des Sammelwerks	Proceedings : 2021 8th International Conference on Advanced Informatics
Untertitel des Sammelwerks	Concepts, Theory, and Application, ICAICTA 2021
Erscheinungsort	New York
Verlag	Institute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)	9781665417433
ISBN (Print)	978-1-6654-1744-0
DOIs	https://doi.org/10.1109/ICAICTA53211.2021.9640268
Publikationsstatus	Veröffentlicht - 2021
Extern publiziert	Ja
Veranstaltung	8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021 - Virtual, Bandung, Indonesien Dauer: 29 Sept. 2021 → 30 Sept. 2021

Konferenz

Konferenz	8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021
Land/Gebiet	Indonesien
Ort	Virtual, Bandung
Zeitraum	29/09/21 → 30/09/21

Bibliographische Notiz

Publisher Copyright:
© 2021 IEEE.

Zugriff auf Dokument

10.1109/ICAICTA53211.2021.9640268

Andere Dateien und Links

Verknüpfung zur Publikation in Scopus

Zitat

@inproceedings{4f376bb504d042a686c451281d627d16,

title = "Using SPARQL to express Causality in Explainable Cyber-Physical Systems",

abstract = "Causality knowledge is an essential component of an explainable cyber-physical systems. Since this knowledge mainly come from domain experts, manual annotation of causality is prone to errors especially if the system is large and complex. This paper describes how SPARQL queries can minimize domain expert work by (1)partitioning causality as two level of abstraction: abstract and concrete causality, and (2)inferring concrete causality from query result. We illustrate the usefulness of this method by showing the use of SPARQL in a smart grid scenario.",

keywords = "causality, cyber physical systems, explainability, knowledge graph, sparql",

author = "Aryan, \{Peb R.\} and Matthias Deimel and Ekaputra, \{Fajar J.\} and Marta Sabou",

note = "Funding Information: The authors would like to thank Austrian Research Promotion Agency (FFG) in the PoSyCo project (FFG No. 3036508). Publisher Copyright: {\textcopyright} 2021 IEEE.; 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021 ; Conference date: 29-09-2021 Through 30-09-2021",

year = "2021",

doi = "10.1109/ICAICTA53211.2021.9640268",

language = "English",

isbn = "978-1-6654-1744-0",

booktitle = "Proceedings : 2021 8th International Conference on Advanced Informatics",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

}

Aryan, PR, Deimel, M, Ekaputra, FJ & Sabou, M 2021, Using SPARQL to express Causality in Explainable Cyber-Physical Systems. in Proceedings : 2021 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021. Institute of Electrical and Electronics Engineers Inc., New York, 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021, Virtual, Bandung, Indonesien, 29/09/21. https://doi.org/10.1109/ICAICTA53211.2021.9640268

Using SPARQL to express Causality in Explainable Cyber-Physical Systems. / Aryan, Peb R.; Deimel, Matthias; Ekaputra, Fajar J. et al.
Proceedings : 2021 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021. New York: Institute of Electrical and Electronics Engineers Inc., 2021.

Publikation: Beitrag in Buch/Konferenzband › Beitrag in Konferenzband

TY - GEN

T1 - Using SPARQL to express Causality in Explainable Cyber-Physical Systems

AU - Aryan, Peb R.

AU - Deimel, Matthias

AU - Ekaputra, Fajar J.

AU - Sabou, Marta

PY - 2021

Y1 - 2021

N2 - Causality knowledge is an essential component of an explainable cyber-physical systems. Since this knowledge mainly come from domain experts, manual annotation of causality is prone to errors especially if the system is large and complex. This paper describes how SPARQL queries can minimize domain expert work by (1)partitioning causality as two level of abstraction: abstract and concrete causality, and (2)inferring concrete causality from query result. We illustrate the usefulness of this method by showing the use of SPARQL in a smart grid scenario.

AB - Causality knowledge is an essential component of an explainable cyber-physical systems. Since this knowledge mainly come from domain experts, manual annotation of causality is prone to errors especially if the system is large and complex. This paper describes how SPARQL queries can minimize domain expert work by (1)partitioning causality as two level of abstraction: abstract and concrete causality, and (2)inferring concrete causality from query result. We illustrate the usefulness of this method by showing the use of SPARQL in a smart grid scenario.

KW - causality

KW - cyber physical systems

KW - explainability

KW - knowledge graph

KW - sparql

UR - https://www.scopus.com/pages/publications/85123752083

U2 - 10.1109/ICAICTA53211.2021.9640268

DO - 10.1109/ICAICTA53211.2021.9640268

M3 - Contribution to conference proceedings

AN - SCOPUS:85123752083

SN - 978-1-6654-1744-0

BT - Proceedings : 2021 8th International Conference on Advanced Informatics

PB - Institute of Electrical and Electronics Engineers Inc.

CY - New York

T2 - 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021

Y2 - 29 September 2021 through 30 September 2021

ER -