Using SPARQL to express Causality in Explainable Cyber-Physical Systems

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

Publikation: Beitrag in Buch/KonferenzbandBeitrag 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.

OriginalspracheEnglisch
Titel des SammelwerksProceedings : 2021 8th International Conference on Advanced Informatics
Untertitel des SammelwerksConcepts, Theory, and Application, ICAICTA 2021
ErscheinungsortNew York
VerlagInstitute of Electrical and Electronics Engineers Inc.
ISBN (elektronisch)9781665417433
ISBN (Print)978-1-6654-1744-0
DOIs
PublikationsstatusVeröffentlicht - 2021
Extern publiziertJa
Veranstaltung8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021 - Virtual, Bandung, Indonesien
Dauer: 29 Sept. 202130 Sept. 2021

Konferenz

Konferenz8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021
Land/GebietIndonesien
OrtVirtual, Bandung
Zeitraum29/09/2130/09/21

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© 2021 IEEE.

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