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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings : 2021 8th International Conference on Advanced Informatics |
| Subtitle of host publication | Concepts, Theory, and Application, ICAICTA 2021 |
| Place of Publication | New York |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9781665417433 |
| ISBN (Print) | 978-1-6654-1744-0 |
| DOIs | |
| Publication status | Published - 2021 |
| Externally published | Yes |
| Event | 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021 - Virtual, Bandung, Indonesia Duration: 29 Sept 2021 → 30 Sept 2021 |
Conference
| Conference | 8th International Conference on Advanced Informatics: Concepts, Theory, and Application, ICAICTA 2021 |
|---|---|
| Country/Territory | Indonesia |
| City | Virtual, Bandung |
| Period | 29/09/21 → 30/09/21 |
Bibliographical note
Funding Information:The authors would like to thank Austrian Research Promotion Agency (FFG) in the PoSyCo project (FFG No. 3036508).
Publisher Copyright:
© 2021 IEEE.
Keywords
- causality
- cyber physical systems
- explainability
- knowledge graph
- sparql