Strobel T, Pychynski T, Harth A (2024)
Publication Type: Conference contribution
Publication year: 2024
URI: https://2024.eswc-conferences.org/wp-content/uploads/2024/04/ESWC_2024_paper_339.pdf
Open Access Link: https://2024.eswc-conferences.org/wp-content/uploads/2024/04/ESWC_2024_paper_339.pdf
Causal inference creates insights into observational data. Such insights could explain an outlying value to perform Root Cause Analysis. But how can causal inference be used with semantically annotated observations? The following demo showcases how to use semantically annotated sensor data for causal inference. The method’s implementation uses an agent pattern interacting with a knowledge graph.
APA:
Strobel, T., Pychynski, T., & Harth, A. (2024). Finding Root Causes for Outliers in Semantically Annotated Sensor Data. In Proceedings of the Extended Semantic Web Conference.
MLA:
Strobel, Tim, Tim Pychynski, and Andreas Harth. "Finding Root Causes for Outliers in Semantically Annotated Sensor Data." Proceedings of the Extended Semantic Web Conference 2024.
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