Finding Root Causes for Outliers in Semantically Annotated Sensor Data - Strobel, Tim; Pychynski, Tim; Harth, Andreas (Extended Semantic Web Conference, 2024)
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 grap

Root Cause Analysis for Manufacturing using Semantic Web Technologies - Strobel, Tim (International Semantic Web Conference, 2023)
Root Cause Analysis is a method to identify the cause of problems in manufacturing. The analysis is usually performed manually by experts, is time-consuming, and costly. Therefore, the automation of the analysis process is of interest. However, besides measurements from manufacturing processes, prior knowledge is needed to create results comparable to those from manual analysis. This work uses Semantic Web technologies to model prior knowledge and Neuro-Symbolic AI to reason for root causes. The Neuro-Symbolic AI combines reasoning on observed production data and on prior knowledge. Using such approach makes it possible to combine efficient pattern recognition and probabilistic reasoning for data analysis. This work shows an evaluation workflow for such proposed methods with a data generation model. Preliminary results show the conceptualization of available prior manufacturing knowledge. The future steps contain the research on data models and the evaluation of existing Neuro-Symbolic approaches.<br