  Real-world datasets collected with sensor networks often contain incomplete
and uncertain labels as well as artefacts arising from the system environment.
Complete and reliable labeling is often infeasible for large-scale and
long-term sensor network deployments due to the labor and time overhead,
limited availability of experts and missing ground truth. In addition, if the
machine learning method used for analysis is sensitive to certain features of a
deployment, labeling and learning needs to be repeated for every new
deployment. To address these challenges, we propose to make use of system
context information formalized in an information graph and embed it in the
learning process via contrastive learning. Based on real-world data we show
that this approach leads to an increased accuracy in case of weakly labeled
data and leads to an increased robustness and transferability of the classifier
to new sensor locations.
