Diabetes Type 1 is a metabolic disease which results in a lack of insulin production, causing high glucose levels in the blood. It is crucial for diabetic patients to balance this glucose level, and they depend on external substances to do so. In order to keep this level under control, they usually need to resort to invasive glucose control methods, such as taking a sample drop of blood from their finger and have it analyzed. Recently, other directions emerged to offer alternative ways to estimate glucose level, using indirect sensor measurements including ECG monitoring and other physiological parameters. This paper showcases a framework for inferring semantically annotated glycemic events on the patient, which leverages data from mobile wearable sensors deployed on a sport-belt. This work is part of the D1namo project for non-invasive diabetes monitoring, and focuses on the representation and query processing of the data produced by the wearable sensors, using semantic technologies and vocabularies that extend existing Web standards. Furthermore, this work shows how different layers of data, from raw measurements to complex events can be represented and linked in this framework, and experimental evidence is provided of how these layers can be efficiently exploited using an RDF Stream Processing engine.