  In this paper an ontological representation and reasoning paradigm has been
proposed for interpretation of time-series signals. The signals come from
sensors observing a smart environment. The signal chosen for the annotation
process is a set of unintuitive and complex gas sensor data. The ontology of
this paradigm is inspired form the SSN ontology (Semantic Sensor Network) and
used for representation of both the sensor data and the contextual information.
The interpretation process is mainly done by an incremental ASP solver which as
input receives a logic program that is generated from the contents of the
ontology. The contextual information together with high level domain knowledge
given in the ontology are used to infer explanations (answer sets) for changes
in the ambient air detected by the gas sensors.
