  Applications like personal assistants need to be aware ofthe user's context,
e.g., where they are, what they are doing, and with whom. Context information
is usually inferred from sensor data, like GPS sensors and accelerometers on
the user's smartphone. This prediction task is known as context recognition. A
well-defined context model is fundamental for successful recognition. Existing
models, however, have two major limitations. First, they focus on few aspects,
like location or activity, meaning that recognition methods based onthem can
only compute and leverage few inter-aspect correlations. Second, existing
models typically assume that context is objective, whereas in most applications
context is best viewed from the user's perspective. Neglecting these factors
limits the usefulness of the context model and hinders recognition. We present
a novel ontological context model that captures five dimensions, namely time,
location, activity, social relations and object. Moreover, our model defines
three levels of description(objective context, machine context and subjective
context) that naturally support subjective annotations and reasoning.An initial
context recognition experiment on real-world data hints at the promise of our
model.
