  In recent years, the world has witnessed various primitives pertaining to the
complexity of human behavior. Identifying an event in the presence of
insufficient, incomplete, or tentative premises along with the constraints on
resources such as time, data and memory is a vital aspect of an intelligent
system. Data explosion presents one of the most challenging research issues for
intelligent systems; to optimally represent and store this heterogeneous and
voluminous data semantically to provide human behavior. There is a requirement
of intelligent but personalized human behavior subject to constraints on
resources and priority of the user. Knowledge, when represented in the form of
an ontology, procures an intelligent response to a query posed by users; but it
does not offer content in accordance with the user context. To this aim, we
propose a model to quantify the user context and provide semantic contextual
reasoning. A diagnostic belief algorithm (DBA) is also presented that
identifies a given event and also computes the confidence of the decision as a
function of available resources, premises, exceptions, and desired specificity.
We conduct an empirical study in the domain of day-to-day routine queries and
the experimental results show that the answer to queries and also its
confidence varies with user context.

Artificial Intelligence