To model contexts and provide inference mechanism in Semantic Web of Things (SWoT), a generic and extensible meta-context ontology model (MCOnt) is proposed to model the common semantics to all dimensions of an information space. It can provide not only high-level meta-contexts which are used to capture basic context concepts, but also extensible domain-specific contexts in a hierarchical manner. Meanwhile, to adapt to dynamic and uncertain contexts in SWoT, an combined inference algorithm with context and Dynamic Bayesian Networks (DBNConU) is proposed.