Managed multi-context systems (mMCSs) allow for the integration ofheterogeneous knowledge sources in a modular and very general way. They were,however, mainly designed for static scenarios and are therefore not well-suitedfor dynamic environments in which continuous reasoning over such heterogeneousknowledge with constantly arriving streams of data is necessary. In this paper,we introduce reactive multi-context systems (rMCSs), a framework for reactivereasoning in the presence of heterogeneous knowledge sources and data streams.We show that rMCSs are indeed well-suited for this purpose by illustrating howseveral typical problems arising in the context of stream reasoning can behandled using them, by showing how inconsistencies possibly occurring in theintegration of multiple knowledge sources can be handled, and by arguing thatthe potential non-determinism of rMCSs can be avoided if needed using analternative, more skeptical well-founded semantics instead with beneficialcomputational properties. We also investigate the computational complexity ofvarious reasoning problems related to rMCSs. Finally, we discuss related work,and show that rMCSs do not only generalize mMCSs to dynamic settings, but alsocapture/extend relevant approaches w.r.t. dynamics in knowledge representationand stream reasoning.
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