Approximations for Explanations of Inconsistency in Partially Known Multi-Context Systems ⋆

Abstract. Multi-context systems are a formalism to interlink decentralized and heterogeneous knowledge based systems (contexts), which interact via (possibly nonmonotonic) bridge rules. Inconsistency is a major problem, as it renders such systems useless. In addition, it is likely that complete knowledge about all system parts is unavailable and cannot be obtained, for instance in applications where confidentiality or trust are prohibitive. We therefore aim at explaining reasons for inconsistency in multi-context systems without having an omniscient view of the whole system. To this end we propose a representation for partial knowledge about contexts, and define over- and underapproximations for existing notions characterizing inconsistency in multi-context systems. Furthermore, we discuss query selection strategies for improving approximations in situations where a limited number of queries can be posed to a partially known context.