Reactive multi-context systems: Heterogeneous reasoning in dynamic environments

作者:

摘要

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

论文关键词:Heterogeneous knowledge,Stream reasoning,Knowledge integration,Reactive systems,Dynamic systems

论文评审过程:Received 9 December 2016, Revised 23 November 2017, Accepted 24 November 2017, Available online 5 December 2017, Version of Record 7 December 2017.

论文官网地址:https://doi.org/10.1016/j.artint.2017.11.007