Incremental, inconsistency-resilient reasoning over Description Logic Abox streams
This work addresses real-time stream reasoning for applications dealing with volatile data, but it is incremental as it builds on existing semantics and algorithms.
The paper tackles the challenges of reasoning over high-velocity, noisy Description Logic ABox streams by proposing novel incremental semantics for sliding windows and inconsistency repair, enabling real-time materialization maintenance.
More and more, data is being produced in a streaming fashion. This has led to increased interest into how actionable insights can be extracted in real time from data streams through Stream Reasoning. Reasoning over data streams raises multiple challenges, notably the high velocity of data, the real time requirement of the reasoning, and the noisy and volatile nature of streams. This paper proposes novel semantics for incremental reasoning over streams of Description Logic ABoxes, in order to tackle these challenges. To address the first two challenges, our semantics for reasoning over sliding windows on streams allow for incrementally computing the materialization of the window based on the materialization of the previous window. Furthermore, to deal with the volatile nature of streams, we present novel semantics for inconsistency repair on such windows, based on preferred repair semantics. We then detail our proposed semi-naive algorithms for incremental materialization maintenance in the case of OWL2 RL, both in the presence of inconsistencies and without.