Incremental, inconsistency-resilient reasoning over Description Logic Abox streams

TL;DR

This paper introduces new semantics and algorithms for real-time, incremental reasoning over streaming Description Logic ABoxes, effectively handling high data velocity, inconsistency, and volatility in stream data.

Contribution

It proposes novel semantics for incremental reasoning and inconsistency repair over Description Logic streams, along with semi-naive algorithms for efficient materialization maintenance.

Findings

Incremental semantics enable real-time reasoning over data streams.

Novel inconsistency repair semantics improve robustness to noisy data.

Algorithms efficiently maintain reasoning results in volatile streaming environments.

Abstract

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.