提出增量推理框架，实时处理动态知识流中的不一致数据。
Incremental, inconsistency-resilient reasoning over Description Logic Abox streams
- 基于滑动窗口的增量计算，仅更新变化部分
- 支持在存在不一致时自动修复，保持结果一致性
- 适用于实时知识图谱更新，适合流式数据系统
越来越多的数据以流式方式产生，推动了对实时数据流进行流式推理的需求。然而，流式推理面临数据高速到达、实时性要求高以及数据噪声与波动性强等挑战。本文提出了描述逻辑ABox流上的增量推理新语义，通过滑动窗口机制实现前一窗口材料化结果的增量更新，显著降低计算开销。针对流数据的不稳定性，引入基于优选修复语义的不一致修复方法，确保结果在存在矛盾时仍具可解释性。论文进一步设计了半朴素算法，在无冲突与有冲突两种场景下均实现了对OWL2 RL知识库的增量材料化维护，有效支持了实时推理需求。
原文摘要 · Abstract (English)
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.
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