In this paper, we address the problem of handling inconsistent data in Temporal Description Logic (TDL) knowledge bases. Considering the data part of the Knowledge Base as the source of inconsistency over time, we propose an ABox repair approach. This is the first work handling the repair in TDL Knowledge bases. To do so, our goal is twofold: 1) detect temporal inconsistencies and 2) propose a data temporal reparation. For the inconsistency detection, we propose a reduction approach from TDL to DL which allows to provide a tight NP-complete upper bound for TDL concept satisfiability and to use highly optimised DL reasoners that can bring precise explanation (the set of inconsistent data assertions). Thereafter, from the obtained explanation, we propose a method for automatically computing the best repair in the temporal setting based on the allowed rigid predicates and the time order of assertions.

翻译：在本文中,我们讨论了处理时间描述逻辑知识库中不一致数据的问题。考虑到知识库中的数据部分是长期不一致的来源,我们提议采用ABox修理方法。这是处理TDL知识库中不一致数据的第一个工作。为此,我们的目标是双重的:(1) 发现时间不一致,(2) 提出数据时间补偿。为了检测不一致,我们提议从TDL到DL的减少方法,为TDL概念的可适用性提供一个紧凑的NP-完整上限,并使用高度优化的DL解释器,以得出准确的解释(一套不一致的数据主张 ) 。 之后,我们从获得的解释中提出一种方法,根据允许的僵硬的前提和时间顺序,自动计算时间设置中的最佳修复。