基于最佳修复语义的时态DL-Lite不一致数据清理
Cleaning Inconsistent Data in Temporal DL-Lite Under Best Repair Semantics
摘要：
在本文中，我们解决的问题，处理不一致的数据在时态描述逻辑（TDL）知识库。考虑到知识库的数据部分作为随着时间的推移不一致的来源，我们提出了一个ABox修复方法。这是在TDL知识库中处理修复的第一项工作。要做到这一点，我们的目标是双重的：1）检测时间不一致性和2）提出一个数据的时间修复。对于不一致性检测，我们提出了一种减少的方法，从TDL DL DL，它允许提供一个紧密的NP-完全的上限TDL概念可满足性，并使用高度优化的DL推理机，可以带来精确的解释（不一致的数据断言集）。此后，根据获得的解释，我们提出了一种基于允许的刚性谓词和断言的时间顺序自动计算时间设置中的最佳修复的方法。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
