Cleaning Inconsistent Data in Temporal DL-Lite Under Best Repair
  Semantics
  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.
