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

TL;DR

This paper introduces a novel approach for detecting and repairing inconsistencies in Temporal Description Logic knowledge bases, leveraging reductions to DL and optimized reasoners for precise explanations and repairs.

Contribution

It is the first work to address data repair in TDL knowledge bases, providing methods for inconsistency detection and automatic best repair computation.

Findings

Reduction from TDL to DL enables NP-complete complexity bounds.

Use of DL reasoners yields precise explanations of inconsistencies.

Proposed repair method effectively resolves temporal data inconsistencies.

Abstract

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.