# Exploiting Uncertainty for Querying Inconsistent Description Logics Knowledge Bases

arXiv: https://arxiv.org/abs/2306.09138 · HTML: https://mlsanity.com/paper/2306.09138/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 93 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 4 of 4)

- [Efficient Knowledge Compilation Beyond Weighted Model Counting](https://mlsanity.com/paper/2205.07496/) (2022)
- [Efficient MUS Enumeration of Horn Formulae with Applications to Axiom Pinpointing](https://mlsanity.com/paper/1505.04365/) (2015)
- [Querying Inconsistent Prioritized Data with ORBITS: Algorithms, Implementation, and Experiments](https://mlsanity.com/paper/2202.07980/) (2022)
- [Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach](https://mlsanity.com/paper/1809.06180/) (2018)

## Abstract (arXiv)

The necessity to manage inconsistency in Description Logics Knowledge Bases (KBs) has come to the fore with the increasing importance gained by the Semantic Web, where information comes from different sources that constantly change their content and may contain contradictory descriptions when considered either alone or together. Classical reasoning algorithms do not handle inconsistent KBs, forcing the debugging of the KB in order to remove the inconsistency. In this paper, we exploit an existing probabilistic semantics called DISPONTE to overcome this problem and allow queries also in case of inconsistent KBs. We implemented our approach in the reasoners TRILL and BUNDLE and empirically tested the validity of our proposal. Moreover, we formally compare the presented approach to that of the repair semantics, one of the most established semantics when considering DL reasoning tasks.