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Inconsistency-Tolerant Semantics Based on (Preferred) Repairs (Invited Paper)

Author Camille Bourgaux

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  • Theory of computation → Automated reasoning
  • Computing methodologies → Description logics
  • Computing methodologies → Nonmonotonic, default reasoning and belief revision
  • Computing methodologies → Reasoning about belief and knowledge
  • Information systems → Data cleaning
Keywords
  • Knowledge bases
  • databases
  • inconsistency handling
  • repairs
  • preferences

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Abstract

Real-world datasets are plagued by data quality issues which may render the data inconsistent w.r.t. a set of constraints, be they given by database integrity constraints or ontologies. A prominent way to handle such inconsistent data is to use inconsistency-tolerant semantics to obtain meaningful answers to queries. Most of these semantics are based on some notion of repairs, which represent ways of restoring the data consistency. The most basic kind of repairs is that of subset repairs, which are maximal consistent subsets of the dataset. However, in many scenarios, one can define preferred repairs based on some preference information. These lecture notes present inconsistency-tolerant semantics, focusing on the repair-based ones, then review different kinds of preferred repairs that have been considered in the literature. We present in particular the relationships between different kinds of preferred repairs and other notions related to inconsistency handling, the computational complexity of reasoning with (preferred) repairs, and some implementations.

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Camille Bourgaux. Inconsistency-Tolerant Semantics Based on (Preferred) Repairs (Invited Paper). In Joint Proceedings of the 20th and 21st Reasoning Web Summer Schools (RW 2024 & RW 2025). Open Access Series in Informatics (OASIcs), Volume 138, pp. 5:1-5:67, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/OASIcs.RW.2024/2025.5

Author Details

Camille Bourgaux
  • DI ENS, ENS, CNRS, PSL University & Inria, Paris, France

Funding

This lecture was supported by the ANR AI Chair INTENDED (ANR-19-CHIA-0014).

Acknowledgements

I am grateful to the RW 2025 organizers for the invitation to give this lecture. Several sections of these lecture notes are based on some work on priority-based repairs that I co-authored with Meghyn Bienvenu.

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