Skip to Main content Skip to Navigation
Theses

Inconsistency Handling in Ontology-Mediated Query Answering

Abstract : The problem of querying description logic knowledge bases using database-style queries (in particular, conjunctive queries) has been a major focus of recent description logic research. An important issue that arises in this context is how to handle the case in which the data is inconsistent with the ontology. Indeed, since in classical logic an inconsistent logical theory implies every formula, inconsistency-tolerant semantics are needed to obtain meaningful answers. This thesis aims to develop methods for dealing with inconsistent description logic knowledge bases using three natural semantics (AR, IAR, and brave) previously proposed in the literature and that rely on the notion of a repair, which is an inclusion-maximal subset of the data consistent with the ontology. In our framework, these three semantics are used conjointly to identify answers with different levels of confidence. In addition to developing efficient algorithms for query answering over inconsistent DL-Lite knowledge bases, we address three problems that should support the adoption of this framework: (i) query result explanation, to help the user to understand why a given answer was (not) obtained under one of the three semantics, (ii) query-driven repairing, to exploit user feedback about errors or omissions in the query results to improve the data quality, and (iii) preferred repair semantics, to take into account the reliability of the data. For each of these three topics, we developed a formal framework, analyzed the complexity of the relevant reasoning problems, and proposed and implemented algorithms, which we empirically studied over an inconsistent DL-Lite benchmark we built. Our results indicate that even if the problems related to dealing with inconsistent DL-Lite knowledge bases are theoretically hard, they can often be solved efficiently in practice by using tractable approximations and features of modern SAT solvers.
Complete list of metadata

Cited literature [103 references]  Display  Hide  Download

https://tel.archives-ouvertes.fr/tel-01378723
Contributor : Abes Star :  Contact
Submitted on : Monday, October 10, 2016 - 4:42:08 PM
Last modification on : Thursday, July 8, 2021 - 3:47:00 AM
Long-term archiving on: : Saturday, February 4, 2017 - 1:27:59 AM

File

70011_BOURGAUX_2016_diffusion....
Version validated by the jury (STAR)

Identifiers

  • HAL Id : tel-01378723, version 1

Citation

Camille Bourgaux. Inconsistency Handling in Ontology-Mediated Query Answering. Artificial Intelligence [cs.AI]. Université Paris Saclay (COmUE), 2016. English. ⟨NNT : 2016SACLS292⟩. ⟨tel-01378723⟩

Share

Metrics

Record views

1375

Files downloads

874