RaDON - Repair and Diagnosis in Ontology Networks

One of the major challenges in managing networked and dynamic ontologies is to handle inconsistencies in single ontologies, and inconsistencies introduced by integrating multiple distributed ontologies. Our RaDON system provides functionalities to repair and diagnose ontology networks by extending the capabilities of existing reasoners. The system integrates several new debugging and repairing algorithms, such as a relevance-directed algorithm to meet the various needs of the users

Ontological Analysis For Description Logics Knowledge Base Debugging

International audienceFormal ontology provides axiomatizations of domain independent principles which, among other applications,can be used to identify modeling errors within a knowledge base. The Ontoclean methodology is probably the best-known illustration of this strategy, but its cost in terms of manual work is often considered dissuasive. This article investigates the applicability of such debugging strategies to Description Logics knowledge bases, showing that even a partial and shallow analysis rapidly performed with a top-level ontology can reveal the presence of violations of common sense, and that the bottleneck, if there is one, may instead reside in the resolution of the resulting inconsistency or incoherence

Trimming a consistent OWL knowledge base, relying on linguistic evidence

International audienceIntuitively absurd but logically consistent sets of statements are common in publicly available OWL datasets. This article proposes an original and fully automated method to point at erroneous axioms in a consistent OWL knowledge base, by weakening it in order to improve its compliance with linguistic evidence gathered from natural language texts. A score for evaluating the compliance of subbases of the input knowledge base is proposed, as well as a trimming algorithm to discard potentially erroneous axioms. The whole approach is evaluated on two real datasets, with automatically retrieved web pages as a linguistic input

Distributional semantics for ontology verification

International audienceAs they grow in size, OWL ontologies tend to comprise intuitively incompatible statements,even when they remain logically consistent. This is true in particular of lightweight ontologies, especially the ones which aggregate knowledge from different sources. The article investigates how distributional semantics can help detect and repair violation of common sense in consistent ontologies, based on the identification of consequences which are unlikely to hold if the rest of the ontology does. A score evaluating the plausibility for a consequence to hold with regard to distributional evidence is defined, as well as several methods in order to decide which statements should be preferably amended or discarded. A conclusive evaluation is also provided, which consists in extending an input ontology with randomly generated statements, before trying to discard them automatically

Revising Description Logic Terminologies to Handle Exceptions: a First Step

Abstract. We propose a methodology to revise a Description Logic knowledge base when detecting exceptions. Our approach relies on the methodology for debugging a Description Logic terminology, addressing the problem of diagnosing incoherent ontologies by identifying a mini-mal subset of axioms responsible for an inconsistency. In the approach we propose, once the source of the inconsistency has been localized, the identified axioms are revised in order to obtain a consistent knowledge base including the detected exception. To this aim, we make use of a non-monotonic extension of the Description Logic ALC based on the com-bination of a typicality operator and the well established nonmonotonic mechanism of rational closure, which allows to deal with prototypical properties and defeasible inheritance.

Axiom Pinpointing in General Tableaux

Axiom pinpointing has been introduced in description logics (DLs) to help the user to understand the reasons why consequences hold and to remove unwanted consequences by computing minimal (maximal) subsets of the knowledge base that have (do not have) the consequence in question. The pinpointing algorithms described in the DL literature are obtained as extensions of the standard tableau-based reasoning algorithms for computing consequences from DL knowledge bases. Although these extensions are based on similar ideas, they are all introduced for a particular tableau-based algorithm for a particular DL. The purpose of this paper is to develop a general approach for extending a tableau-based algorithm to a pinpointing algorithm. This approach is based on a general definition of „tableaux algorithms,' which captures many of the known tableau-based algorithms employed in DLs, but also other kinds of reasoning procedures