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Ph.D de

Ph.D
Group : Large-scale Heterogeneous DAta and Knowledge

Knowledge Extraction from Description Logic Terminologies

Starts on 01/10/2015
Advisor : DAGUE, Philippe

Funding : Bourse pour étudiant étranger
Affiliation : Université Paris-Sud
Laboratory : LRI - LaHDAK

Defended on 30/11/2018, committee :
Directeur de thèse :
- Philippe DAGUE, Université Paris-Sud

Encadrante :
- Yue MA, Université Paris-Sud

Rapporteurs :
- Marie-Christine ROUSSET, Laboratoire d'Informatique de Grenoble
- Thomas SCHNEIDER, University of Bremen

Examinateurs :
- Marie-Laure MUGNIER, LIRMM, Montpellier
- Anne VILNAT, Université Paris-Sud

Research activities :

Abstract :
An increasing number of large ontologies are being developed and made available, e.g., in repositories such as the NCBO Bioportal. Ensuring access to the knowledge contained in ontologies that is most relevant to users has been identified as an important challenge. In this work, we tackle this challenge by proposing three different approaches to extracting knowledge from Description Logic ontologies: extracting minimal ontology modules (i.e., sub-ontologies that are minimal w.r.t. set inclusion while still preserving all entailments over a given vocabulary); computing best ontology excerpts (a certain, small number of axioms that best capture the knowledge about the vocabulary while allowing for a degree of semantic loss); and determining projection modules (sub-ontologies of a target ontology that entails subsumption, instance or conjunctive queries that follow from a reference ontology). For each of these approaches, we are interested in extracting not only one but all instances of the module notion. For computing minimal modules and best excerpts, we introduce the notion of subsumption justification as a generalisation of the notion of a justification (a minimal set of axioms needed to preserve a given logical consequence) to capture the subsumption knowledge over the vocabulary. Similarly, for computing projection modules, we introduce the notion of projection justifications that preserve the answers to one of three query types as given by a reference ontology. Finally, we evaluate our approaches using a prototype implementation of the algorithms on large ontologies.

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