2 research outputs found
Debugging and repair of description logic ontologies.
Thesis (M.Sc.)-University of KwaZulu-Natal, Westville, 2010.In logic-based Knowledge Representation and Reasoning (KRR), ontologies are used to
represent knowledge about a particular domain of interest in a precise way. The building
blocks of ontologies include concepts, relations and objects. Those can be combined to
form logical sentences which explicitly describe the domain. With this explicit knowledge
one can perform reasoning to derive knowledge that is implicit in the ontology. Description
Logics (DLs) are a group of knowledge representation languages with such capabilities that
are suitable to represent ontologies. The process of building ontologies has been greatly
simpli ed with the advent of graphical ontology editors such as SWOOP, Prote ge and
OntoStudio. The result of this is that there are a growing number of ontology engineers
attempting to build and develop ontologies. It is frequently the case that errors are
introduced while constructing the ontology resulting in undesirable pieces of implicit
knowledge that follows from the ontology. As such there is a need to extend current
ontology editors with tool support to aid these ontology engineers in correctly designing
and debugging their ontologies. Errors such as unsatis able concepts and inconsistent
ontologies frequently occur during ontology construction. Ontology Debugging and Repair
is concerned with helping the ontology developer to eliminate these errors from the ontology.
Much emphasis, in current tools, has been placed on giving explanations as to why these
errors occur in the ontology. Less emphasis has been placed on using this information to
suggest e cient ways to eliminate the errors. Furthermore, these tools focus mainly on the
errors of unsatis able concepts and inconsistent ontologies. In this dissertation we ll an
important gap in the area by contributing an alternative approach to ontology debugging
and repair for the more general error of a list of unwanted sentences. Errors such as
unsatis able concepts and inconsistent ontologies can be represented as unwanted sentences
in the ontology. Our approach not only considers the explanation of the unwanted sentences
but also the identi cation of repair strategies to eliminate these unwanted sentences from
the ontology
Practical reasoning for defeasable description logics.
Doctor of Philosophy in Mathematics, Statistics and Computer Science. University of KwaZulu-Natal, Durban 2016.Description Logics (DLs) are a family of logic-based languages for formalising
ontologies. They have useful computational properties allowing the development
of automated reasoning engines to infer implicit knowledge from
ontologies. However, classical DLs do not tolerate exceptions to speci ed
knowledge. This led to the prominent research area of nonmonotonic or defeasible
reasoning for DLs, where most techniques were adapted from seminal
works for propositional and rst-order logic.
Despite the topic's attention in the literature, there remains no consensus
on what \sensible" defeasible reasoning means for DLs. Furthermore, there
are solid foundations for several approaches and yet no serious implementations
and practical tools. In this thesis we address the aforementioned issues
in a broad sense. We identify the preferential approach, by Kraus, Lehmann
and Magidor (KLM) in propositional logic, as a suitable abstract framework
for de ning and studying the precepts of sensible defeasible reasoning.
We give a generalisation of KLM's precepts, and their arguments motivating
them, to the DL case. We also provide several preferential algorithms
for defeasible entailment in DLs; evaluate these algorithms, and the main
alternatives in the literature, against the agreed upon precepts; extensively
test the performance of these algorithms; and ultimately consolidate our implementation
in a software tool called Defeasible-Inference Platform (DIP).
We found some useful entailment regimes within the preferential context
that satisfy all the KLM properties, and some that have scalable performance
in real world ontologies even without extensive optimisation