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Impact Analysis in Description Logic Ontologies

Goncalves, Joao Rafael Landeiro De sousa

[Thesis]. Manchester, UK: The University of Manchester; 2014.

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Abstract

With the growing popularity of the Web Ontology Language (OWL) as a logic-based ontology language, as well as advancements in the language itself, the need for more sophisticated and up-to-date ontology engineering services increases as well. While, for instance, there is active focus on new reasoners and optimisations, other services fall short of advancing at the same rate (it suffices to compare the number of freely-available reasoners with ontology editors). In particular, very little is understood about how ontologies evolve over time, and how reasoners’ performance varies as the input changes.Given the evolving nature of ontologies, detecting and presenting changes (via a so-called diff) between them is an essential engineering service, especially for version control systems or to support change analysis. In this thesis we address the diff problem for description logic (DL) based ontologies, specifically OWL 2 DL ontologies based on the SROIQ DL. The outcomes are novel algorithms employing both syntactic and semantic techniques to, firstly, detect axiom changes, and what terms had their meaning affected between ontologies, secondly, categorise their impact (for example, determining that an axiom is a stronger version of another), and finally, align changes appropriately, i.e., align source and target of axiom changes (so the stronger axiom with the weaker one, from our example), and axioms with the terms they affect.Subsequently, we present a theory of reasoner performance heterogeneity, based on field observations related to reasoner performance variability phenomena. Our hypothesis is that there exist two kinds of performance behaviour: an ontology/reasoner combination can be performance-homogeneous or performance-heterogeneous. Finally, we verify that performance-heterogeneous reasoner/ontology combinations contain small, performance-degrading sets of axioms, which we call hot spots. We devise a performance hot spot finding technique, and show that hot spots provide a promising basis for engineering efficient reasoners.