Techniques for optimized reasoning in description logic knowledge bases

  • One of the main goals of the artificial intelligence community is to create machines able to reason with dynamically changing knowledge. To achieve this goal, a multitude of different problems have to be solved, of which many have been addressed in the various sub-disciplines of artificial intelligence, like automated reasoning and machine learning. The thesis at hand focuses on the automated reasoning aspects of these problems and address two of the problems which have to be overcome to reach the afore-mentioned goal, namely 1. the fact that reasoning in logical knowledge bases is intractable and 2. the fact that applying changes to formalized knowledge can easily introduce inconsistencies, which leads to unwanted results in most scenarios. To ease the intractability of logical reasoning, I suggest to adapt a technique called knowledge compilation, known from propositional logic, to description logic knowledge bases. The basic idea of this technique is to compile the given knowledge base into a normal form which allows to answer queries efficiently. This compilation step is very expensive but has to be performed only once and as soon as the result of this step is used to answer many queries, the expensive compilation step gets worthwhile. In the thesis at hand, I develop a normal form, called linkless normal form, suitable for knowledge compilation for description logic knowledge bases. From a computational point of view, the linkless normal form has very nice properties which are introduced in this thesis. For the second problem, I focus on changes occurring on the instance level of description logic knowledge bases. I introduce three change operators interesting for these knowledge bases, namely deletion and insertion of assertions as well as repair of inconsistent instance bases. These change operators are defined such that in all three cases, the resulting knowledge base is ensured to be consistent and changes performed to the knowledge base are minimal. This allows us to preserve as much of the original knowledge base as possible. Furthermore, I show how these changes can be applied by using a transformation of the knowledge base. For both issues I suggest to adapt techniques successfully used in other logics to get promising methods for description logic knowledge bases.

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Author:Claudia Schon
Referee:Ulrich Furbach, Steffen Staab
Advisor:Ulrich Furbach
Document Type:Doctoral Thesis
Date of completion:2016/06/28
Date of publication:2016/06/30
Publishing institution:Universität Koblenz, Universitätsbibliothek
Granting institution:Universität Koblenz, Fachbereich 4
Date of final exam:2016/06/15
Release Date:2016/06/30
Tag:description logic; reasoning
Number of pages:xv, 202
Institutes:Fachbereich 4 / Institut für Informatik
BKL-Classification:54 Informatik
Licence (German):License LogoEs gilt das deutsche Urheberrecht: § 53 UrhG