Šio magistrinio darbo tikslai yra: Išanalizuoti esančias ontologijas; Išnagrinėti ontologijų kūrimo principus bei taisykles; Aprašyti ontologijos kūrimo metodologiją; Panaudoti metodologiją praktiškai kuriant vaistų ontologiją, pasirinkus kūrimo įrankį; Atlikti esamų ontologijų tikrinimo priemonių analizę; Atrinkti tinkamiausią tikrinimo priemonę ontologijos teisingumo užtikrinimui. / Existing Ontologies analyse, creation of the Ontologies concepts and rools, creation methodology and it's usage with Protege tool. Ontologies reasoners comparison and finding the best reasoner.

Title: Reasoning in Description Logics Author: Mgr. Jaromír Malenko Department: Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague Supervisor: Prof. RNDr. Petr Štěpánek, DrSc.; Department of Theoretical Computer Science and Mathematical Logic, Faculty of Mathematics and Physics, Charles University in Prague Keywords: Description logic, Reasoner, Cartesian product, Non-monotonic reasoning Abstract: We deal with several aspects of reasoning in Description Logics. First, since description logic (DL) is a subset of First Order Logic (FOL), we use a FOL reasoner to reason in DL. We implemented dl2fol, a DL reasoner that takes an ontology (a DL theory with rules), translates it into a FOL theory, passes this set of formulae to an underling FOL reasoner, and interprets the result in terms of given ontology. This is an effective method for reasoning with newly introduced language constructors. However, we observed longer running times and that satisfiability of some DL concepts wasn't proved due to FOL undecidability. Second, we extend two DLs by introducing new language construct: cartesian product (CP) of concepts and roles. This allows for expressing relationships, that are not expressible by other means in weaker DLs. We...

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.

We propose in the paper a new solution to the so-called Logical Omniscience Problem of epistemic logic. Almost all attempts in the literature to solve this problem consist in weakening the standard epistemic systems: weaker systems are considered where the agents do not possess the full reasoning capacities of ideal reasoners. We shall argue that this solution is not satisfactory: in this way omniscience can be avoided, but many intuitions about the concepts of knowledge and belief get lost. We shall show that axioms for epistemic logics must have the following form: if the agent knows all premises of a valid inference rule, and if she thinks hard enough, then she will know the conclusion. To formalize such an idea, we propose to \dynamize' epistemic logic, that is, to introduce a dynamic component into the language. We develop a logic based on this idea and show that it is suitable for formalizing the notion of actual, or explicit knowledge.

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The inadequacy of the usual possible world semantics of modal languages when the meaning of 'belief' is attached to the modal operator is discussed. Three other approaches are then investigated. In the case of Moore's autoepistemic logic it becomes possible to compare an agent's beliefs to 'reality', which cannot be done directly in the possible world semantics. Levesque's semantics makes explicit in the object language the notion of 'this is all the information the agent has', which plays an important role in nonmonotonic reasoning. Both of these approaches deal with ideal reasoners. The third approach, Konolige's deduction model, is based on a semantics capable of describing the beliefs of one or more resourcebounded agents. Finally, the AGM postulates for belief revision are discussed. / Computer Science / M.Sc. (Computer Science)

Description Logics (DLs) belong to a successful family of knowledge representation formalisms with two key assets: formally well-defined semantics which allows to represent knowledge in an unambiguous way and automated reasoning which allows to infer implicit knowledge from the one given explicitly. This thesis investigates various reasoning techniques for tractable DLs in the EL family which have been implemented in the CEL system. It suggests that the use of the lightweight DLs, in which reasoning is tractable, is beneficial for ontology design and maintenance both in terms of expressivity and scalability. The claim is supported by a case study on the renown medical ontology SNOMED CT and extensive empirical evaluation on several large-scale biomedical ontologies.

The inadequacy of the usual possible world semantics of modal languages when the meaning of 'belief' is attached to the modal operator is discussed. Three other approaches are then investigated. In the case of Moore's autoepistemic logic it becomes possible to compare an agent's beliefs to 'reality', which cannot be done directly in the possible world semantics. Levesque's semantics makes explicit in the object language the notion of 'this is all the information the agent has', which plays an important role in nonmonotonic reasoning. Both of these approaches deal with ideal reasoners. The third approach, Konolige's deduction model, is based on a semantics capable of describing the beliefs of one or more resourcebounded agents. Finally, the AGM postulates for belief revision are discussed. / Computer Science / M.Sc. (Computer Science)

Description Logics (DLs) belong to a successful family of knowledge representation formalisms with two key assets: formally well-defined semantics which allows to represent knowledge in an unambiguous way and automated reasoning which allows to infer implicit knowledge from the one given explicitly. This thesis investigates various reasoning techniques for tractable DLs in the EL family which have been implemented in the CEL system. It suggests that the use of the lightweight DLs, in which reasoning is tractable, is beneficial for ontology design and maintenance both in terms of expressivity and scalability. The claim is supported by a case study on the renown medical ontology SNOMED CT and extensive empirical evaluation on several large-scale biomedical ontologies.

Reasoners can be used to derive implicit consequences from an ontology. Sometimes unwanted consequences are revealed, indicating errors or privacy-sensitive information, and the ontology needs to be appropriately repaired. The classical approach is to remove just enough axioms such that the unwanted consequences vanish. However, this is often too rough since mere axiom deletion also erases many other consequences that might actually be desired. The goal should not be to remove a minimal number of axioms but to modify the ontology such that only a minimal number of consequences is removed, including the unwanted ones. Specifically, a repair should rather be logically entailed by the input ontology, instead of being a subset. To this end, we introduce a framework for computing fixed-premise repairs of $\mathcal{EL}$ TBoxes. In the first variant the conclusions must be generalizations of those in the input TBox, while in the second variant no such restriction is imposed. In both variants, every repair is entailed by an optimal one and, up to equivalence, the set of all optimal repairs can be computed in exponential time. A prototypical implementation is provided. In addition, we show new complexity results regarding gentle repairs. / This is an extended version of an article accepted at the 45th German Conference on Artificial Intelligence (KI 2022).