Ontology learning is one of the essential topics in the scope of an important area of current computer science and artificial intelligence - the upcoming Semantic Web. As the Semantic Web idea comprises semantically annotated descendant of the current world wide web and related tools and resources, the need of vast and reliable knowledge repositories is obvious. Ontologies present well defined, straightforward and standardised form of these repositories. There are many possible utilisations of ontologies - from automatic annotation of web resources to domain representation and reasoning tasks. However, the ontology creation process is very expensive, time-consuming and unobjective when performed manually. So a framework for automatic acquisition of ontologies would be very advantageous. In this work we present such a framework called OLE (an acronym for Ontology LEarning) and current results of its application. The main relevant topics, state of the art methods and techniques related to ontology acquisition are discussed as a part of theoretical background for the presentation of the OLE framework and respective results. Moreover, we describe also preliminary results of progressive research in the area of uncertain fuzzy ontology representation that will provide us with natural and reasonable instruments for dealing with inconsistencies in empiric data as well as for reasoning. Main future milestones of the ongoing research are debated as well.

The thesis deals with security hazards in distributed environments where traditional centralized approaches are only of limited serviceability. One of the very successful model for treating security and access management in distributed systems are so called reputation systems. The main goal of the rep- utation systems is to provide entities in the environment with mechanisms for inferring and building trust consequently used for access control. If the trust between two entities is high enough, transactions are likely to be allowed. The thesis proposes a new security model with trust management system for dynamic and distributed environments with huge number of entities. In dynamic systems new entities or relationships are likely to emerge or existing entities or relationships may often disappear. Such dynamics pose severe problems even for traditional reputation systems. Therefore our approach differs from the traditional ones in the way adopted for establishment and management of trust between entities in our point of view trust is not assigned to particular relationships but the trust is common for a group of entities. In this way, our proposal significantly enhances ability to infer trust between entities with no previous personal experiences with each other or in environments with huge number of entities. For the proposal differs in understanding of trust, it uses a hypergraph model for representation of system of entities. The security model proposed in the thesis contains two algorithms for transformation of a general input graph structure into hypergraph model, an algorithm treating dynamics of the distributed environment and a security subsystem. Our experimental implementation SecGrid utilizes proposed algorithms and it is used for experimental verification of the security models. The experiments investigate ability of the transformation algorithms; in details the dynamic part of our proposal together with the security subsystem proposed specially for the hypergraph model. Experiments show that our model overcame the traditional graph model in many ways especially in dynamic environments with huge amount of entities.