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Agent-based simulations are an effective simulation technique that can flexibly be applied to real-world business problems. By integrating such simulations into business games, they become a widely accepted educational instrument in the context of business training. Not only can they be used to train standard behaviour in training scenarios but they can also be used for open experimentation to discover structure in complex contexts (e.g. complex adaptive systems) and to verify behaviours that have been predicted on the basis of theoretical considerations. Traditional modelling techniques are built on mathematical models consisting of differential or difference equations (e.g. the well-known system dynamics approach). However, individual behaviour is not visible in these equations. This problem is addressed by using software agents to simulate individuals and to model their actions in response to external stimuli. To be effective, business training tools have to provide sufficiently realistic models of real-world aspects. Ideally, system effects on a macroscopic level are caused by behaviour of system components on a more microscopic level. For instance, in modelling market mechanisms market participants can explicitly be modelled as agents with individual behaviour and personal goals. Agents can communicate and act on the basis of what they know and which communication acts they perform. The evolution of the market then depends on the actions of the participants directly and not on abstract mathematical expressions. Generally, agent-based modelling is a challenging task, when modelling knowledge and behaviour. With the rise of the so-called semantic web ontologies have become popular, allowing the representation of knowledge using standardised formal languages which can be made available to agents acting in a simulation. However, the combination of agent-based systems with ontologies has not yet been researched sufficiently, because both concepts (web ontology languages and agent oriented programming languages) have been developed independently and the link has not yet been built adequately. Using ontologies as a knowledge base allows access to powerful standardised inference engines that offer leverage for the decision process of the agent. Agents can then determine their actions in accordance with this knowledge. To model agents using ontologies creates a new perspective for multi-agent simulation scenarios as programming details are reduced and a separation of modelling aspects from coding details is promising as business simulation scenarios can be set up with a reduced development effort. This thesis focuses on how ontologies can be integrated utilising the agent framework Jadex. A basic architecture with layered ontologies and its integration into the belief-desire-intention (BDI) agent model is presented. The abstract level of the approach guarantees applicability to different simulation scenarios which can be modelled by creating appropriate ontologies. Examples are based upon the simulation of market mechanisms within the context of different industries. The approach is implemented in the integrated simulation environment AGADE which incorporates agent-based and semantic technologies. Simulations for different scenarios that model typical market scenarios are presented.