Benefits from structured knowledge representation have motivated the creation of large description logic ontologies. For accessing implicit information and avoiding errors in ontologies, reasoning services are necessary. However, the available reasoning methods suffer from scalability problems as the size of ontologies keeps growing.
This thesis investigates a distributed reasoning method that improves scalability by splitting a reasoning process into a set of largely independent subprocesses. In contrast to most description logic reasoners, the proposed approach is based on resolution calculi. We prove that the method is sound and complete for first order logic and different description logic subsets. Evaluation of the implementation shows a heavy decrease of runtime compared to reasoning on a single machine. Hence, the increased computation power pays off the overhead caused by distribution. Dependencies between subprocesses can be kept low enough to allow efficient distribution.
Furthermore, we investigate and compare different algorithms for computing the distribution of axioms and provide an optimization of the distributed reasoning method that improves workload balance in a dynamic setting
