Master Thesis

Semantic Web reasoning systems are confronted with the task to process growing amounts of distributed, dynamic resources. This thesis presents a novel way of approaching the challenge by RDF graph traversal, exploiting the advantages of swarm intelligence. The nature-inspired and index-free methodology is realised by self-organising swarms of autonomous, light-weight entities that traverse RDF graphs by following paths, aiming to instantiate pattern-based inference rules. The method is evaluated on the basis of a series of simulation experiments with respect to desirable properties of Semantic Web reasoning, focussing on anytime behaviour, adaptiveness and scaleability.