Within the last decade, many new approaches have been developed towards a shift from the "classic" web, which is designed primarily for human use, to a machine-processable web, which can be used by heterogenous systems to cooperate in an independent and scalable manner.

These approaches include new technologies for modeling semantic data to be commonly understood by applications such as data miners, web service clients, autonomous software agents, or reasoning tools, as well as new coordination models for a loose and scalable coupling of independent systems, varying from high-end servers to small embedded applications in PDAs, cell phones, or sensor nets.

Especially the combination of semantically modeled domain knowledge and common web services to become semantic web services seems to be a promising technology in the respect of an internet-scale integration model. They overcome the tightly coupled message exchange pattern which is used in classic Web Services, but use Tuple Spaces instead, which accompany theWordWideWeb's core paradigm of information exchange via persistent publication. As those technologies imply internet-scalability, it is essential to investigate how decentralized and fully distributed architectures can be realized without significant performance impacts. Observing natural societies like swarms, flocks, or hives, they seem to provide many of the desired characteristics, such as scalability, dynamism, failure tolerance and simplicity. Swarm strategies are already successfully used in the field  of  peer-to-peer networking and special cases of linear optimization. This thesis implements and evaluates swarm-based approaches for semantic Tuple Spaces, based on the previous work of Daniel Graff, who implemented a LINDA Tuple Space system called SwarmLinda. It extends this space with semantic triple clustering and retrieval by adapting common similarity measures for a distributed swarm-based architecture  and by developing research strategies inspired by ant routing algorithms.