Evolutionary and swarm computing for scaling up the semantic web

The success of the Semantic Web, with the ever increasing publication of machine readable semantically rich data on theWeb, has started to create serious problems as the scale and complexity of information outgrows the current methods in use, which are mostly based on database technology, expressive knowledge representation formalism and high-performance computing. We argue that methods from computational intelligence (CI) can play an important role in solving these problems. In this paper we introduce and systemically discuss the typical application problems on the Semantic Web and discuss CI alternative to address the limitations of their underlying reasoning tasks consistently with respect to the increasing size, dynamicity and complexity of the data. Finally, we discuss two case studies in which we successfully applied soft computing methods to two of the main reasoning tasks; an evolutionary approach to querying, and a swarm algorithm for entailment.