Evolutionary and Swarm Computing for the Semantic Web

The Semantic Web has become a dynamic and enormous network of typed links between data sets stored on different machines. These data sets are machine readable and unambiguously interpretable, thanks to their underlying standard representation languages. The expressiveness and flexibility of the publication model of Linked Data has led to its widespread adoption and an ever increasing publication of semantically rich data on the Web. This success however 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 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 argue that the existing approaches to address their underlying reasoning tasks consistently fail because of the increasing size, dynamicity and complexity of the data. For each of these primitive reasoning tasks we will discuss possible problem solving methods grounded in Evolutionary and Swarm computing, with short descriptions of existing approaches. Finally, we will 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.