Collective Knowledge Systems are information systems that utilize the knowledge made available through Social Media by using Semantic Web technology. The current state of the Social Web, comprising all the content made available through regular user participation, can best be described as “collected intelligence,” i.e., value mainly ensues from this knowledge being available rather than directly usable in a computational context. The potential of collective intelligence is powerfully illustrated in nature by, for example, the behaviour of social insects. Some of these evolutionary adaptations have been successfully abstracted and captured in computational systems, such as Ant Colony Optimization, amongst many others. Referring to collective intelligence in the context of the human cyberspace, Pierre Levy synthesised the latter as “enabling members of delocalized communities to interact within a mobile landscape of signification,” stressing that “the greater the number of collective intellects with which an individual is involved, the more opportunities he has to diversify his knowledge and desire.” His vision on collective intelligence of the sort emerging in cyberspace expands to dramatically alter the course of development of (knowledge) economies. What remains after we have mechanized agriculture, industry and messaging technologies? The economy will centre, as it does already, on that which can never be fully automated, on that which is irreducible: the production of the social bond, the relational. Those who manufacture things will become scarcer and scarcer, and their labour will become mechanized, augmented, automated to a greater and greater extent. The final frontier will be the human itself, that which can’t be automated: the creation of sensible worlds, invention, relation, the continuous recreation of the community. What kind of engineering will best meet the needs of a growing economy of human qualities? While it may be argued that systems such as Wikipedia (and its more machine-friendly version -- DBpedia), Twitter, and Google are systems that exhibit characteristics of Collective Knowledge Systems, we argue that none of these systems fully adheres to the functionality originally specified in: next to the collective knowledge stored by the systems, i.e., collected knowledge, the system should be able to “create new value from the collected data.” It is here that the vast amounts of content on the Social Web can be morphed from a universe of unstructured data into knowledge that is available in a computational context. And it is here that Semantic Web technologies, through their ability to infer new knowledge from known facts, can crucially contribute
