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It studies being or existence and its basic categories and relationships, to determine
what entities and what type of entities exist. At the more specific level of knowl-
edge representation and knowledge management, ontologies identify ideas as con-
cepts applied to specific domains and organized as graphs via relationship links. A
typical example of an ontology as shown in Figure 2 is given by an automotive
ontology, organizing concepts used by enterprises operating in the automotive
industry.
Fig. 2. An automotive ontology
Our view both of ontologies and of communities is information-driven: they are
identified with the information they contain, either because they produce it (in the
case of communities) or because they categorize it (in the case of ontologies). Fur-
thermore, both communities and ontologies can be represented as networks (di-
rected graphs). This common formal representation makes it easy to model the in-
teraction between the two levels. Indeed, as a very important caveat , it should be
made clear that the notion of community that we adopt here not only assumes net-
works as a form of representation, but is itself a specialization of the notion of net-
work. In fact, we adhere to the view, coming from the tradition of network theory,
that a community can be defined in topological terms as a region of a dynamic
network where links are denser than in the surrounding regions. In other words,
communities are directly identified with highly interconnected regions of dynamic
networks, as shown in Figure 3. This allows us to model as communities social
networks whose nodes map directly into human individuals, such as family clans,
but also digital communities where the role of humans is crucial but indirect, in that
the primary community members are Web sites pointing one to the other. As we
shall discuss later on, the most immediate applications for our cognitive prosthesis
supporting abstraction capabilities in collective intelligences are indeed in the do-
main of this kind of Web communities.
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