We are facing an unforeseen growth of the complexity of (data, content and) knowledge. Here we talk of complexity meaning the size, the sheer numbers, the spatial and temporal pervasiveness of knowledge, and the unpredictable dynamics of knowledge change, unknown at design time but also at run time. The obvious example is the Web and all the material, also multimedia, which is continuously made available on line.

Our goal in this talk is to propose a novel approach which deals with this level of complexity and that, hopefully, will overcome some of the scalability issues shown by the existing data and knowledge representation technology. The key idea is to propose a bottom-up approach where diversity is considered as a feature which must be maintained and exploited and not as a defect that must be absorbed in some general schema. The proposed solution amounts to making a paradigm shift from the view where knowledge is mainly assembled by combining basic building blocks to a view where new knowledge is obtained by the design or run-time adaptation of existing knowledge. Typically, we will build knowledge on top of a landscape of existing highly interconnected knowledge parts. Knowledge will no longer be produced ab initio, but more and more as adaptations of other, existing knowledge parts, often performed in runtime as a result of a process of evolution. This process will not always be controlled or planned externally but induced by changes perceived in the environment in which systems are embedded. The challenge is to develop design methods and tools that enable effective design by harnessing, controlling and using the effects of emergent knowledge properties.  

We will present and discuss the ideas above considering, as an example, the use of ontologies  in the formalization of knowledge (for instance in the SemanticWeb).