Scalable reasoning for the Semantic Web is a crucial issue. Without scalability the Semantic Web will not be able to reason about the high and growing amount of data with respect to time performance and tolerant reasoning. Approximate reasoning seems to be a promising approach to introduce scalability to the Semantic Web.

Different forms of approximated reasoning are possible. First the reasoning process itself can be approximated by replacing the inference engine by a sophisticated approximated one. Second the knowledge, i.e. the ontology, can be weakened or, third, translated into another representation formalism. Obviously during both transformations knowledge is lost.

This talk reports about the investigation and experiences made in KNOWLEDGEWEB, an EU-funded network of excellence. The logical foundation of some approaches is briefly explained but also their practical consequences for scalable reasoning will be investigated.

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