  In this paper we extract the topology of the semantic space in its
encyclopedic acception, measuring the semantic flow between the different
entries of the largest modern encyclopedia, Wikipedia, and thus creating a
directed complex network of semantic flows. Notably at the percolation
threshold the semantic space is characterised by scale-free behaviour at
different levels of complexity and this relates the semantic space to a wide
range of biological, social and linguistics phenomena. In particular we find
that the cluster size distribution, representing the size of different semantic
areas, is scale-free. Moreover the topology of the resulting semantic space is
scale-free in the connectivity distribution and displays small-world
properties. However its statistical properties do not allow a classical
interpretation via a generative model based on a simple multiplicative process.
After giving a detailed description and interpretation of the topological
properties of the semantic space, we introduce a stochastic model of
content-based network, based on a copy and mutation algorithm and on the Heaps'
law, that is able to capture the main statistical properties of the analysed
semantic space, including the Zipf's law for the word frequency distribution.
