  Knowledge computation tasks are often infeasible for large data sets. This is
in particular true when deriving knowledge bases in formal concept analysis
(FCA). Hence, it is essential to come up with techniques to cope with this
problem. Many successful methods are based on random processes to reduce the
size of the investigated data set. This, however, makes them hardly
interpretable with respect to the discovered knowledge. Other approaches
restrict themselves to highly supported subsets and omit rare and interesting
patterns. An essentially different approach is used in network science, called
$k$-cores. These are able to reflect rare patterns if they are well connected
in the data set. In this work, we study $k$-cores in the realm of FCA by
exploiting the natural correspondence to bi-partite graphs. This structurally
motivated approach leads to a comprehensible extraction of knowledge cores from
large formal contexts data sets.
