  Description logics (DLs) are standard knowledge representation languages for
modelling ontologies, i.e. knowledge about concepts and the relations between
them. Unfortunately, DL ontologies are difficult to learn from data and
time-consuming to encode manually. As a result, ontologies for broad domains
are almost inevitably incomplete. In recent years, several data-driven
approaches have been proposed for automatically extending such ontologies. One
family of methods rely on characterizations of concepts that are derived from
text descriptions. While such characterizations do not capture ontological
knowledge directly, they encode information about the similarity between
different concepts, which can be exploited for filling in the gaps in existing
ontologies. To this end, several inductive inference mechanisms have already
been proposed, but these have been defined and used in a heuristic fashion. In
this paper, we instead propose an inductive inference mechanism which is based
on a clear model-theoretic semantics, and can thus be tightly integrated with
standard deductive reasoning. We particularly focus on interpolation, a
powerful commonsense reasoning mechanism which is closely related to cognitive
models of category-based induction. Apart from the formalization of the
underlying semantics, as our main technical contribution we provide
computational complexity bounds for reasoning in EL with this interpolation
mechanism.
