  Ontologies formalise how the concepts from a given domain are interrelated.
Despite their clear potential as a backbone for explainable AI, existing
ontologies tend to be highly incomplete, which acts as a significant barrier to
their more widespread adoption. To mitigate this issue, we present a mechanism
to infer plausible missing knowledge, which relies on reasoning by analogy. To
the best of our knowledge, this is the first paper that studies analogical
reasoning within the setting of description logic ontologies. After showing
that the standard formalisation of analogical proportion has important
limitations in this setting, we introduce an alternative semantics based on
bijective mappings between sets of features. We then analyse the properties of
analogies under the proposed semantics, and show among others how it enables
two plausible inference patterns: rule translation and rule extrapolation.
