  Signature-based abduction aims at building hypotheses over a specified set of
names, the signature, that explain an observation relative to some background
knowledge. This type of abduction is useful for tasks such as diagnosis, where
the vocabulary used for observed symptoms differs from the vocabulary expected
to explain those symptoms. We present the first complete method solving
signature-based abduction for observations expressed in the expressive
description logic ALC, which can include TBox and ABox axioms, thereby solving
the knowledge base abduction problem. The method is guaranteed to compute a
finite and complete set of hypotheses, and is evaluated on a set of realistic
knowledge bases.
