Signature-Based Abduction for Expressive Description Logics -- Technical Report
This solves a knowledge base abduction problem useful for tasks like diagnosis, where vocabulary mismatches occur, but it is incremental as it extends existing abduction methods to a more expressive logic.
The paper tackles the problem of signature-based abduction for expressive description logics, presenting the first complete method for ALC that computes a finite and complete set of hypotheses, evaluated on realistic knowledge bases.
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.