Signature-Based Abduction for Expressive Description Logics -- Technical Report

TL;DR

This paper introduces the first complete method for signature-based abduction in expressive description logic ALC, enabling explanation generation over complex knowledge bases with guarantees of finiteness and completeness.

Contribution

It presents a novel, comprehensive approach to signature-based abduction in ALC, addressing a gap in existing methods and handling TBox and ABox axioms.

Findings

Method guarantees finite and complete hypothesis sets.

Evaluated successfully on realistic knowledge bases.

Advances abduction techniques in expressive description logics.

Abstract

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.