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

arXiv: https://arxiv.org/abs/2007.00757 · HTML: https://mlsanity.com/paper/2007.00757/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2020.

## Built on by (most cited first, 9 of 9)

- [Signature-Based Abduction with Fresh Individuals and Complex Concepts for Description Logics (Extended Version)](https://mlsanity.com/paper/2105.00274/) (2021)
- [Connection-minimal Abduction in EL via Translation to FOL -- Technical Report](https://mlsanity.com/paper/2205.08449/) (2022)
- [Dual Forgetting Operators in the Context of Weakest Sufficient and Strongest Necessary Conditions](https://mlsanity.com/paper/2305.07233/) (2023)
- [Can You Tell the Difference? Contrastive Explanations for ABox Entailments](https://mlsanity.com/paper/2511.11281/) (2025)
- [ABox Abduction for Inconsistent Knowledge Bases under Repair Semantics](https://mlsanity.com/paper/2605.01341/) (2026)
- [Optimal Alignment of Temporal Knowledge Bases](https://mlsanity.com/paper/2307.15439/) (2023)
- [Why Not? Explaining Missing Entailments with Evee (Technical Report)](https://mlsanity.com/paper/2308.07294/) (2023)
- [Interpolation in Knowledge Representation](https://mlsanity.com/paper/2512.08833/) (2025)
- [NeurOWL: An LLM-Based Neural-symbolic Framework for Incomplete OWL Ontology Reasoning](https://mlsanity.com/paper/2607.15776/) (2026)

## Abstract (arXiv)

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.