arXiv AI By Jan Novacek, Alexander Viehl, Oliver Bringmann, Wolfgang Rosenstiel

Reasoning-supported Robustness Validation of Automotive E/E Components

Read the original on arXiv AI →

arXiv:2608. 16421v1 Announce Type: new Abstract: This paper presents an ontology-supported approach to tackle the complexity of the Robustness Validation (RV) process of automotive electrical/electronic (E/E) components.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv AI
Sep 25

Ontology-Mediated Neurosymbolic Constraint Acquisition from Multiple Stakeholders

The paper introduces an architecture that bridges the gap between neural constraint sources and symbolic consumers by employing an OWL configuration ontology. It combines soft stakeholder preferences elicited by LLM assistants with hard hardware specifications, using description logic to detect unsatisfiability and produce symbolic explanations for interactive renegotiation. Remaining conflicts are addressed downstream through priority-based relaxation, demonstrated on a microgrid use case and positioned as broadly applicable to multi‑stakeholder domains.

By Stefan Bischof, Juliana Kainz, Danilo Valerio
arXiv AI
Jun 24

When CQs Go Wrong: Challenges in CQ Verification with OE-Assist

arXiv:2606. 24619v1 Announce Type: new Abstract: Competency Questions (CQs) are the central component of CQ-verification, an established process in which an ontology is evaluated against a set of natural language questions to determine whether the intended purpose of the ontology has been properly modelled.

By Anna Sofia Lippolis, Mohammad Javad Saeedizade, Robin Keskis\"arkk\"a, Aldo Gangemi, Eva Blomqvist, Andrea Giovanni Nuzzolese