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.
arXiv:2607. 17963v1 Announce Type: new Abstract: Ontology extension refers to the process of enriching an existing ontology in response to emerging requirements, making it more complete.
arXiv:2404. 11716v2 Announce Type: replace Abstract: Building Energy Management (BEM) is central to reducing energy use and CO2 emissions in the building sector.
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.
arXiv:2608. 12304v1 Announce Type: new Abstract: Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements.
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.
Dynamic Master Logic (DML) provides a hierarchical framework for representing system behavior by linking functional objectives to underlying structural elements. However, DML construction typically relies on expert interpretation of technical documentation, limiting scalability for complex systems.
UniDataAgent (UniDataAgent) is an ontology‑grounded system designed to automate enterprise question‑to‑report tasks while preserving organization‑specific semantics. It separates semantic acquisition from online execution, with an Ontology Acquisition and Validation (OAV) stage that builds versioned ontologies from metadata, business knowledge, and expert input, and a Question‑to‑Report Execution (QRE) stage that retrieves semantic contracts, coordinates skills and data tools, validates results, and produces evidence‑linked reports. In a deployment across 27 enterprise tables and thousands of metric types, ontology construction took a few hours versus a week manually, and report generation took minutes versus several working days, achieving 95.0% strict accuracy on real business questions compared to 72.5% for document RAG.
arXiv:2607. 29657v1 Announce Type: new Abstract: Fault detection and diagnosis (FDD) technology is essential for improving HVAC system reliability, energy efficiency, and maintenance effectiveness.
arXiv:2607. 18262v1 Announce Type: new Abstract: The SFB 1574 Circular Factory is building a shared knowledge graph infrastructure for integrating data about returned products.
arXiv:2609.24397v1 Announce Type: new Abstract: The conventional testing process quickly fails when applied to ML-based systems such as obstacle detection in vehicles: if an obstacle is not detected...
arXiv:2608.22974v1 Announce Type: new Abstract: Large language model (LLM) agents rely heavily on knowledge encoded in model parameters or presented as unstructured context. In domain-specific tasks,...