Syntactic Simplification of OWL Class Expressions

Rewrite Once, Validate Anywhere: Producing OWL-Aware SHACL Constraints (Extended Version)

The Shapes Constraint Language (SHACL) is a W3C recommendation to express syntactic constraints, called shapes, on RDF graphs. SHACL validators are used to test whether a given graph adheres to such a shape. However, RDF graphs often come with OWL ontologies, whose implicit knowledge needs to be taken into account. This is classically handled by first applying reasoning and then performing the constraint checking on the results, often using different technologies which makes the process inefficient and vulnerable for mistakes. To overcome this, we propose to internalise the OWL axioms in the SHACL constraints; we construct a rewriting which takes as input both shapes and an OWL EL$^-$ ontology -- a fragment of OWL EL restricting the usage of existential restrictions -- and produces SHACL constraints. This output can then be evaluated by any validator supporting SHACL core regardless of its reasoning support, while yielding the same results as the traditional approach. The implementation of our translation is evaluated both against applying state-of-the-art reasoners and validators consecutively, as against validators with built-in reasoning support. For our benchmark, we show that our approach is in general more efficient in finding violations compared to the sequential approach, thus providing a powerful tool which simplifies combining reasoning with validation.

DeLTA: A Description Logic-based Annotation Schema for Constructing Expressive OWL DL Axioms from Text

LLM-Based Formalization of Engineering Requirements into Ontology-Constrained Knowledge Graphs

Requirements engineering plays a central role in mechanical design, yet technical requirements remain predominantly expressed in natural language, limiting traceability, validation, and computational reasoning. This work presents an ontology-constrained pipeline for transforming natural-language engineering requirements into Industrial Ontologies Foundry (IOF)-grounded knowledge graphs enriched with QUDT-based quantitative semantics. The pipeline decomposes text blocks into individual prescriptive clauses, extracts structural slots and constraint atoms through a typed intermediate representation, normalizes quantitative expressions via QUDT unit and quantity-kind grounding, and instantiates IOF-compliant OWL ABox graphs. The transformation is implemented as a hybrid neuro-symbolic workflow that combines Large Language Models (LLMs) with typed intermediate representations, rule-based post-processing, and description-logic reasoning. Evaluation on a Formula SAE (FSAE) rules corpus, intentionally selected to stress quantitative constraint handling, shows good structural reliability in the evaluated setting. Slot-level extraction achieved a macro accuracy of 94.50%, while quantitative constraint identification reached 97.64% precision and 96.88% recall. Normalization coverage was 98.78%, with residual errors primarily attributable to quantity-kind disambiguation. At the graph level, 93.33% of grounded artifacts passed all ontology-conformance checks, with residual violations concentrated in requirement–specification linkage and specification typing rather than in quantitative-value modeling or systematic misuse of the IOF backbone. These results indicate that ontology-constrained LLM pipelines can support the formalization of engineering requirements into semantically explicit graph representations that are suitable for downstream querying, validation, and analysis.

OntoExpand: SPARQL-Based Ontology Expansion and Reasoning

LLM-Assisted Ontology Engineering and Construction of a French Legal Knowledge Graph

A two-stage LLM-assisted workflow for French maintenance regulations is presented: ontology engineering from a SEMLEG-based core ontology, followed by construction of an ontology-grounded French legal knowledge graph.

Beyond Surface Forms: Symbolic Edits as a Test for Logical Reasoning with LLMs

Logical reasoning with large language models (LLMs) is a critical capability, as it reflects a system's ability to correctly deduce hypotheses from a given context using faithful deductive processes. However, LLM reasoning has often been shown to be sensitive to small surface-level variations in problem formulation, raising questions about whether models truly follow the underlying logical structure. Studying this behavior is challenging because the symbolic components of logical problems, such as operators and predicates, are difficult to systematically manipulate in natural language. We introduce a tool-driven framework for generating controlled, label-preserving edits to logical reasoning problems. Our method operates on symbolic representations of first-order logic and constraint satisfaction problem tasks, enabling targeted modifications to logical operators and other structural components before translating them back into natural language. Using this framework, we evaluate various LLMs under cumulative and individual operator edits and analyze their behavior in response to these changes. Our quantitative and qualitative analyses show that LLM reasoning behavior under controlled operator edits is inconsistent, regardless of model size or family: models sometimes adapt correctly to structural changes but often fail to track their logical consequences. The results from this automated stress test enable an evaluation of language models across different dimensions and help measure the reliability of their reasoning.