This work proposes a domain-independent text annotation schema that maps directly to OWL DL syntax, enabling the algorithmic generation of expressive axioms from the annotations, and demonstrates the applicability and scalability of the approach through two distinct use cases: building requirements and scientific claims.
The results support the feasibility of ontology-driven generation for static-classification systems, whereas arithmetic risk computation and temporal event processing remain better suited to complementary procedural technologies.
Borivoj Bogdanović, S. Nikolić· Computers· 0 citations
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.
Génesis Montenegro, M. Billami, Catherine Faron et al.· arXiv.org· 0 citations
A seven-stage graph-grounded pipeline that converts domain documents into a complete, auditable Web Ontology Language (OWL) Terminological Box (TBox) without any unconstrained generation step is presented, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.
Maruf Ahmed Mridul, A. Talukder, O. Seneviratne· 0 citations
Scientific Knowledge Graphs (SKGs) rely on increasingly complex and heterogeneous semantic models, whose reuse and interoperability require ontologies and other semantic artefacts to be understandable by both machines and humans. However, existing ontology documentation tools provide limited support for modern reuse-or...
Valentina Pasqual, A. Nazari, S. Peroni· 0 citations
OntoExpand is introduced, a new methodology for ontology expansion that uses SPARQL CONSTRUCT queries as an efficient alternative to conventional reasoning techniques that improves performance, reduces computational overhead, is pattern driven allowing a more granular expansion control and seamlessly integrates with SP...
Vitor Lelis, N. Leite, José Carlos et al.· 0 citations
The ontology demonstrates approaches to enhancing the precision of knowledge provenance and outlines the systematic engineering of a knowledge provenance ontology designed to improve its reliability for executive decision-making. Utilizing architectural principles, we propose a formal model that evaluates knowledge pro...
Carolina de Oliveira, Fernando Alvaro Ostuni Gauthier· European Conference on Knowl...· 0 citations
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