Jul 2026· IEEE International Conference on Circuits and Systems for Communications· pp. 1-6· 0 citations· 18 references
Abstract
Ontology alignment is critical for semantic interoperability, yet it remains difficult due to semantic ambiguity, poor axiomization, and scalability constraints. While newer Large Language Model (LLM)-based techniques increase semantic comprehension, they frequently rely on repeated model invocations, use stochastic alignment decisions, and are primarily concerned with class-level matching. This study provides an ontology matching system that separates semantic comprehension and alignment judgments. Each element is processed independently, using a single LLM call for semantic typing, reducing stochasticity. This typing guides the selection of a reference ontology (e.g., DBpedia, Friend Of A friend (FOAF), SKOS) as a semantic mediator. Alignment is performed using embedding-based similarity with optional OWL validation. The approach supports both Terminological Box (TBox) and RBox alignment, addressing Role Box (RBox) matching while improving scalability, interpretability, and reliability. It also ties to Retrieval-Augmented Generation (RAG) frameworks by using external ontologies as structured knowledge sources, while avoiding generative alignment decisions.
The results suggest that OntoExtend is useful as a drafting assistant for requirement-driven ontology extension in real world scenarios, while remaining sensitive to CQ specificity and modelling profile.
Anna Sofia Lippolis, Mohammad Javad Saeedizade, Stefan Schmid et al.· 1 citation
Constructing formal ontologies from domain documents requires simultaneously enforcing corpus grounding, vocabulary consistency, axiom-level expressivity, and end-to-end provenance, a combination no existing automatic system delivers. We present 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. Documents are first encoded as Unified Discourse-Hypergraphs (UDH) capturing entity participation and discourse dependencies; subsequent stages transform this graph evidence into a class hierarchy, typed object and datatype properties, and restriction axioms, with Large Language Model (LLM) usage restricted to narrow, graph-grounded mediation tasks. A paired Assertional Box (ABox) population procedure grounds named individuals in the induced TBox, enabling SPARQL-based functional evaluation. Every emitted term carries a full decision chain from raw source passages through each pipeline stage, making the TBox directly auditable and suitable for targeted human refinement. Evaluated on the life insurance domain using two established benchmarks and a new 100-contract corpus spanning ten product types, our pipeline achieves strong results across all evaluation dimensions, outperforming direct and multi-agent LLM baselines on competency-question (CQ) coverage (0.85 vs. 0.63 and 0.62 on one term-life contract; 0.77 vs. 0.40 and 0.44 on another contract), while also attaining high keyphrase coverage comparable to a manually-constructed reference ontology and strong performance on structured gap-and-overlap reasoning, all without any manual TBox engineering. Ontology growth analysis provides evidence consistent with vocabulary saturation at scale, demonstrating that the pipeline produces stable, reusable domain representations from large document corpora.
Maruf Ahmed Mridul, A. Talukder, O. Seneviratne· 0 citations
The goal of this work is to put into context recent updates in ontology engineering and present the necessary additional information targeted to reuse by providing an updated overview and proposing a two-tier categorisation, believed to be the first review to offer such an intra-category classification.
Davide Di Pierro, L. Abrouk, A. Guyot et al.· 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.· 0 citations