OntoExtend: A Framework for Requirement-driven and Scalable Ontology Extension with LLMs

OntoExpand: SPARQL-Based Ontology Expansion and Reasoning

On the Use of Large Language Models for Schema Mapping Generation in Ontology-Based Data Integration

From Rule Engines to Ontologies: An OWL 2 DL Approach for Domain-Specific Evaluation Information Systems

Domain-specific information systems often maintain their data model, rule base, and application infrastructure as separate artifacts, complicating maintenance and pre-deployment verification. This study investigates whether these artifacts can be unified in a verifiable ontology-to-code pipeline without changing the expected classifications. The proposed Model-Driven Architecture uses the Business Application Builder framework and a Web Ontology Language 2 Description Logic ontology to represent domain structure, classification rules, and generation metadata. HermiT verifies consistency, satisfiability, and subsumption under open-world semantics before code generation. The generator produces persistence, business-logic, data-transfer, and presentation layers, while the generated Java application evaluates stored records under closed-world semantics and resolves overlapping categories using ontology-declared priorities. In a Serbian research-evaluation case study, the generated system reproduced the M30 and M33 classifications of an established Jess implementation. An internal secondary experiment generated and executed a prenatal-diagnosis application; all six runtime classifications matched the HermiT entailments and expected outcomes. The public artifact independently reproduces the ontology-level experiments but excludes the proprietary generator and generated source code. 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. No performance superiority is claimed.

COntExt: Towards Context-Aware Ontology Extension from Operational Metrics

Organizations increasingly define operational metrics in structured, machine-readable formats to monitor systems, processes, and compliance. These metric definitions implicitly encode domain knowledge, such as referencing concepts, properties, and relationships, that often extends what is captured in formal ontologies. Yet the connection between operational metric catalogues and ontological knowledge remains manual, ad-hoc, and labor-intensive. We present COntExt, a framework for context-aware ontology extension that takes structured metric definitions as input and suggests how referenced concepts and properties should be integrated into an existing ontology, utilizing the context of these metrics. The framework defines the extension problem as three sub-tasks: parent class prediction, relation type prediction, and data property assignment. Across four cybersecurity ontologies, we evaluate different algorithms for each task. Our results show that metric-derived context improves the suggestions over ontology-context baselines for relation type prediction and data property assignment. Our work demonstrates that operational metric catalogues are a practical and underexploited source for ontology extension. This work enables organizations to maintain their ontologies at a significantly lower cost than manual engineering.

Authors’ responses to the feedback of the reviewers Paper: From Manual to LLM-assisted: A Mapping Review on Ontology Construction Methods

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.

Toward Effective and Reliable LLM Agents via Dynamic Ontology

OaK is presented, an ontology-as-a-kernel framework that dynamically constructs and refines task-oriented ontologies for LLM agents and shows that OaK improves standard LLM agents, strengthens evidence grounding, and boosts the reliability of multi-step reasoning.