multi-level ontology design

To address critical challenges in hybrid modeling and simulation—including semantic inconsistency, poor model reusability, and weak interoperability across systems, disciplines, and tools—this paper proposes an ontology-driven hybrid modeling and simulation framework. Methodologically, it introduces a novel “ontology + reference ontology” dual-track coordination mechanism that uniformly supports human–human, human–machine, and machine–machine interoperability axes, while endowing ontologies with dual functionality: domain description and simulation specification. The framework integrates capability-oriented problem modeling, hierarchical ontology design, ontology pattern reuse, and RDF/OWL-based semantic web technologies. Empirical validation across four representative application domains—sea-level rise analysis, Industry 4.0 modeling, policy experimentation in artificial societies, and cyber threat assessment—demonstrates significant improvements in semantic alignment, multi-tool integration, and explainable AI support. Results confirm enhanced semantic rigor, model reusability, and cross-system interoperability.

This study addresses three critical challenges in materials science ontologies: fragmentation, insufficient regulatory integration, and a lack of mechanistic explanatory power. To overcome these limitations, the authors propose a novel multi-level modular ontology architecture featuring a three-dimensional design that integrates abstraction level, user audience, and mechanistic depth. The framework incorporates a seven-layer mechanistic explanation backbone grounded in symmetry, thermodynamics, defect chemistry, and related principles. Implemented using OWL and SHACL, the resulting Ceramic Ontology (OCO v0.94) comprises 5,196 classes, 167,348 axioms, 829 cross-ontology mappings, and 163 competency questions. This ontology enables cross-material reuse, supports deep mechanistic reasoning, and facilitates compliance with the European Union’s Digital Product Passport requirements.

Traditional ontology design relies on single-axis, mutually exclusive, and collectively exhaustive top-level categories (e.g., continuants vs. occurrents), which impedes the dynamic evolution required by open, collaborative knowledge graphs. Method: Taking Wikidata as a case study, we systematically analyze its polyhierarchical architecture—centered on a unified root node “entity” and supporting multiple inheritance and cross-classification across orthogonal axes—and formally articulate the multi-axis ontology paradigm for the first time. Using ontology engineering, schema reverse engineering, and pattern induction, we develop an extensible multi-axis ontology design framework. Contribution/Results: Our framework significantly improves cross-domain collaborative editing efficiency, category adaptability to dynamic changes, and flexibility/maintainability in large-scale entity classification. It relaxes the classical formal ontology constraints of exclusivity and exhaustiveness, enabling modular, scalable modeling for collaborative knowledge graphs.

Ontology interoperability is hindered by conceptual conflicts and semantic overlap, limiting the coordinated use of ontologies in knowledge graphs (KGs). To address this, we propose a full-lifecycle ontology interoperability ecosystem that integrates three complementary semantic technologies: (1) Ontology Design Patterns (ODPs) to support reusable and compatible ontology design; (2) Ontology Matching and Versioning (OM&OV) to ensure evolutionary consistency across ontology updates; and (3) Ontology-Compliant Knowledge Graphs (OCKGs) to enable staged, semantically aligned ontology integration. Evaluated in the construction domain, our approach improves inter-ontology concept mapping accuracy by 32.7% and accelerates KG construction by 41%. It significantly enhances ontology integrability and maintainability in real-world tasks, providing a systematic, methodology-driven foundation for standardized ontology interoperability in domain-specific knowledge graphs.

This paper addresses core challenges in applied ontologies—namely, referential ambiguity of terms, weak semantic holism, and poor cross-domain understandability and reusability. To resolve these issues, we propose the “ontology component,” a novel structural unit centered on domain terms and integrating description logic-based formal semantics with natural language annotations in a principled manner. Leveraging an assertion-driven approach to semantic formalization, our method enables operational definitions of meaning. Crucially, this design unifies formal logical representation and natural language interpretation for the first time, substantially enhancing term-level semantic robustness, clarity, and interdisciplinary accessibility. Moreover, ontology components natively support version evolution and modular reuse, thereby establishing both a theoretical foundation and a practical methodology for building reusable, evolvable semantic infrastructure.

This work addresses the limitations of existing ontology documentation tools in supporting modular modeling and human readability, particularly in handling cross-module entities and annotations. To overcome these challenges, the authors refactor and extend the LODE framework by introducing a modular Reader-Model-Viewer architecture that decouples parsing, modeling, and rendering components. Implemented as a web service, the new framework provides enhanced capabilities for generating OWL ontology documentation, featuring dedicated entity pages, RDF provenance tracking, and Markdown-based rendering. These improvements significantly increase the intelligibility and reusability of modular scientific knowledge graph ontologies. The framework has been successfully applied to the documentation of the SKG-O ontology, demonstrating its practical utility and effectiveness.

This study addresses the challenge of high costs associated with manual ontology construction in specialized domains, which often results in a lack of authoritative reference resources. It presents the first systematic exploration of leveraging large language models (LLMs) for automated domain ontology development. Focusing on Brazil’s “Blue Amazon” maritime territory as a case study, the authors employ prompt engineering to guide GPT-3.5 and GPT-4 in emulating domain experts, enabling the automatic generation of structured concept hierarchies from initial seed concepts. The experimental pipeline produced twenty ontologies, which expert evaluators deemed largely coherent and logically organized. Although minor human refinement remains necessary, the results strongly demonstrate the feasibility and practical potential of using LLMs as virtual experts to support ontology construction in knowledge-intensive domains.

Traditional ontology extension methods are resource-intensive and error-prone, while existing large language model–based approaches lack explicit alignment with user requirements and reusable core ontologies, and suffer from insufficient systematic evaluation. This work proposes the first ontology extension framework that integrates competency question–driven design with retrieval-augmented generation (RAG), enabling context-aware, requirement-guided generation of ontology fragments by explicitly linking user needs—formulated as competency questions—with existing ontological knowledge. Evaluated on two real-world use cases, the generated fragments exhibit sound structural integrity and pass all functional tests; expert engineers assessed them as requiring only minor to moderate revisions for integration. These results demonstrate the feasibility, scalability, and evalability of the proposed approach.