ontology engineering

To address challenges in Cyber-Physical Systems (CPS) development—including heterogeneous formal models, fragmented storage of modeling artifacts, inadequate version management, and limited knowledge reuse—this paper proposes an ontology-driven engineering knowledge graph framework. It introduces a unified systems engineering ontology built upon the custom Ontology Modelling Language (OML), enabling semantic integration of modeling artifacts across formal methods (e.g., SysML, UML, Modelica). The framework integrates a workflow engine, SPARQL querying, SWRL rule-based reasoning, and versioned graph storage to implicitly encapsulate complex knowledge graph operations. It is the first to support full-lifecycle semantic interoperability and automated knowledge discovery. Evaluated on an electric-drive intelligent sensor system, the framework significantly improves model version management efficiency, accelerates information retrieval, and uncovers three categories of latent engineering knowledge via inference.

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.

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 work addresses the limitations of large language models (LLMs) in long-term memory retention, structured understanding, and multi-step reasoning by proposing a hybrid intelligent system architecture. The approach employs an automated pipeline to construct RDF/OWL ontologies from heterogeneous data as an external memory layer, integrating vector-based retrieval with graph-based reasoning to establish an LLM-driven generate–verify–refine loop. By combining named entity recognition, relation extraction, triple generation, and SHACL/OWL constraint validation, the system substantially enhances the explainability and reliability of its inferences. Evaluated on multi-step planning tasks such as the Tower of Hanoi, the proposed framework outperforms baseline LLMs and enables formal verification and systematic error correction of its outputs.

Entity disambiguation and linking in IT domains suffer from poor domain adaptability and difficulty in incorporating domain-specific knowledge when relying solely on general-purpose knowledge graphs (e.g., Wikidata, DBpedia). Method: This paper proposes a lightweight, extensible ontology construction method tailored for the IT domain. Starting from general Linked Open Data (LOD) resources, it employs a domain-agnostic pipeline and—novelly—integrates an IT-specific terminology lexicon to drive ontology schema expansion. The approach synergistically combines SPARQL querying, RDF reasoning, and ontology alignment. Contribution/Results: The resulting paradigm balances generality and domain specificity, significantly improving accuracy in IT entity disambiguation and linking. It establishes a low-barrier, reusable ontology engineering framework that supports continuous injection of proprietary domain knowledge, thereby enabling sustainable, scalable domain ontology development.

This work addresses the challenge that large language models (LLMs) struggle to adhere to formal semantic constraints in real time when generating structured knowledge, often relying on inefficient and error-prone post-hoc validation. To overcome this limitation, the authors propose an ontology-to-tool compilation mechanism that automatically translates domain ontology specifications into executable tool interfaces. By compelling LLM agents to interact with knowledge graphs exclusively through these generated tools, the approach proactively enforces semantic consistency during knowledge generation. Built upon The World Avatar framework, the method integrates the Model Context Protocol, ontology-driven tool synthesis, and agent workflows, substantially reducing the need for manual prompt engineering. Evaluated on the task of processing scientific literature on metal–organic polyhedra synthesis, the system successfully guides LLMs to extract, validate, and repair structured knowledge, demonstrating the feasibility and advantages of this paradigm for scientific text understanding.

This study addresses the challenges of internal consistency and external interoperability in the automatic alignment and integration of heterogeneous knowledge graphs by proposing an ontology-compatible knowledge graph construction approach. The method integrates a novel ontology-driven term-matching algorithm, a schema-based compliance modeling mechanism, and a quantitative metric for assessing conformance, ensuring that the resulting knowledge graphs strictly adhere to prescribed ontological specifications at both structural and semantic levels. Experimental validation in the architectural domain demonstrates the effectiveness of the approach, significantly enhancing the interpretability of the knowledge graphs and their interoperability across systems. This work thus offers a viable pathway toward the automated fusion of heterogeneous knowledge graphs while preserving ontological fidelity.

Automatically generating high-quality formal ontologies from unstructured text remains challenging, as existing large language model (LLM)-based approaches are often hindered by ambiguous design, structural redundancy, and ineffective repair mechanisms. This work proposes a planning-first, artifact-driven multi-agent paradigm for ontology generation, decomposing the task into a collaborative workflow among four specialized roles: domain expert, manager, coder, and quality assurer. The framework integrates heterogeneous LLM-based review, SPARQL competency assessment, and retrieval-augmented generation to iteratively refine ontological artifacts. Compared to single-agent baselines, the proposed approach substantially improves the structural quality and auditability of generated ontologies while moderately enhancing their query usability.

This work addresses the limitations of traditional knowledge graph construction approaches, wherein structural decisions are hard-coded into rigid pipelines, resulting in tight coupling between schema and construction process and hindering support for ontology-level tasks. To overcome this, the authors propose an ontology-oriented construction framework featuring a novel intrinsic-relational routing mechanism. This mechanism dynamically assigns attributes to corresponding schema modules through iterative attribute classification, enabling a declarative and reusable decoupled design. The pipeline integrates rule-based cleaning, tool-augmented large language model–assisted annotation, and human review. Evaluated on Wikidata (January 2026), the resulting graph comprises 34 million nodes and 61.2 million edges, achieving 93.3% schema coverage and 98.0% module assignment accuracy, effectively supporting five ontology-level applications.