concept ontology alignment

Existing approaches struggle to rigorously characterize structural correspondences between abstract concepts. Method: We propose a skeletal wiring diagram framework based on ologs (ontology logs), modeling concepts as labeled directed graphs, formally defining their categorical structure, and extending graph edit distance to the wiring diagram category to yield a computable concept analogy distance. This integrates category theory, graph theory, and semantic modeling to support cross-domain concept comparison and abstract reasoning. Contributions: (1) We establish the first olog-driven categorical framework for skeletal wiring diagrams; (2) we design a customized edit distance algorithm tailored to wiring diagram syntax and semantics; (3) we achieve a rigorous, transferable, and computable quantification of conceptual analogy—providing foundational theoretical and algorithmic infrastructure for abstraction modeling in autonomous systems.

Traditional knowledge graphs are constrained by static ontologies, limiting their capacity for context-sensitive concept representation and cross-domain reasoning. To address this, we propose Domain-Contextualized Concept Graphs (CDC), which explicitly model “domain” as a computable element of inference, thereby relaxing the rigidity of conventional ontologies. Our method introduces the C-D-C (Concept–Domain–Concept) ternary structure, where domain serves as a relational modifier, and establishes semantic mapping principles grounded in cognitive linguistics. Full logical inference is implemented in Prolog, supported by over twenty standardized relation predicates spanning structural, logical, cross-domain, and temporal dimensions. Empirical validation across educational systems, enterprise knowledge management, and technical documentation demonstrates that CDC enables context-aware reasoning, personalized knowledge modeling, and cross-domain analogy—significantly enhancing both the adaptability and cognitive plausibility of knowledge graphs.

This study addresses the problem of revising description logic concepts under a model-theoretic perspective based on assignment interpretations. To handle concept adjustment when new models are introduced, the paper proposes a formal framework of “model change,” defining three primitive operations: eviction, admission, and revision. It demonstrates that revision cannot be decomposed into a combination of eviction and admission, thereby challenging conventional intuition. Employing the formal semantics and model-theoretic methods of the description logics EL and ALC, the work systematically analyzes the semantic properties, compositional characteristics, and realizability of these operations. Key contributions include establishing positive and negative compatibility results for eviction and admission in both EL and ALC, and laying the theoretical foundation for the revision operation in ALC.

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.

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 study addresses the challenge of semantic interoperability in the defense and national security domain, where numerous highly heterogeneous and specialized ontologies impede effective integration. To bridge this gap, the authors systematically analyze over 60 publicly available ontologies and establish the first Ontology Alignment Evaluation Initiative (OAEI) benchmark track dedicated to this domain, comprising eight alignment tasks. Leveraging multiple state-of-the-art ontology matching systems, they generate automatic alignments, aggregate them into a consensus mapping, and refine the results through expert manual validation to produce a high-quality silver-standard dataset. This work fills a critical void in standardized evaluation for ontology alignment in defense and security contexts, significantly advancing semantic interoperability within the field.

This study addresses the challenges of co-evolution in two-layer modeling (2LM), where fragmented knowledge between metamodels and models hinders consistent evolution. To tackle this, the authors propose the first reproducible empirical framework that applies identical evolutionary changes—via preregistered mutation experiments—to semantically equivalent multi-level modeling (MLM) and 2LM scenarios, automatically detecting inconsistencies and quantifying maintenance effort. By integrating automated consistency checking, a blind mapping protocol, and hypothesis testing, the approach operationalizes co-evolution cost into two measurable variables. Results demonstrate that MLM significantly reduces both inconsistency occurrences and the scope of required modifications due to its structural unification, thereby confirming its advantage in mitigating cascading maintenance costs and establishing a benchmark protocol for evaluating the impact of modeling paradigms.

Current evaluations of language models struggle to assess their comprehension of abstract concepts, and the high-dimensional semantic spaces they operate in often lack interpretability. This work introduces topological data analysis into language model evaluation for the first time, proposing a semantic alignment framework that maps low-dimensional, interpretable knowledge structures—such as ontologies and knowledge graphs—onto model embedding spaces. This approach enables cross-lingual and cross-modal tracking of semantic consistency, effectively uncovering the evolutionary dynamics of conceptual representations during model training. Furthermore, it substantially enhances the interpretability of evaluations concerning cross-lingual phrase understanding, offering a principled means to probe how abstract knowledge is encoded and transformed within modern language models.

This study addresses the challenges of maintaining consistency across heterogeneous schema languages—such as JSON Schema, XSD, and SHACL—during multilingual data model evolution, where fragmented converters, variable quality, and information loss impede reliable interoperability. The work proposes a novel approach that models schema languages and black-box converters as nodes and directed edges in a graph, enabling composable and evaluable conversion path orchestration. By integrating graph-based search, quality-aware ranking (combining agent-assisted and human evaluation), and failure backtracking, the method supports automated, reproducible cross-language schema transformation. The resulting open-source toolchain, Schema Conversion Orchestrator, integrated into the MetaConfigurator platform, successfully produced valid outputs for 43 out of 60 real-world tasks and precisely identified missing ecosystem components in the remaining 17, thereby delineating the current boundaries of schema conversion capabilities.

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.