metadata and ontology management

To address insufficient semantic description of multidimensional aggregate/summary data, poor adaptability of metadata standards, and cross-source interoperability challenges in big data environments, this paper proposes a multidimensional data source profiling metadata model tailored for data ecosystems. Built upon RDF, the model is the first to support extensible semantic modeling of both aggregate and summary multidimensional data, enabling semantic alignment of dimensions and measures with reference knowledge graphs. It integrates multi-granularity metadata profiles—spanning source-level, attribute-level, and value-distribution characteristics. The model ensures flexible extensibility and cross-source interoperability. Experimental results demonstrate that profile generation time scales linearly with data cardinality, confirming its engineering practicality and predictable performance.

Traditional knowledge graph (KG) schema construction heavily relies on manual curation by domain experts, limiting scalability and maintainability. Method: We propose the first automated KG schema generation method targeting the Shape Expressions (ShEx) formal language, leveraging large language models (LLMs) in a multi-stage pipeline that jointly incorporates local structural patterns and global semantic context from KGs. Contribution/Results: To support rigorous evaluation, we introduce two benchmark datasets—YAGO Schema and Wikidata EntitySchema—and define dedicated metrics for ShEx schema quality. Experiments across multiple large-scale KGs demonstrate that our approach generates highly accurate, formally verifiable ShEx schemas, significantly improving automation and scalability. This work advances the paradigm shift from manual to LLM-driven KG schema engineering and establishes a novel benchmark and methodology for applying LLMs to syntactically strict, formal specification languages.

This work addresses the lack of semantic interoperability in structured data (e.g., JSON, YAML, CSV) within scientific workflows, which hinders consistent interpretation. The authors introduce an RDF authoring view in the MetaConfigurator editor that leverages AI-assisted generation of RML mappings to automatically transform structured data into RDF. The system supports triple editing, SPARQL querying, and knowledge graph visualization. Key innovations include the first integration of large language models for natural language-to-SPARQL translation, bidirectional synchronization between JSON-LD and RDF triples, and ontology-aware IRI auto-completion. Demonstrated on MOF synthesis experiments, the approach successfully converts JSON protocols into semantic knowledge graphs, enabling interactive exploration of relationships between experimental conditions and outcomes, thereby significantly lowering the barrier to adopting Semantic Web technologies.

To address the challenges of metadata integration and discovery across distributed, heterogeneous data sources in scientific research and library environments, this paper proposes the “Metadata Lake” paradigm—extending the data lake concept to metadata management. It establishes a unified metadata catalog supporting cross-domain aggregation, semantic alignment, and on-demand virtualized delivery. Grounded in the FAIR principles, the system employs RDF/OWL for semantic modeling, Apache Jena for ontology reasoning, GraphQL-based metadata APIs, and a lightweight microservice architecture to unify metadata ingestion, fusion, and querying. Experiments across six real-world scientific data sources demonstrate a 3.2× improvement in metadata discovery efficiency, 91.4% accuracy in cross-source entity linkage, real-time incremental synchronization, and dual-mode querying via SPARQL and GraphQL. This work constitutes the first systematic definition and implementation of a Metadata Lake architecture, delivering a scalable, semantically enriched metadata infrastructure for virtual data lakes.

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.

Existing knowledge graph benchmark datasets commonly lack complete ontological schema information, limiting their utility for evaluating algorithms that rely on semantic constraints or neuro-symbolic reasoning. To address this gap, this work proposes a workflow that jointly extracts both schema and factual triples from knowledge graphs to construct consistency-aware datasets. By leveraging the OWL ontology language and description logic-based reasoning mechanisms, the approach resolves inconsistencies and infers implicit knowledge. The project delivers the first systematically constructed, high-expressivity dataset that integrates a complete ontological schema with factual assertions, while also enriching existing benchmarks with schema information. All released resources support both logical reasoning services and tensor-based loading in mainstream machine learning frameworks, substantially enhancing the fidelity and comprehensiveness of algorithm evaluation.

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.

Enterprise multi-source heterogeneous databases engender data silos and impede semantic interoperability. To address this, we propose a multi-agent collaborative semantic mapping framework wherein large language models serve as semantic agents to automatically align relational database tables and columns with the Schema.org standard ontology, thereby constructing a unified semantic abstraction layer. The framework integrates knowledge graphs, Schema.org-based semantic annotation, and a multi-agent architecture to achieve over 90% mapping accuracy across diverse domains. Compared with conventional ETL pipelines or manual mapping approaches, our method significantly improves efficiency and scalability in cross-system data integration. It provides a practical, scalable technical pathway for large-scale enterprise semantic interoperability, enabling robust, ontology-driven data unification without extensive human curation.

This study investigates how domain-specific metadata schemas can be effectively integrated with the generic DataCite schema to enhance metadata quality and interoperability in research data repositories. Through structural comparisons, cross-schema mapping analyses, and workflow evaluations of metadata records from eight repositories in the earth and social sciences, the research reveals how disciplinary characteristics influence the completeness of DataCite records. Findings indicate that discrepancies between schemas stem primarily from differing modeling philosophies rather than expressive capacity. While optimized cross-schema mappings significantly improve metadata quality, the diversity of repository workflows also critically affects record completeness. Building on these insights, the study proposes a strategy that leverages the complementary strengths of domain-specific and generic schemas, offering practical guidance for fostering interdisciplinary data sharing.

Although scientific data increasingly adhere to the FAIR principles and employ standardized identifiers, practical interoperability remains hindered by heterogeneity in identifier systems and data models. This work proposes and implements two synergistic tools—Babel and ORION—to bridge this gap. Babel constructs clusters of equivalent identifiers through mapping-based clustering and exposes them via a high-performance quantitative API, while ORION standardizes heterogeneous knowledge bases by aligning them to a community-governed common data model. Together, they systematically address the longstanding disconnect between the FAIR “Interoperable” principle and its real-world implementation. The integration of these tools has enabled the construction of a fully interoperable knowledge base, substantially enhancing cross-resource data integration and query capabilities. The resulting framework is publicly available.