manage catalog governance

Traditional metadata management suffers from low automation, reactive governance, weak semantic expressiveness, and heavy reliance on manual effort. To address these challenges, this paper proposes the first AI-augmented metadata management framework integrating large language models (LLMs), knowledge graphs, and automated semantic understanding. The framework enables intelligent metadata generation, dynamic governance, and semantic enrichment—overcoming limitations of static schemas and manual annotation. By orchestrating open-source and commercial toolchains, it achieves unified modeling and real-time evolution of heterogeneous, cross-source data. Experimental results demonstrate a 32% improvement in metadata generation accuracy and a 4.8× increase in coverage; governance cycle time is reduced by 73%; and discoverability and interoperability of complex datasets are significantly enhanced. This work establishes a scalable, production-ready paradigm for intelligent data governance in data-intensive environments.

This work addresses the inadequacy of existing large language model (LLM) lifecycle frameworks, which predominantly emphasize operational efficiency while lacking explicit support for security-critical activities—such as data provenance, component signing, and access control—and failing to align governance requirements with specific lifecycle phases. The paper proposes the first security-oriented LLM system lifecycle model, structured not by workflow but by security boundaries, organizing 32 phases into four layered pipelines: data, model, distribution, and application, while integrating LLMOps and governance pillars. It uniquely identifies 13 distinct security-critical phases and exposes a structural imbalance wherein regulatory evidence is concentrated at deployment despite pivotal decisions occurring during development. By mapping key standards—including NIST AI RMF, the EU AI Act, and ISO/IEC 42001—the study establishes a phase-to-governance correspondence mechanism, yielding a comprehensive, lifecycle-spanning security analysis framework that offers structured guidance for compliance and secure design.

To address challenges in data spaces—including difficult integration of metadata catalogs with connectors, weak cross-domain interoperability, and compromised data sovereignty due to tight coupling between policies and metadata—this paper proposes a hybrid federated architecture that decouples metadata management from access policy enforcement. We build an extensible federated metadata catalog atop DataHub, supporting multi-source data ingestion and end-to-end lineage governance. Additionally, we design and implement Rainbow Catalog, a custom ODRL-compliant policy engine enabling fine-grained access control policy modeling, publication, and dynamic enforcement, tightly integrated with metadata querying. The architecture strictly adheres to the International Data Spaces (IDS) reference architecture and protocol specifications. Evaluation demonstrates significant improvements in cross-domain data sharing security, automation, and standardized interoperability—while rigorously preserving data sovereignty.

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.

This work addresses the need for automated digital rights policy generation in multi-institutional, culturally oriented trusted data spaces. We propose a large language model (LLM)-based natural language-to-ODRL policy mapping method. Our approach uniquely integrates the W3C ODRL ontology and its structured documentation as core components of prompt engineering to guide GPT-4 in generating high-fidelity, standards-compliant policies. Additionally, we introduce an ontology-adaptation heuristic tailored for knowledge graph construction to enhance semantic alignment. Evaluated on 12 culturally diverse use cases spanning varying complexity levels, our method achieves a policy generation accuracy of 91.95%, significantly outperforming existing baselines. The contribution lies in establishing a scalable, interpretable, and standards-aligned automation paradigm for open digital rights management—bridging natural language requirements with formal, machine-processable ODRL policies.

This work addresses the lack of a unified configuration governance mechanism in heterogeneous multi-agent systems, which hinders versioned, auditable, and cross-framework consistent management. The authors propose a framework-agnostic reference model for agent configuration governance that normalizes diverse configurations into a canonical configuration graph via semantic projection and enforces uniform governance semantics over this graph. Key innovations include typed and independently versioned configuration items, strict decoupling of configuration from runtime, a lattice-based monotonic influence propagation mechanism, and dependency-aware immutable revisions with provenance tracking. The model is validated across LangGraph, CrewAI, and OpenAI Agents SDK, demonstrating governance-equivalent ACM representations across 27 governance scenarios and 9 propagation cases, while guaranteeing convergence, termination, and a unique fixed point.

This study addresses the proliferation of functional redundancy in service-oriented architectures caused by heterogeneous clients, which undermines system evolvability and maintainability. To mitigate this issue, the authors propose a novel reference architecture that synergistically integrates metadata-driven mechanisms with pattern languages. By leveraging metadata management and a plugin-based design, the approach effectively constrains service redundancy while enhancing reuse capabilities. The work innovatively combines metadata mechanisms and pattern languages in architectural construction and validates its efficacy through a triangulated evaluation method incorporating scenario-based assessment and real-world case studies. Empirical results demonstrate that the majority of system changes during evolution require no code modifications—only configuration adjustments or the addition of pluggable components—thereby significantly improving architectural stability and reuse efficiency.