Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0
Abstract
The convergence of artificial intelligence, cyber-physical systems, and cross-enterprise data ecosystems has propelled industrial intelligence to unprecedented scales. Yet, the absence of a unified trust foundation across data, services, and knowledge layers undermines reliability, accountability, and regulatory compliance in real-world deployments. While existing surveys address isolated aspects, such as data governance, service orchestration, and knowledge representation, none provides a holistic, cross-layer perspective on trustworthiness tailored to industrial settings. To bridge this gap, we present \textsc{Trisk} (TRusted Industrial Data-Service-Knowledge governance), a novel conceptual and taxonomic framework for trustworthy industrial intelligence. Grounded in a five-dimensional trust model (quality, security, privacy, fairness, and explainability), \textsc{Trisk} unifies 120+ representative studies along three orthogonal axes: governance scope (data, service, and knowledge), architectural paradigm (centralized, federated, or edge-embedded), and enabling technology (knowledge graphs, zero-trust policies, causal inference, etc.). We systematically analyze how trust propagates across digital layers, identify critical gaps in semantic interoperability, runtime policy enforcement, and operational/information technologies alignment, and evaluate the maturity of current industrial implementations. Finally, we articulate a forward-looking research agenda for Industry 5.0, advocating for an integrated governance fabric that embeds verifiable trust semantics into every layer of the industrial intelligence stack. This survey serves as both a foundational reference for researchers and a practical roadmap for engineers to deploy trustworthy AI in complex and multi-stakeholder environments.
Keywords
Cite
@article{arxiv.2601.04569,
title = {Industrial Data-Service-Knowledge Governance: Toward Integrated and Trusted Intelligence for Industry 5.0},
author = {Hailiang Zhao and Ziqi Wang and Daojiang Hu and Zhiwei Ling and Wenzhuo Qian and Jiahui Zhai and Yuhao Yang and Zhipeng Gao and Mingyi Liu and Kai Di and Xinkui Zhao and Zhongjie Wang and Jianwei Yin and MengChu Zhou and Shuiguang Deng},
journal= {arXiv preprint arXiv:2601.04569},
year = {2026}
}