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Artificial intelligence (AI) and data-driven applications are transforming industry and research across domains. Despite research advances, the management of the AI and data life cycle in organizations remains largely manual and ad hoc. At the same time, data spaces initiatives are emerging, providing new data-sharing opportunities but posing new challenges for organizations seeking to leverage all their potential. Tackling these challenges, this chapter describes the new framework proposed by the CyclOps project, which aims to enable interoperable and trustworthy automatic management, governance, and maintenance of the entire data life cycle for large-scale volumes of data generated in heterogeneous distributed sources. This supports the development and deployment of AI-based applications across both business and research contexts. CyclOps operationalizes the end-to-end data life cycle, placing at its core knowledge graphs, an established semantic formalism to represent data and metadata adhering to the FAIR Principles, while capturing relevant information to improve reproducibility, traceability, and explainability of the AI results. This core layer is complemented with tools for the automation of data management tasks; distributed data processing; AI tools, algorithms, and models; data space interoperability; and a human-centric interface. The chapter presents the key innovative propositions of CyclOps and its underlying technologies and illustrates them through selected use cases that highlight the general applicability of the approach.

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CyclOps: Leveraging Semantic Technologies for AI and Data Life Cycle Management and Governance

  • 章节

  • 出版模式：

    开放获取

  • 发表日期：

    2026年

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  • Monica Caballero,
  • Anna Queralt,
  • Diego Calvanese,
  • Alexandros Oikonomidis,
  • Chukwuemeka Muonagor,
  • Alberto Abella,
  • Piero Campalani,
  • Benjamin Cogrel,
  • Gabriele Tassi,
  • Julia Palma,
  • Alex Barceló,
  • Saeed Talebzadeh,
  • Ross Little,
  • Peio Oiz,
  • Florencia Pérez,
  • Alexandros Kalafatelis,
  • Maria Font,
  • Sergi Nadal,
  • Alexandros Nizamis,
  • Admela Jukan,
  • Simone Martin Marotta,
  • Ricardo Simón-Carbajo,
  • Javier Conejero,
  • Dolores Ordóñez

摘要

Artificial intelligence (AI) and data-driven applications are transforming industry and research across domains. Despite research advances, the management of the AI and data life cycle in organizations remains largely manual and ad hoc. At the same time, data spaces initiatives are emerging, providing new data-sharing opportunities but posing new challenges for organizations seeking to leverage all their potential. Tackling these challenges, this chapter describes the new framework proposed by the CyclOps project, which aims to enable interoperable and trustworthy automatic management, governance, and maintenance of the entire data life cycle for large-scale volumes of data generated in heterogeneous distributed sources. This supports the development and deployment of AI-based applications across both business and research contexts. CyclOps operationalizes the end-to-end data life cycle, placing at its core knowledge graphs, an established semantic formalism to represent data and metadata adhering to the FAIR Principles, while capturing relevant information to improve reproducibility, traceability, and explainability of the AI results. This core layer is complemented with tools for the automation of data management tasks; distributed data processing; AI tools, algorithms, and models; data space interoperability; and a human-centric interface. The chapter presents the key innovative propositions of CyclOps and its underlying technologies and illustrates them through selected use cases that highlight the general applicability of the approach.