KG.gov: Knowledge Graphs as the Backbone of Data Governance in AI

TY - JOUR

T1 - KG.gov: Knowledge Graphs as the Backbone of Data Governance in AI

AU - Merono Penuela, Albert

AU - Simperl, Elena

AU - Kurteva, Anelia

AU - Reklos, Ioannis

PY - 2025

Y1 - 2025

N2 - As (generative) Artificial Intelligence continues to evolve, so do the challenges associated with governing the data that powers it. Ensuring data quality, privacy, security, and ethical use become more and more challenging due to the increasing volume and variety of the data, the complexity of AI models, and the rapid pace of technological advancement. Knowledge graphs have the potential to play a significant role in enabling data governance in AI, as we move beyond their traditional use as data organisational systems. To address this, we present KG.gov, a framework that positions KGs at a higher abstraction level within AI workflows, and enables them as a backbone of AI data governance. We illustrate the three dimensions of KG.gov: modelling data, alternative representations, and describing behaviour; and describe the insights and challenges of three use cases implementing them: Croissant, a vocabulary to model and document ML datasets; WikiPrompts, a collaborative KG of prompts and prompt workflows to study their behaviour at scale; and Multimodal transformations, an approach for multimodal KGs harmonisation and completion aiming at broadening access to knowledge.

AB - As (generative) Artificial Intelligence continues to evolve, so do the challenges associated with governing the data that powers it. Ensuring data quality, privacy, security, and ethical use become more and more challenging due to the increasing volume and variety of the data, the complexity of AI models, and the rapid pace of technological advancement. Knowledge graphs have the potential to play a significant role in enabling data governance in AI, as we move beyond their traditional use as data organisational systems. To address this, we present KG.gov, a framework that positions KGs at a higher abstraction level within AI workflows, and enables them as a backbone of AI data governance. We illustrate the three dimensions of KG.gov: modelling data, alternative representations, and describing behaviour; and describe the insights and challenges of three use cases implementing them: Croissant, a vocabulary to model and document ML datasets; WikiPrompts, a collaborative KG of prompts and prompt workflows to study their behaviour at scale; and Multimodal transformations, an approach for multimodal KGs harmonisation and completion aiming at broadening access to knowledge.

KW - knowledge graphs

KW - ai

KW - governance

M3 - Article

SN - 1570-8268

JO - Journal of Web Semantics

JF - Journal of Web Semantics

ER -