Knowledge graphs, FAIR principles and generative AI for interdisciplinary research
By Anna Fensel

Anna Fensel is Full Professor of Artificial Intelligence and Data Science at Wageningen University & Research (Netherlands). Previously, she held positions at the University of Innsbruck (Austria), FTW – Telecommunications Research Center Vienna (Austria), and the University of Surrey (UK). She earned her habilitation and PhD in Computer Science from the University of Innsbruck and her Specialist Diploma (MSc-equivalent) in Mathematics and Computer Science from Novosibirsk State University (Russia). Her research focuses on semantic technologies, linked data, and knowledge graphs, and their adoption across domains including sustainability, energy efficiency, production, food and health, and social sciences. She has coordinated EU and national projects and served as principal investigator in 20+ projects. She has contributed to 100+ scientific events, chaired major conferences, serves as journal editor and reviewer, and evaluates research proposals. She has co-authored ~170 refereed publications and received several best paper awards.

The integration of knowledge graphs and semantic web technologies in interdisciplinary research presents a transformative opportunity for its progress, particularly when leveraging on FAIR (Findable, Accessible, Interoperable, Reusable) principles and generative AI (Artificial Intelligence). While symbolic AI and semantic web methods offer robust solutions for data integration and interoperability, a persistent challenge often remains: the lack of high-quality, semantically rich data. Current FAIR data implementations often still fall short, limiting their usability in machine and deep learning analysis and applications. This issue is especially critical in complex, interdisciplinary fields such as agriculture, health, food and social sciences, where heterogeneous data must be efficiently linked to address multi-faceted challenges such as sustainability or climate adaptation. Additionally, data ownership concerns, legal compliance (e.g., GDPR, AI Act), and fragmented governance frameworks further hinder collaboration and innovation. To overcome these barriers, we need specialized research infrastructural solutions that enable responsible data sharing and reuse, while ensuring legal and ethical compliance. In this talk, I will present knowledge graphs -enabled strategies and solutions to enhance FAIR and CARE data practices and explore their role in generative AI applications for advancing agri-food -related research and sustainable and healthy development.