Data Quality and Ethics in Cultural Heritage Knowledge Graphs
Valentina Presutti, University of Bologna, Italy, Italy
Available Soon
Ladjel Bellatreche, National Engineering School for Mechanics and Aerotechnics, France, France
Brief Bio
Valentina is an Associate Professor of Computer Science at the University of Bologna. She is also an Associate Researcher at the Institute of Cognitive Science and Technologies of CNR and coordinator of STLab. She received her Ph.D in Computer Science at the University of Bologna (2006). Her research interests include AI, Semantic Web and Linked Data, Knowledge Extraction, Empirical Semantics, Social Robotics, Ontology and Knowledge Engineering. She coordinates the EUH2020 project Polifonia (2021-2024). She was responsible for several national and EU projects (e.g. MARIO, IKS, ArCo). During her post-doc she worked in NeOn and created ontologydesignpatterns.org and the series WOP, reference resources for semantic web researchers. She has published +150 peer reviewed articles. She is part of the editorial board of J. of Web Semantics (Elsevier), Data Intelligence (MIT Press), JASIST (Wiley), Intelligenza Artificiale (IOS Press), and of "Semantic Web Studies" (IOS Press). She is co-director of International Semantic Web Research Summer School (ISWS) and has served in organisational and scientific roles for several events. Google Profile - https://scholar.google.com/citations?user=dvNHkAwAAAAJ&hl=en
Abstract
Digital cultural heritage risks turning into a distorted echo of the past, built on fragmented and biased data. This keynote argues that ensuring data quality is not merely a technical challenge, but an ethical responsibility: Knowledge Graphs (KGs) that represent our heritage actively shape historical narratives and influence the reliability of AI systems. The talk will highlight both the technical and ethical dimensions of this issue, focusing on challenges such as data heterogeneity and cultural bias. Drawing on examples from our research, such as ArCo, the KG of Italian Cultural Heritage, and ChoCo, a KG integrating musical harmony datasets, I will illustrate practical methodologies to enhance data quality, while also exposing their limits and pointing to open research challenges that demand attention.
Brief Bio
Ladjel Bellatreche is an Exceptional Class Full Professor at the National Engineering School for Mechanics and Aerotechnics (ISAE-ENSMA) in Poitiers, France, since 2010. He previously served as an Assistant and Associate Professor at Poitiers University and is currently a Part-time Professor at Harbin Institute of Technology (HIT), China. He has held various visiting positions at international institutions in Australia, Canada, the USA, and China.
Professor Bellatreche’s research covers data science, artificial intelligence, knowledge graphs, query-answering systems, recommender systems, and large language models, with a strong emphasis on query performance and energy efficiency. He has authored over 360 publications in leading international conferences and journals. He has led research teams and played major roles in international conferences such as ADBIS, CoopIS, ER, DaWaK, and IEEE Big Data. He serves as Associate Editor for Data and Knowledge Engineering (Elsevier) and Knowledge and Information Systems Journal (Springer), and is a member of the steering committees of several international conferences, including ER, ADBIS, DOLAP, MEDES, and BDA. He is also active on editorial boards and program committees, and has (co)-supervised 37 PhD students. His work spans multiple application domains, including aeronautics, urban computing, medicine, the film industry, and sustainable development