Embedded Files
Irene Celino
Irene Celino
Irene Celino is a Computer Scientist and innovation leader with over 20 years of experience at the intersection of knowledge technologies and human-centered AI. Her work focuses on Semantic Web, Knowledge Graphs, and human-in-the-loop approaches, bridging advanced research with real-world impact. She is Research Line Manager at Cefriel, where she leads interdisciplinary teams translating cutting-edge AI and data innovation into applications for smart cities, mobility, and Industry 5.0. She has contributed to more than 40 collaborative research projects across Europe, combining scientific excellence with hands-on project leadership and technology transfer, and has authored over 100 scientific publications.
More information at iricelino.org.
The Hitchhiker's Guide to Knowledge Engineering
The Hitchhiker's Guide to Knowledge Engineering
Knowledge Engineering has always been concerned with making knowledge explicit, structured and usable. Yet real-world projects repeatedly reveal that knowledge cannot be fully understood when viewed solely through the lens of models, ontologies or knowledge graphs. Knowledge remains deeply connected to the people who create it, interpret it, use it and continuously reshape it.
In this talk, I will revisit a set of lessons learned from more than two decades of applied Knowledge Engineering. Drawing on experiences in ontology engineering, knowledge curation, semantic interoperability, governance, knowledge automation and knowledge graph adoption, I will discuss situations where technical solutions alone were not sufficient to address the underlying challenges. Through a collection of practical examples and assumptions repeatedly challenged by real-world complexity, I will reflect on what these experiences can teach us about the enduring challenges of Knowledge Engineering in practice.
Paul Groth
Paul Groth
Paul Groth is Professor of Algorithmic Data Science at the University of Amsterdam where he leads the Intelligent Data Engineering Lab (INDElab) and is scientific director of the UvA’s Data Science Center. He holds a Ph.D. in Computer Science from the University of Southampton (2007) and has done research at the University of Southern California, the Vrije Universiteit Amsterdam and Elsevier Labs. His research focuses on intelligent systems for dealing with large amounts of diverse contextualized data with a particular focus on web and science applications. This includes research in data provenance, data integration and knowledge sharing.
Previously, Paul led the design of a number of large scale data integration and knowledge graph construction efforts in the biomedical domain. Paul was co-chair of the W3C Provenance Working Group that created a standard for data provenance interchange. He has also contributed to the emergence of community initiatives to build a better scholarly data ecosystem including altmetrics and the FAIR data principles.
Paul is co-author of “Provenance: an Introduction to PROV” and “The Semantic Web Primer: 3rd Edition” as well as over 200 academic articles. More information at pgroth.com.
The Knowledge Placement Problem
The Knowledge Placement Problem
Knowledge in modern AI systems resides in many substrates: model weights, prompts and context windows, retrieval indexes, agent memories, knowledge graphs and databases, callable tools, and people. Foundation models have expanded our ability to make knowledge machine-usable and to extract, transform, and transfer it between representations. Yet these transformations are costly and lossy. Deciding which knowledge belongs where—and when it should move—is now a pervasive design problem, addressed through fine-tuning, retrieval, editing, memory, and extraction. These decisions are often guided by trial and error, software defaults, and intuition, resulting in knowledge infrastructures that can be brittle, difficult to audit, and expensive to maintain. In this talk, I frame the knowledge placement problem as a central challenge for knowledge engineering and outline a research agenda for understanding how placement decisions shape the reliability, cost, provenance, and maintainability of AI systems.
Page updated
Google Sites
Report abuse