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Keynote Lectures

Keynote Lecture
David Camacho, Technical University of Madrid, Spain

Open Data Infrastructure in the Age of Generative AI
Elena Simperl, King's College London, United Kingdom

 

Keynote Lecture

David Camacho
Technical University of Madrid
Spain
 

Brief Bio
David Camacho is Full Professor at Computer Systems Engineering Department of Universidad Politécnica de Madrid (UPM), he is the head of the Applied Intelligence and Data Analysis research group (AIDA: https://aida.etsisi.upm.es), the Director of the PhD program in Computer Science and Technologies of Smart Cities, and the Director of the Master program in Machine Learning and Big Data at UPM. He has published more than 300 journals, books, and conference papers (https://scholar.google.es/citations?hl=en&user=fpf6EDAAAAA). His research interests include Machine Learning (Clustering/Deep Learning), Computational Intelligence (Evolutionary Computation, Swarm Intelligence), Social Network Analysis, Fake News and Disinformation Analysis. He has participated/led more than 60 AI-based R&D projects (National and International: H2020, MCSA ITN-ETN, DG Justice, ISFP, NRF Korea), applied to real-world problems in areas as aeronautics, aerospace engineering, cybercrime/cyber intelligence, social networks applications, disinformation countering, or video games among others. He serves as Editor in Chief of Expert Systems from 2023 and sits on the Editorial Board of several journals including Information Fusion, Human-centric Computing and Information Sciences (HCIS), and Cognitive Computation, IEEE Transactions on Emerging Topics in Computational Intelligence (IEEE TETCI), among others. Contact at: David.Camacho@upm.es

Google Scholar: https://scholar.google.com/citations?hl=es&user=fpf6EDAAAAAJ#
ResearchGate: https://www.researchgate.net/profile/David-Camacho-12


Abstract
Available soon.



 

 

Open Data Infrastructure in the Age of Generative AI

Elena Simperl
King's College London
United Kingdom
 

Brief Bio
Elena Simperl is a Professor of Computer at King’s College London and the Director of Research for the Open Data Institute (ODI). She is a?Fellow of the British Computer Society?and the Royal Society of Arts, and a Hans Fischer Senior Fellow. Elena’s work is at the intersection between AI and social computing. She features in?the top 100 most influential scholars in knowledge engineering of the last decade and in the Women in AI 2000 ranking. She is the president of the Semantic Web Sciences Association.


Abstract
Open data infrastructure refers to the systems, frameworks, and processes put in place to collect, store, manage, and share data generated or held by governments and other public institutions. It is meant to ensure that public data is accessible, high-quality, secure, and usable by a wide range of stakeholders, including the general public. For more than a decade, we have witnessed millions of datasets made available via such infrastructure, advancing research, policymaking, and innovation. However, open data infrastructure is still far from realising its potential; non-technical users, in particular, face significant barriers in navigating complex datasets and extracting meaningful information to support their decisions.
In this talk I will walk through some of my recent research into how generative AI could address these barriers. I will start with a series of user studies, which explore how professionals in various data-related roles engage with chatbots to find, make sense, and use open data. Diving deeper to the accuracy issues suggested by these studies, I will then describe two experiments, which use machine unlearning and information leakage methods to understand if existing open datasets are used by widely accessible generative AI tools. Informed by the findings, my team developed PortalGPT, a proof of concept leveraging knowledge graphs, large language models, and retrieval-augmented generation to make open data more accessible and actionable for people with varying levels of data literacy. PortalGPT enhances dataset discovery by bridging the gap between user information needs and structured data queries and enables dataset exploration through interactive analysis tools.
Through conversational natural language interactions, users can seamlessly search, analyze, and explore knowledge from open data portals, redefining the traditional methods of navigating and utilizing open datasets.



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