Keynote presentation from Prof. Michel Dumoniter at the EGC 2025
Today, REALM's coordinator, Professor Michel Dumontier from Maastricht University will give a keynote lecture at the EGC2025 Conference in Strasbourg, France.
The lecture is titled "Towards Biomedical Neurosymbolic AI: From Semantic Knowledge Infrastructure to Explainable Predictions".
Abstract: The increased availability of biomedical data, particularly in the public domain, offers the opportunity to better understand human health and to develop effective therapeutics for a wide range of unmet medical needs. However, data scientists remain stymied by the fact that data remain hard to find and to productively reuse because data and their metadata i) are wholly inaccessible, ii) are in non-standard or incompatible representations, iii) do not conform to community standards, and iv) have unclear or highly restricted terms and conditions that preclude legitimate reuse. These limitations require a rethink on data that can be made machine and AI-ready – the key motivation behind the FAIR Guiding Principles. Concurrently, while recent efforts have explored the use of deep learning to fuse disparate data into predictive models for a wide range of biomedical applications, these models often fail even when the correct answer is already known, and fail to explain individual predictions in terms that data scientists can appreciate. These limitations suggest that new methods to produce practical artificial intelligence are still needed.
In his talk, Professor Dumontier will discuss his team's work in (1) building an integrative knowledge infrastructure to prepare FAIR and « AI-ready » data and services along with (2) neurosymbolic AI methods to improve the quality of predictions and to generate plausible explanations. Attention is given to standards, platforms, and methods to wrangle knowledge into simple, but effective semantic (e.g. knowledge graphs and ontologies) and latent representations (i.e. embeddings), and to make these available into standards-compliant and discoverable interfaces that can be used in model building, validation, and explanation. The work of Prof. Dumontier and his team, together with the work of others in the field, creates a baseline for building trustworthy and easy-to-deploy AI models in biomedicine.
The EGC (Extraction et Gestion des Connaissances) Conference is an annual event bringing together researchers and practitioners from disciplines related to data and knowledge sciences. These disciplines include machine learning, knowledge engineering, and representation, reasoning on data and knowledge, data mining and analysis, information systems, databases, semantic web, and open data.
Learn more about the programe here.