Graph Intelligence for Explainable Decision-Making — Shaping the Future: Bridging Expert Knowledge, Data Science, and Generative AI

Abstract

In the age of complexity and information overload, making decisions that are both rational and explainable has never been more critical. This presentation introduces a unified ecosystem built around graph intelligence, where Bayesian networks and semantic graphs serve as powerful frameworks for modeling, interpreting, and sharing knowledge.

At the core of this ecosystem lies BayesiaLab, which enables the construction of probabilistic models through expert input, data-driven learning, and knowledge mining powered by generative AI. These models become operational tools for simulation, diagnosis, optimization, and risk management via the WebSimulator. Complementing this, HellixMap opens new horizons for the qualitative exploration and communication of knowledge structures, turning every graph into a navigable, shareable representation of collective intelligence.

By bridging expert knowledge, data science, and generative AI, this approach empowers organizations to build explainable, actionable models—enhancing decision-making while preserving transparency and trust.

Join us to discover how this graph-centric paradigm is reshaping the future of knowledge representation and decision support.

About the Presenter

Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.