Adaptive AI Webinar – Neurosymbolic AI for and with Knowledge Graphs

Neuro-symbolic AI is often regarded as the 3rd wave of AI, aiming to integrate symbolic and neural approaches to combine their strengths and address their respective limitations. Stemming from symbolic AI, knowledge graphs (KGs) are a key representation for structured knowledge on the Web. They also constitute a natural meeting point between symbolic reasoning and machine learning, with their logic-based framework and the availability of efficient Machine Learning models to manipulate them. In this talk, Pierre Minnin will present an overview of his research on neuro-symbolic AI for and with Knowledge Graphs, spanning the full KG lifecycle: construction, refinement, and downstream applications.

This webinar is organised within the ENFIELD Project, under the Adaptive AI pillar, and will present ongoing research work developed in the broader context of a TES mobility grants funded through the ENFIELD Open Calls.

Participation is free!

Date: 22^nd June 2026, 10:00-11:00 CEST
Location: Online (Teams)
To register, you must fill in the following form by 19^th June 2026 (EOB CEST).

Target audience

The webinar is targeted towards researchers, PhD students, engineers, and practitioners working in artificial intelligence, machine learning, adaptive AI systems, natural language processing, knowledge extraction, and data science. It is particularly relevant for researchers involved in the ENFIELD project and related research initiatives on adaptive and trustworthy AI.

Speakers

Pierre Monnin, Research Scientist in AI within the Centre Inria d’Université Côte d’Azur

Moderator

Andon Tchechmedjiev, Associate Professor, Institut Mines-Telecom (IMT Mines Alès), France.

Program

Date 22 June 2026, 10:00-11:00 CEST	Time	Presentations
	10:00 – 10:05	Welcome and Introduction – ENFIELD Adaptive AI Pillar and TES Mobility Programme
	10:05 – 10:50	Neurosymbolic AI for and with Knowledge Graphs
	10:50 – 11:00	Discussions and Q&A
	11:00	Closing remarks

Topics & Abstracts

Title:	Neurosymbolic AI for and with Knowledge Graphs
Speaker:	Pierre Monnin
Bio:	Pierre Monnin is a Research Scientist in AI within the Centre Inria d’Université Côte d’Azur, member of the Wimmics team, common with the I3S laboratory (Université Côte d’Azur, CNRS). His research focuses on neuro-symbolic AI with and for knowledge graphs. He investigates interactions between domain knowledge in knowledge graphs and different forms of reasoning in a neuro-symbolic perspective (e.g., injection of domain knowledge in Machine Learning models, analogical reasoning). In particular, studying such interactions in the context of the lifecycle of knowledge graphs (construction, matching, refinement, mining, knowledge discovery), and their usage in downstream applications (e.g., recommender systems, explainable AI). His work involves both theoretical and applied perspectives, often in interdisciplinary settings (e.g., biomedical, educational domains).
Abstract:	Neuro-symbolic AI is often regarded as the 3rd wave of AI, aiming to integrate symbolic and neural approaches to combine their strengths and address their respective limitations. Stemming from symbolic AI, knowledge graphs (KGs) are a key representation for structured knowledge on the Web. They also constitute a natural meeting point between symbolic reasoning and machine learning, with their logic-based framework and the availability of efficient Machine Learning models to manipulate them. In this talk, I present an overview of my research on neuro-symbolic AI for and with Knowledge Graphs, spanning the full KG lifecycle: construction, refinement, and downstream applications.