Samy Badreddine
Profile
Samy received his bachelor’s in civil engineering and a master’s in computer science and engineering both from the Free University of Brussels. During his master studies, he joined Sony Computer Science Laboratories for a three-month internship and fell in love with research. He decided to join Sony AI after graduating to grow further in this environment. His research focus is in neurosymbolic AI, where he explores non-classical logical operators and their mathematical properties in machine learning contexts.
Publications
Literature-based Hypothesis Generation: Predicting the evolution of scientific literature to support scientists
AI4X, 2025 | Tarek R Besold, Uchenna Akujuobi, Samy Badreddine, Jihun Choi, Hatem ElShazly, Frederick Gifford, Kana Maruyama, Kae Nagano, Pablo Sanchez Martin, Thiviyan Thanapalasingam, Alessandra Toniato, Christoph Wehner
Science is advancing at an increasingly quick pace, as evidenced, for instance, by the exponential growth in the number of published research articles per year [1]. On the one hand, this poses anincreasingly pressing challenge: Effectively navigating this ever-growing body o...
Link prediction for hypothesis generation: an active curriculum learning infused temporal graph-based approach
AIR, 2024 | Uchenna Akujuobi, Priyadarshini Kumari, Jihun Choi, Samy Badreddine, Kana Maruyama, Sucheendra K Palaniappan*, Tarek R Besold
Over the last few years Literature-based Discovery (LBD) has regained popularity as a means to enhance the scientific research process. The resurgent interest has spurred the development of supervised and semi-supervised machine learning models aimed at making previously imp...
What's Wrong with Gradient-based Complex Query Answering?
NESY, 2023 | Ouns El Harzli, Samy Badreddine, Tarek Besold
Multi-hop query answering on knowledge graphs is known to be a challenging computational task. Neurosymbolic approaches using neural link predictors have shown promising results but are still outperformed by combinatorial optimization methods on several benchmarks, including...
Logic Tensor Networks
ARTIFICIAL INTELLIGENCE, 2022 | Samy Badreddine, Artur d'Avila Garcez*, Luciano Serafini*, Michael Spranger
Attempts at combining logic and neural networks into neurosymbolic approaches have been on the increase in recent years. In a neurosymbolic system, symbolic knowledge assists deep learning, which typically uses a sub-symbolic distributed representation, to learn and reason a...
Extending Real Logic with Aggregate Functions
IJCLR, 2021 | Samy Badreddine, Michael Spranger
Real Logic is a recently introduced first-order language where formulas have fuzzy truth values in the interval [0, 1] and semantics are defined concretely with real domains. The Logic Tensor Networks (LTN) framework has applied Real Logic to many important AI tasks through ...