7 citations · 10 across the 6 of their papers we have counts for
6 papers
The RAIL Principles for Neurosymbolic AI: Reasoning, Assurances, Interfacing and Learning
Agnese Chiatti, Michael Cochez, Cristina Cornelio +14
Neurosymbolic AI systems that integrate machine learning and symbolic reasoning are rapidly gaining attention. They complement the data-intensive statistical approaches of neural n…
Enhancing Structural Mapping with LLM-derived Abstractions for Analogical Reasoning in Narratives
Mohammadhossein Khojasteh, Yifan Jiang, Stefano De Giorgis +2
Analogical reasoning is a key driver of human generalization in problem-solving and argumentation. Yet, analogies between narrative structures remain challenging for machines. Cogn…
Reviewing Clinical Knowledge in Medical Large Language Models: Training and Beyond
Qiyuan Li, Haijiang Liu, Caicai Guo +6
The large-scale development of large language models (LLMs) in medical contexts, such as diagnostic assistance and treatment recommendations, necessitates that these models possess…
Semantic Web and Creative AI -- A Technical Report from ISWS 2023
Raia Abu Ahmad, Reham Alharbi, Roberto Barile +74
The International Semantic Web Research School (ISWS) is a week-long intensive program designed to immerse participants in the field. This document reports a collaborative effort p…
Aligning Generalisation Between Humans and Machines
Filip Ilievski, Barbara Hammer, Frank van Harmelen +22
Recent advances in AI -- including generative approaches -- have resulted in technology that can support humans in scientific discovery and forming decisions, but may also disrupt…
A-NeSI: A Scalable Approximate Method for Probabilistic Neurosymbolic Inference
Emile van Krieken, Thiviyan Thanapalasingam, Jakub M. Tomczak +2
We study the problem of combining neural networks with symbolic reasoning. Recently introduced frameworks for Probabilistic Neurosymbolic Learning (PNL), such as DeepProbLog, perfo…