Over the course of the year, we've had the pleasure of working with many talented researchers from across the globe. As 2025 draws to a close, we take a look back at some of the excellent blog posts from our contributors. This work contributes to the field of explainable AI by developing a novel neural network that can be directly transformed into logic. The authors explore the tensions between creators and AI-generated content through a survey of 459 artists. Find out more about work presented at ECAI on generating a comprehensive biomedical knowledge graph question answering dataset.

The biomedical domain is a complex network of interconnected knowledge, encompassing genetics, diseases, drugs, and biological processes. While knowledge graphs (KGs) excel at organizing and linking this information, their complexity often makes them difficult for users to query. Ideally, users should be able to ask questions in natural language and receive precise answers directly from the KG, without needing specialized query expertise. However, enabling deep learning-based systems to query KGs using natural language remains a major challenge. Existing biomedical knowledge graph question answering (BioKGQA) datasets are small and limited in scope, typically containing only a few hundred question answering (QA) pairs.