Concept Extraction

Concept extraction focuses on automatically identifying and extracting meaningful concepts from text or images, aiming to bridge the gap between raw data and human-understandable knowledge. Current research emphasizes improving the accuracy and interpretability of concept extraction, exploring methods like concept bottleneck models, rule-based data augmentation, and leveraging large language models (LLMs) and graph neural networks (GNNs) for various domains, including biomedical text, patents, and industrial quality control images. These advancements are crucial for enhancing the explainability of complex AI systems and enabling more effective knowledge discovery and utilization across diverse scientific and industrial applications.