Concept Embeddings

Concept embeddings represent abstract concepts as numerical vectors, aiming to capture semantic relationships and facilitate knowledge reasoning within machine learning models. Current research focuses on developing methods to learn these embeddings from various data sources (text, images, knowledge graphs), often employing techniques like contrastive learning, hierarchical clustering, and neural-symbolic integration within architectures such as concept bottleneck models and those leveraging large language models. This work is significant because improved concept embeddings enhance the interpretability and explainability of complex models, leading to better performance in tasks like anomaly detection, concept recommendation, and ontology completion, while also enabling verification and validation of AI systems.