Semantic Association Rule

Semantic association rule learning aims to discover meaningful relationships between data features, going beyond simple correlations to incorporate semantic context and knowledge. Current research focuses on integrating machine learning techniques, such as autoencoders and neural networks (including transformers and BiLSTMs), with semantic information from sources like ontologies and knowledge graphs to improve the efficiency and interpretability of rule extraction, particularly from high-dimensional data like time series and images. This enhanced approach promises more accurate and generalizable insights across diverse applications, including e-commerce recommendations, industrial process monitoring, and image retrieval.