Embeddings is a methodological approach and concept in data representation that maps high-dimensional, often discrete data points (such as words, graphs, or categories) into a lower-dimensional continuous vector space. This transformation is designed to capture and preserve meaningful relationships and structural properties inherent in the original data, enabling quantitative analysis, facilitating similarity comparisons, and serving as an effective input representation for various computational models and machine learning algorithms.