Table Representation Learning

Table representation learning is an exciting field that focuses on understanding the structure and relationships within tabular data. This can involve learning embeddings for individual columns or entire tables, and can be used for various applications such as data discovery, data validation, and data integration.

One key aspect of table representation learning is understanding the semantics of columns, which can be used to generate metadata and help with tasks like table comprehension and data discovery.

By accurately representing columns and their relationships, table representation learning can help improve machine learning models and enable more complex analysis of tabular data.

X::Semantic Type Detection