Table Annotation

Table annotation, the process of semantically enriching tabular data, aims to improve machine understanding and utilization of this ubiquitous data format. Current research focuses on leveraging large language models (LLMs) and generative AI for automated annotation, alongside heuristic-based methods, to address the scalability challenges of manual annotation. These efforts are driving the development of larger, more diverse annotated datasets and improved algorithms for tasks such as entity disambiguation, schema-driven information extraction, and text-to-SQL parsing. The resulting advancements have significant implications for various applications, including data integration, knowledge discovery, and the development of more robust machine learning models for tabular data.