Semantic Table

Semantic table interpretation (STI) aims to automatically understand the meaning of tabular data, enriching it with semantic annotations to improve data analysis and knowledge graph construction. Current research focuses on leveraging large language models (LLMs) and retrieval-augmented generation (RAG) techniques, alongside more traditional methods like similarity-based approaches and heuristic algorithms, to achieve this, often exploring the use of metadata alone when data is unavailable. The ability to accurately interpret tables' semantics is crucial for various applications, enabling more robust and explainable data analytics workflows and facilitating knowledge discovery from diverse data sources.