Towards generative semantic table interpretation

Semantic Table Interpretation (STI), or Semantic Table Annotation, is the process of understanding the semantics of tabular data with reference information identified in knowledge graphs (KG). In this paper, we first present insights gained from the design and implementation of DAGOBAH SL, a top performing STI system in state-of-the-art benchmarks, and we discuss the unsolved challenges that need to be addressed to make STI more effective in practice. Pre-trained generative Large Language Models (LLMs) have demonstrated their powerful versatility in tackling a broad spectrum of natural language understanding tasks. We envision their potential for improving STI systems. We describe several appealing research ideas that could lay the foundation for future development of Generative Semantic Table Interpretation.