Survey on Semantic Interpretation of Tabular Data: Challenges and Directions
It addresses the problem of automating semantic interpretation of tabular data for knowledge-intensive applications, but it is incremental as a survey rather than a novel method.
This survey provides a comprehensive overview of Semantic Table Interpretation (STI), categorizing approaches with a taxonomy of 31 attributes, examining tools based on 12 criteria, and analyzing Gold Standards for evaluation.
Tabular data plays a pivotal role in various fields, making it a popular format for data manipulation and exchange, particularly on the web. The interpretation, extraction, and processing of tabular information are invaluable for knowledge-intensive applications. Notably, significant efforts have been invested in annotating tabular data with ontologies and entities from background knowledge graphs, a process known as Semantic Table Interpretation (STI). STI automation aids in building knowledge graphs, enriching data, and enhancing web-based question answering. This survey aims to provide a comprehensive overview of the STI landscape. It starts by categorizing approaches using a taxonomy of 31 attributes, allowing for comparisons and evaluations. It also examines available tools, assessing them based on 12 criteria. Furthermore, the survey offers an in-depth analysis of the Gold Standards used for evaluating STI approaches. Finally, it provides practical guidance to help end-users choose the most suitable approach for their specific tasks while also discussing unresolved issues and suggesting potential future research directions.