LAUSR

Tables are probably the most natural way to represent relational data in various media and formats. They store a large number of valuable facts that could be utilized for question answering, knowledge base population, natural language generation, and other applications. However, many tables are not accompanied by semantics for the automatic interpretation of the information they present. Table Understanding (TU) aims at recovering the missing semantics that enables the extraction of facts from tables. This problem covers a range of issues from table detection in document images to semantic table interpretation with the help of external knowledge bases. To date, the TU research has been ongoing on for 30 years. Nevertheless, there is no common point of view on the scope of TU; the terminology still needs agreement and unification. In recent years, science and technology have shown a rapidly increasing interest in TU. Nowadays, it is especially important to check the meaning of this research problem once again. This article gives a comprehensive characterization of the TU problem, including a description of its subproblems, tasks, subtasks, and applications. It also discusses the common limitations used in the existing problem statements and proposes some directions for further research that would help overcome the corresponding limitations.