Spreadsheets are widely used in various business tasks, and contain
amounts of valuable data. However, spreadsheet tables are usually
organized in a semi-structured way, and contain complicated semantic
structures, e.g., header types and relations among headers. Lack of
documented semantic table structures, existing data analysis and
error detection tools can hardly understand spreadsheet tables. Therefore,
identifying semantic table structures in spreadsheet tables is of
great importance, and can
greatly promote various analysis tasks on spreadsheets.

In this paper, we propose Tasi (Table
structure identification) to
automatically identify semantic table structures in spreadsheets.
Based on the contents, styles, and spatial locations in table
headers, Tasi adopts a multi-classifier to predict potential header
types and relations, and then integrates all header types and
relations into consistent semantic table structures. We further
propose TasiError, to detect spreadsheet errors based on the
identified semantic table structures by Tasi. Our experiments on
real-world spreadsheets show that, Tasi can precisely identify
semantic table structures in spreadsheets, and TasiError can detect
real-world spreadsheet errors with higher precision (75.2%) and
recall (82.9%) than existing approaches.