Combining information on structure and content to automatically annotate natural science spreadsheets

Our case study results show that combining information on structural properties of spreadsheet tables with lexical matching to external vocabularies results in higher precision and recall of annotation of individual terms. We show that the semantic characterization of blocks of spreadsheet terms is an essential first step in the identification of relations between cells in a table. As such, the annotation approaches presented in this study provide the basic information that is needed to construct the domain model of scientific spreadsheets.