Combining information on structure and content to automatically annotate natural science spreadsheets. Issue 103 (July 2017)

• Record Type: Journal Article
• Authors: de Vos, Martine; Wielemaker, Jan; Rijgersberg, Hajo; Schreiber, Guus; Wielinga, Bob; Top, Jan
• Is Part Of: International journal of human-computer studies. Issue 103(2017)
• Page Start: 63
• Page End: 76
• Publication Date: 2017-07
• Subjects: Domain Model -- Implicit knowledge -- Methodology -- Spreadsheets -- Units of measure -- Vocabularies; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2017.02.006
• Languages: English
• ISSNs: 1071-5819

Abstract

Abstract: In this paper we propose several approaches for automatic annotation of natural science spreadsheets using a combination of structural properties of the tables and external vocabularies. During the design process of their spreadsheets, domain scientists implicitly include their domain model in the content and structure of the spreadsheet tables. However, this domain model is essential to unambiguously interpret the spreadsheet data. The overall objective of this research is to make the underlying domain model explicit, to facilitate evaluation and reuse of these data. We present our annotation approaches by describing five structural properties of natural science spreadsheets, that may pose challenges to annotation, and at the same time, provide additional information on the content. For example, the main property we describe is that, within a spreadsheet table, semantically related terms are grouped in rectangular blocks. For each of the five structural properties we suggest an annotation approach, that combines heuristics on the property with knowledge from external vocabularies. We evaluate our approaches in a case study, with a set of existing natural science spreadsheets, by comparing the annotation results with a baseline based on purely lexical matching. 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. Abstract : Highlights: We describe five structural properties of scientific spreadsheet tables. Within a spreadsheet table, semantically related terms are grouped in blocks. We annotate tables using information on their structure and external vocabularies. Including information on table structure improves annotation of spreadsheet terms. We identify relations within a table by semantically categorizing blocks of terms.