Publication
Spread2RML: Constructing Knowledge Graphs by Predicting RML Mappings on Messy Spreadsheets
Markus Schröder; Christian Jilek; Andreas Dengel
In: The Eleventh International Conference on Knowledge Capture. International Conference on Knowledge Capture (K-Cap-2021), December 2-3, Virtual, ISBN 978-1-4503-8457-5/21/12, ACM, 2021.
Abstract
The RDF Mapping Language (RML) allows to map semi-structured data to RDF knowledge graphs. Besides CSV, JSON and XML, this also includes the mapping of spreadsheet tables. Since spreadsheets have a complex data model and can become rather messy, their mapping creation tends to be very time consuming. In order to reduce such efforts, this paper presents Spread2RML which predicts RML mappings on messy spreadsheets. This is done with an extensible set of RML object map templates which are applied for each column based on heuristics. In our evaluation, three datasets are used ranging from very messy synthetic data to spreadsheets from data.gov which are less messy. We obtained first promising results especially with regard to our approach being fully automatic and dealing with rather messy data.