Work

Spread2RML

Year: 2021

Type: conference-paper

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.

Source: ACM International Conference on Knowledge Capture (K-CAP)

DOI: 10.1145/3460210.3493544

Authors: Markus Schröder, Christian Jilek, Andreas Dengel

Institutions: University of Kaiserslautern, German Research Centre for Artificial Intelligence

Language: English

Topic: Semantic Web and Ontologies

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funder: Bundesministerium für Bildung und Forschung

Award: G8254455972