Constructing a Knowledge Graph for Open Statistical Data

Tracking #: 2706-3920

This paper is currently under review
Enayat Rajabi1
Rishi Midha
Devanshika Ghosh

Responsible editor: 
Guest Editors KG Validation and Quality

Submission type: 
Full Paper
Open Government Data has been published widely by different governments to be used by public and data consumers. The majority of published datasets are statistical. Transforming Open Government Data into Knowledge graphs bridge the semantic gap and give machines the power to logically infer and reason. Through this paper, a knowledge graph is proposed for Open Statistical Data. An RDF-based knowledge graph with a rule-based ontology are presented on this paper. A case study on Nova Scotia Open Data (a provincial Open Data portal in Canada) is also presented. The proposed knowledge graph can be used on any statistical Open Data and can bring all provincial Open Government Data under a single umbrella. The knowledge graph was tested and underwent a quality check process. The study shows that the integration of statistical data from multiple sources using ontologies and interlinking features of Semantic Web potentially enables the performance of advanced data analytics and leads to the production of valuable data sources and it generates a dense knowledge graph with cross-dimensional information and data. The ontology designed to develop the graph adheres to best practices and standards thereby allowing for expansion, modification and flexible re-use.
Full PDF Version: 
Under Review