Enabling ad-hoc reuse of private data repositories through schema extraction

• Published in: Journal of biomedical semantics Vol. 11; no. 1; pp. 1 - 15
• Main Authors: Gleim, Lars Christoph, Karim, Md Rezaul, Zimmermann, Lukas, Kohlbacher, Oliver, Stenzhorn, Holger, Decker, Stefan, Beyan, Oya
• Format: Journal Article
• Language: English
• Published: London BioMed Central 08.07.2020; BMC
• Subjects: Algorithms; Authoring; Computer architecture; Datasets; Electronic health records; Explicit knowledge; Information sharing; Information systems; Interoperability; Linked data; Ontology; Personal health; Privacy; Queries; RDF; Repositories; Researchers; Schema extraction; Semantic web; Semantics; SPARQL; Web Ontology Language-OWL
• ISSN: 2041-1480; 2041-1480
• DOI: 10.1186/s13326-020-00223-z

Background Sharing sensitive data across organizational boundaries is often significantly limited by legal and ethical restrictions. Regulations such as the EU General Data Protection Rules (GDPR) impose strict requirements concerning the protection of personal and privacy sensitive data. Therefore new approaches, such as the Personal Health Train initiative, are emerging to utilize data right in their original repositories, circumventing the need to transfer data. Results Circumventing limitations of previous systems, this paper proposes a configurable and automated schema extraction and publishing approach, which enables ad-hoc SPARQL query formulation against RDF triple stores without requiring direct access to the private data. The approach is compatible with existing Semantic Web-based technologies and allows for the subsequent execution of such queries in a safe setting under the data provider’s control. Evaluation with four distinct datasets shows that a configurable amount of concise and task-relevant schema, closely describing the structure of the underlying data, was derived, enabling the schema introspection-assisted authoring of SPARQL queries. Conclusions Automatically extracting and publishing data schema can enable the introspection-assisted creation of data selection and integration queries. In conjunction with the presented system architecture, this approach can enable reuse of data from private repositories and in settings where agreeing upon a shared schema and encoding a priori is infeasible. As such, it could provide an important step towards reuse of data from previously inaccessible sources and thus towards the proliferation of data-driven methods in the biomedical domain.