A Provenance Management Framework for Knowledge Graph Generation in a Web Portal

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BTW 2023
Gesellschaft für Informatik e.V.
Knowledge Graphs (KGs) are the semantic backbone for a wide variety of applications in different domains. In recent years, different web portals providing relevant functionalities for managing KGs have been proposed. An important functionality such portals is provenance data management of KG generation process. Capturing, storing, and accessing provenance data efficiently are complex problems. Solutions to these problems vary widely depending on many factors like the computational environment, computational methods, desired provenance granularity, and much more. In this paper, we present one possible solution: a new framework to capture coarse-grained workflow provenance of KGs during creation in a web portal. We capture the necessary information of the KG generation process; store and retrieve the provenance data using standard functionalities of relational databases. Our captured workflow can be rerun over the same or different input source data. With this, the framework can support four different applications of provenance data: (i) reproduce the KG, (ii) create a new KG with an existing workflow, (iii) undo the executed tools and adapt the provenance data accordingly, and (iv) retrieve the provenance data of a KG.
Kleinsteuber, Erik; Babalou, Samira; König-Ries, Birgitta (2023): A Provenance Management Framework for Knowledge Graph Generation in a Web Portal. BTW 2023. DOI: 10.18420/BTW2023-65. Bonn: Gesellschaft für Informatik e.V.. ISBN: 978-3-88579-725-8. pp. 951-963. Dresden, Germany. 06.-10. März 2023