Managing Provenance Data in Knowledge Graph Management Platforms

被引:0
作者
Kleinsteuber, Erik [1 ]
Al Mustafa, Tarek [1 ]
Zander, Franziska [1 ,2 ]
König-Ries, Birgitta [1 ,2 ,3 ]
Babalou, Samira [1 ,2 ]
机构
[1] Institute for Computer Science, Friedrich Schiller University Jena, Jena, Germany
[2] German Center for Integrative Biodiversity Research (iDiv), Halle-Jena-Leipzig, Germany
[3] Michael-Stifel-Center for Data-Driven and Simulation Science, Friedrich Schiller University Jena, Jena, Germany
关键词
Semantic Web;
D O I
10.1007/s13222-023-00463-0
中图分类号
学科分类号
摘要
Knowledge Graphs (KGs) present factual information about domains of interest. They are used in a wide variety of applications and in different domains, serving as powerful backbones for organizing and extracting knowledge from complex data. In both industry and academia, a variety of platforms have been proposed for managing Knowledge Graphs. To use the full potential of KGs within these platforms, it is essential to have proper provenance management to understand where certain information in a KG stems from. This plays an important role in increasing trust and supporting open science principles. It enables reproducibility and updatability of KGs. In this paper, we propose a framework for provenance management of KG generation within a web portal. We present how our framework captures, stores, and retrieves provenance information. Our provenance representation is aligned with the standardized W3C Provenance Ontology. Through our framework, we can rerun the KG generation process over the same or different source data. With this, we support four applications: reproducibility, altered rerun, undo operation, and provenance retrieval. In summary, our framework aligns with the core principles of open science. By promoting transparency and reproducibility, it enhances the reliability and trustworthiness of research outcomes. © The Author(s) 2024.
引用
收藏
页码:43 / 52
页数:9
相关论文
共 64 条
  • [1] Nickel M(2015)A review of relational machine learning for knowledge graphs IEEE 104 11-33
  • [2] Murphy K(2022)Rtx-kg2: a system for building a semantically standardized knowledge graph for translational biomedicine BMC Bioinform 23 400-1125
  • [3] Tresp V(2019)metaphactory: a platform for knowledge graph management SW 10 1109-543
  • [4] Gabrilovich E(2022)End-to-end provenance representation for the understandability and reproducibility of scientific experiments using a semantic approach J Biomed Semant 13 1-16
  • [5] Wood E(2018)A systematic review of provenance systems Knowl Inf Syst 57 495-906
  • [6] Glen AK(2013)Prov-dm: The prov data model W3c Recomm 14 15-21
  • [7] Kvarfordt LG(2017)A survey on provenance: what for? What form? What from? VLDB J 26 881-756
  • [8] Womack F(2008)Provenance for computational tasks: a survey Comput Sci Eng 10 11-undefined
  • [9] Acevedo L(2011)The open provenance model core specification (v1. 1) Future Gener Comput Syst 27 743-undefined
  • [10] Yoon TS(2009)Owl 2 web ontology language primer W3c Recomm 27 123-undefined