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Abstract
Provenance is information about entities, activities, and people involved in producing a piece of data or a thing, which can be used to form assessments about the data or the thing's quality, reliability, or trustworthiness. PROV-DM is the conceptual data model that forms the basis for the W3C provenance (PROV) family of specifications. In this paper, we propose a new serialization for PROV in JSON called PROV-JSONLD. It provides a lightweight representation of PROV expressions in JSON, which is suitable to be processed by Web applications, while maintaining a natural encoding that is familiar with PROV practitioners. In addition, PROV-JSONLD exploits JSON-LD to define a semantic mapping that conforms to the PROV-O specification and, hence, the encoded PROV expressions can be readily processed as Linked Data. Finally, we show that the serialization is also efficiently processable in our evaluation. Overall, PROV-JSONLD is designed to be suitable for interchanging provenance information in Web and Linked Data applications, to offer a natural encoding of provenance for its targeted audience, and to allow for fast processing.
Original language | English |
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Title of host publication | Provenance and Annotation of Data and Processes |
Subtitle of host publication | 8th and 9th International Provenance and Annotation Workshop, IPAW 2020 + IPAW 2021, Virtual Event, July 19–22, 2021, Proceedings |
Publisher | Springer, Cham |
Pages | 51-67 |
Number of pages | 17 |
ISBN (Electronic) | 978-3-030-80960-7 |
ISBN (Print) | 978-3-030-80959-1 |
DOIs | |
Publication status | Published - 9 Jul 2021 |
Keywords
- Provenance
- PROV
- JSON
- JSON-LD
- Linked Data
- Standardisation
- Serialization
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Dive into the research topics of 'The PROV-JSONLD Serialization: A JSON-LD Representation for the PROV Data Model'. Together they form a unique fingerprint.Projects
- 1 Finished
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PLEAD: Provenance-driven and Legally-grounded Explanations for Automated Decisions
EPSRC Engineering and Physical Sciences Research Council
1/09/2019 → 31/03/2022
Project: Research