Document Type

Journal Article

Publication Title

Data Science and Engineering

Publisher

Springer

School

School of Science / ECU Security Research Institute

RAS ID

31622

Comments

Sikos, L. F., & Philp, D. (2020). Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs. Data Science and Engineering. https://doi.org/10.1007/s41019-020-00118-0

Abstract

Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of structured data. However, the standard used for representing these triples, RDF, inherently lacks the mechanism to attach provenance data, which would be crucial to make automatically generated and/or processed data authoritative. This paper is a critical review of data models, annotation frameworks, knowledge organization systems, serialization syntaxes, and algebras that enable provenance-aware RDF statements. The various approaches are assessed in terms of standard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability. This can be used to advance existing solutions and help implementers to select the most suitable approach (or a combination of approaches) for their applications. Moreover, the analysis of the mechanisms and their limitations highlighted in this paper can serve as the basis for novel approaches in RDF-powered applications with increasing provenance needs.

DOI

10.1007/s41019-020-00118-0

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

Research Themes

Securing Digital Futures

Priority Areas

Artificial intelligence and autonomous systems

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