View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Research Online @ ECU Edith Cowan University Research Online ECU Publications Post 2013 1-1-2020 Provenance-aware knowledge representation: A survey of data models and contextualized knowledge graphs Leslie F. Sikos Edith Cowan University,
[email protected] Dean Philp Follow this and additional works at: https://ro.ecu.edu.au/ecuworkspost2013 Part of the Computer Sciences Commons 10.1007/s41019-020-00118-0 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 This Journal Article is posted at Research Online. https://ro.ecu.edu.au/ecuworkspost2013/8373 Data Science and Engineering https://doi.org/10.1007/s41019-020-00118-0 Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs Leslie F. Sikos1 · Dean Philp2 Received: 1 April 2019 / Revised: 13 February 2020 / Accepted: 3 March 2020 © The Author(s) 2020 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 author- itative. 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 stan- dard compliance, formal semantics, tuple type, vocabulary term usage, blank nodes, provenance granularity, and scalability.