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Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs

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Sikos, Leslie F., Philp, Dean·2020·Data Science and Engineering
DOI 10.1007/s41019-020-00118-0
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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.

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  • Surveys and frameworks for provenance in knowledge graphs emphasize the importance of recording user metadata about who created or modified a memory, when changes occurred, and the associated confidence levels.

    “Surveys and frameworks for provenance in knowledge graphs emphasize the importance of recording user metadata about who created or modified a memory, when changes occurred, and the associated confidence levels [35].”

    From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [35]

  • Provenance-aware models, such as CRDTs and knowledge graph provenance frameworks, allow systems to track the origin, modification history, and confidence of memory entries, supporting conflict resolution.

    “Provenance-aware models, such as CRDTs and knowledge graph provenance frameworks allow systems to track the origin, modification history, and confidence of memory entries, supporting conflict resolution [33,35].”

    From Memory Fabric for Conversational AI Agents: Enabling Shared and Persistent Memory Across Users (2025) · cites this paper as [35]

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Metadata

Authors
Sikos, L. F., & Philp, D.
Affiliations
  • Edith Cowan University (AU)
  • Defence Science and Technology Group (AU)
Corresponding author
Sikos, Leslie F.
ORCID
Sikos, Leslie F.
Venue
Data Science and Engineering
Publisher
Springer Science and Business Media LLC
Published
2020-05-08
Volume
5
Issue
3
Pages
293-316
ISSN
  • 2364-1185 (ISSN-L)
  • 2364-1541
DOI
10.1007/s41019-020-00118-0
OpenAlex
W3023112361
Cited by
60
References
1
Citations by year
2026: 4 · 2025: 10 · 2024: 11 · 2023: 13 · 2022: 12 · 2021: 8 · 2020: 2
FWCI
3.44
Type
journal article
Language
en
Open access
Licence
Locations
  • Data Science and Engineering (publishedVersion, cc-by)
  • Australasian Journal of Paramedicine (submittedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
  • Physical Sciences › Computer Science › Information Systems › Library Science and Information Systems
  • Social Sciences › Decision Sciences › Information Systems and Management › Scientific Computing and Data Management
Keywords
provenance, RDF, knowledge graphs, knowledge representation, data model, annotation framework, knowledge organization systems, algebra, blank nodes, structured data

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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