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

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...

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Bibliographic Details
Published in:Data Science and Engineering Vol. 5; no. 3; pp. 293 - 316
Main Authors: Sikos, Leslie F., Philp, Dean
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020
Springer
Springer Nature B.V
SpringerOpen
Subjects:
Algorithm Analysis and Problem Complexity
Annotations
Artificial Intelligence
Chemistry and Earth Sciences
Computer Science
Contextual knowledge graph
Data Mining and Knowledge Discovery
Data models
Database Management
Graphical representations
Knowledge bases (artificial intelligence)
Knowledge representation
Physics
RDF data model
RDF provenance
RDF reification alternatives
Semantics
Statistics for Engineering
Surveys
Systems and Data Security
Web applications
Australia
New York
Israel
Contextual knowledge graph
RDF data model
RDF provenance
RDF reification alternatives
ISSN:2364-1185, 2364-1541
Online Access:Get full text
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Summary: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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ISSN:2364-1185
2364-1541
DOI:10.1007/s41019-020-00118-0