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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Vydané v:	Data Science and Engineering Ročník 5; číslo 3; s. 293 - 316
Hlavní autori:	Sikos, Leslie F., Philp, Dean
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Berlin/Heidelberg Springer Berlin Heidelberg 01.09.2020 Springer Springer Nature B.V SpringerOpen
Predmet:	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
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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.
AbstractList	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. 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.
Audience	Academic
Author	Sikos, Leslie F. Philp, Dean
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Snippet	Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of... Abstract Expressing machine-interpretable statements in the form of subject-predicate-object triples is a well-established practice for capturing semantics of...
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SubjectTerms	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
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Title	Provenance-Aware Knowledge Representation: A Survey of Data Models and Contextualized Knowledge Graphs
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Volume	5
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