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

• 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

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...