Well-Stratified Linked Data for Well-Behaved Data Citation
Dario De Nart, Dante Degl'Innocenti, Marco Peressotti

TL;DR
This paper proposes a minimal, type-based approach to linked data citation that simplifies provenance and subset identification, advocating for external citation handling with specialized languages.
Contribution
It introduces a simple type system for linked data citation, reducing complexity compared to reification, and suggests external citation management with dedicated languages.
Findings
Type system effectively captures citation requirements
Type checking simplifies provenance and subset identification
External citation languages are recommended for flexibility
Abstract
In this paper we analyse the functional requirements of linked data citation and identify a minimal set of operations and primitives needed to realize such task. Citing linked data implies solving a series of data provenance issues and finding a way to identify data subsets. Those two tasks can be handled defining a simple type system inside data and verifying it with a type checker, which is significantly less complex than interpreting reified RDF statements and can be implemented in a non data invasive way. Finally we suggest that data citation should be handled outside of the data, possibly with an ad-hoc language.
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Taxonomy
TopicsSemantic Web and Ontologies · Scientific Computing and Data Management · Biomedical Text Mining and Ontologies
