Document (#43602)

Author

Binding, C.
Gnoli, C.
Tudhope, D.

Title

Migrating a complex classification scheme to the semantic web : expressing the Integrative Levels Classification using SKOS RDF

Source

Journal of documentation. 77(2021) no.4, S.926-945

Year

2021

Abstract

Purpose The Integrative Levels Classification (ILC) is a comprehensive "freely faceted" knowledge organization system not previously expressed as SKOS (Simple Knowledge Organization System). This paper reports and reflects on work converting the ILC to SKOS representation. Design/methodology/approach The design of the ILC representation and the various steps in the conversion to SKOS are described and located within the context of previous work considering the representation of complex classification schemes in SKOS. Various issues and trade-offs emerging from the conversion are discussed. The conversion implementation employed the STELETO transformation tool. Findings The ILC conversion captures some of the ILC facet structure by a limited extension beyond the SKOS standard. SPARQL examples illustrate how this extension could be used to create faceted, compound descriptors when indexing or cataloguing. Basic query patterns are provided that might underpin search systems. Possible routes for reducing complexity are discussed. Originality/value Complex classification schemes, such as the ILC, have features which are not straight forward to represent in SKOS and which extend beyond the functionality of the SKOS standard. The ILC's facet indicators are modelled as rdf:Property sub-hierarchies that accompany the SKOS RDF statements. The ILC's top-level fundamental facet relationships are modelled by extensions of the associative relationship - specialised sub-properties of skos:related. An approach for representing faceted compound descriptions in ILC and other faceted classification schemes is proposed.

Content

Vgl.: https://doi.org/10.1108/JD-10-2020-0167.

Theme

Semantic Web
Semantische Interoperabilität

Object

ILC
SKOS

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5. Panzer, M.; Zeng, M.L.: Modeling classification systems in SKOS : Some challenges and best-practice (2009) 0.19

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