The Current State of SKOS Vocabularies on the Web

  • Nor Azlinayati Abdul Manaf
  • Sean Bechhofer
  • Robert Stevens
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7295)


We present a survey of the current state of Simple Knowledge Organization System (SKOS) vocabularies on the Web. Candidate vocabularies were gathered through collections and web crawling, with 478 identified as complying to a given definition of a SKOS vocabulary. Analyses were then conducted that included investigation of the use of SKOS constructs; the use of SKOS semantic relations and lexical labels; and the structure of vocabularies in terms of the hierarchical and associative relations, branching factors and the depth of the vocabularies. Even though SKOS concepts are considered to be the core of SKOS vocabularies, our findings were that not all SKOS vocabularies published explicitly declared SKOS concepts in the vocabularies. Almost one-third of the SKOS vocabularies collected fall into the category of term lists, with no use of any SKOS semantic relations. As concept labelling is core to SKOS vocabularies, a surprising find is that not all SKOS vocabularies use SKOS lexical labels, whether skos:prefLabel or skos:altLabel, for their concepts. The branching factors and maximum depth of the vocabularies have no direct relationship to the size of the vocabularies. We also observed some common modelling slips found in SKOS vocabularies. The survey is useful when considering, for example, converting artefacts such as OWL ontologies into SKOS, where a definition of typicality of SKOS vocabularies could be used to guide the conversion. Moreover, the survey results can serve to provide a better understanding of the modelling styles of the SKOS vocabularies published on the Web, especially when considering the creation of applications that utilize these vocabularies.


Resource Description Framework Taxonomy Category Automatic Reasoner Modelling Slip Associative Relation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Nor Azlinayati Abdul Manaf
    • 1
    • 2
  • Sean Bechhofer
    • 1
  • Robert Stevens
    • 1
  1. 1.School of Computer ScienceThe University of ManchesterUnited Kingdom
  2. 2.MIMOS Berhad, Technology Park MalaysiaKuala LumpurMalaysia

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