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Knowledge Organization Systems (KOS) in the Semantic Web: a multi-dimensional review


Since the Simple Knowledge Organization System (SKOS) specification and its SKOS eXtension for Labels (SKOS-XL) became formal W3C recommendations in 2009, a significant number of conventional Knowledge Organization Systems (KOS) (including thesauri, classification schemes, name authorities, and lists of codes and terms, produced before the arrival of the ontology-wave) have made their journeys to join the Semantic Web mainstream. This paper uses “LOD KOS” as an umbrella term to refer to all of the value vocabularies and lightweight ontologies within the Semantic Web framework. The paper provides an overview of what the LOD KOS movement has brought to various communities and users. These are not limited to the colonies of the value vocabulary constructors and providers, nor the catalogers and indexers who have a long history of applying the vocabularies to their products. The LOD dataset producers and LOD service providers, the information architects and interface designers, and researchers in sciences and humanities, are also direct beneficiaries of LOD KOS. The paper examines a set of the collected cases (experimental or in real applications) and aims to find the usages of LOD KOS in order to share the practices and ideas among communities and users. Through the viewpoints of a number of different user groups, the functions of LOD KOS are examined from multiple dimensions. This paper focuses on the LOD dataset producers, vocabulary producers, and researchers (as end-users of KOS).

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We want to thank all reviewers for their positive and constructive comments which helped to improve this paper. In addition, we thank all our co-organizers of former NKOS workshops and all participants of NKOS-related events for their continuously input and feedback which motivated us to write this paper. Supplementary materials (e.g., high-resolution figures) of this paper are available under

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Correspondence to Marcia Lei Zeng or Philipp Mayr.

Appendix A. User persona document example: Vocabulary Producer (VP)

Appendix A. User persona document example: Vocabulary Producer (VP)

Name Vocabulary Producer
Key VP
Sources Original sources Used for
LOV on Google+ VP-1
Getty Vocab Google Group!forum/gettyvocablod VP-1
LODLAM challenges and sessions VP-2
Research-based journal publications; conference and workshop presentations VP-2, VP-3, VP-4
Theses and dissertations VP-2, VP-3
GitHub entries such as OpenSKOS, NatLibFi/Skosmos, JSKOS VP-4
Social media sources: tweets, blogs, Facebook groups VP-2, VP-5
Informal interviews and local meetings VP-1, VP-2
Mailing lists within a user group VP-1
Tasks Vocabulary producers are involved in the development, maintenance, and enrichment of new and existing KOS in a wide range of scales (e.g., micro, satellite, unified, heterogeneous, extended, enriched, or other kinds). The tasks usually include:
   \(\bullet \)    Creating, developing;
   \(\bullet \)    Maintaining, enriching, extending, translating;
   \(\bullet \)    Integrating and unifying;
   \(\bullet \)    Transforming (e.g., making an ontology from a thesaurus);
   \(\bullet \)    Mapping with others;
   \(\bullet \)    Sharing, reusing, contributing;
   \(\bullet \)    Quality control and maintenance.
Content \(\bullet \)    Entries / instances—with all property components required, including semantic and linguistic, format requirements, following standards and best practices;
\(\bullet \)    URIs—with namespace of any entry from any source;
\(\bullet \)    Rights and contributors;
\(\bullet \)    Provenance data;
\(\bullet \)    Updates info (new concepts, terms, relations, sources, etc.);
\(\bullet \)    Samples, previews, feedback, issues;
\(\bullet \)    Related images;
\(\bullet \)    Sources and URIs of the related real things;
\(\bullet \)    Alignments coded with appropriate degrees.
Interactions \(\bullet \)    Working platforms (spreadsheet, local database, open tool, etc.);
\(\bullet \)    Desktops/mobile applications;
\(\bullet \)    Web sites (HTML, navigate-able);
\(\bullet \)    API-based services;
\(\bullet \)    SPARQL endpoints (with or without templates);
\(\bullet \)    Datasets.
Goals \(\bullet \)    Create and maintain high-quality vocabularies;
\(\bullet \)    Follow the vocabulary principles of user-warrant, literary-warrant, organizational warrant;
\(\bullet \)    Follow international standards for KOS structure, components, and interoperability;
\(\bullet \)    Comply with Linked Data principles;
\(\bullet \)    Enrich, extend, and update contents constantly;
\(\bullet \)    Share, reuse, and contribute (both in and out) in vocabulary productions.

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Zeng, M.L., Mayr, P. Knowledge Organization Systems (KOS) in the Semantic Web: a multi-dimensional review. Int J Digit Libr 20, 209–230 (2019).

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  • Linked Open Data
  • Knowledge Organization Systems
  • LOD KOS functions
  • Personas