Journal of Data and Information Science ›› 2020, Vol. 5 ›› Issue (1): 93-118.doi: 10.2478/jdis-2020-0008

• Research Paper • Previous Articles    

FAIR + FIT: Guiding Principles and Functional Metrics for Linked Open Data (LOD) KOS Products

Marcia Lei Zeng(), Julaine Clunis   

  1. School of Information, Kent State University, Ohio, USA
  • Received:2020-01-18 Revised:2020-03-10 Accepted:2020-03-16 Online:2020-04-15 Published:2020-04-17
  • Contact: Marcia Lei Zeng


Purpose: To develop a set of metrics and identify criteria for assessing the functionality of LOD KOS products while providing common guiding principles that can be used by LOD KOS producers and users to maximize the functions and usages of LOD KOS products.

Design/methodology/approach: Data collection and analysis were conducted at three time periods in 2015-16, 2017 and 2019. The sample data used in the comprehensive data analysis comprises all datasets tagged as types of KOS in the Datahub and extracted through their respective SPARQL endpoints. A comparative study of the LOD KOS collected from terminology services Linked Open Vocabularies (LOV) and BioPortal was also performed.

Findings: The study proposes a set of Functional, Impactful and Transformable (FIT) metrics for LOD KOS as value vocabularies. The FAIR principles, with additional recommendations, are presented for LOD KOS as open data.

Research limitations: The metrics need to be further tested and aligned with the best practices and international standards of both open data and various types of KOS.

Practical implications: Assessment performed with FAIR and FIT metrics support the creation and delivery of user-friendly, discoverable and interoperable LOD KOS datasets which can be used for innovative applications, act as a knowledge base, become a foundation of semantic analysis and entity extractions and enhance research in science and the humanities.

Originality/value: Our research provides best practice guidelines for LOD KOS as value vocabularies.

Key words: Knowledge Organization Systems, Linked Open Data, FAIR, FIT, Semantic web