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Introduction
The linked data (LD) initiative is the latest achievement in the natural evolution of the semantic web (Allemang and Hendler, 2011). The interest of libraries, museums, and archives (the cultural heritage context) is expanding to LD because it helps produce data innovatively and with a standardized format to be reusable and discoverable (Guerrini and Possemato, 2013). Generally, LDs are the data published on the web in a machine-readable format (Raza et al., 2019), wherein meaning is precisely defined and linked to other datasets on the web (e.g. DBpedia) and can be linked by other databases as well. In recent years, LD has been the focus of research in library and information science (LIS) in addition to computer science (Southwick, 2015). Berners-Lee introduced the concept of LD in 2006. Published data are based on principles that facilitate the link between databases, elements, and vocabularies (Moulaison and Million, 2014). Vocabularies play a significant role in promoting the semantic expression of LD by defining a schema layer for entity recognition and interconnection for knowledge graphs (Jia, 2021). Theoretically, the LD method refers to a set of best practices for structuring and linking the data available on the web (Bizer et al., 2009).
There is a strong potential in LD that provides solutions for heterogeneous web objects and many library issues, e.g. increasing web searches, authority control, classification, data flexibility, and ambiguity (Zengenene, 2013). Thus, given the growth and importance of this field, surveying the thematic clusters and analyzing topic maturity are essential measures.
This research aimed to visualize and analyze the co-word network and thematic clusters of the intellectual structure in the field of LD during 1900–2021. The following research questions were addressed: