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This study aims to explore the reusing of Dublin core metadata initiative (DCMI) metadata terms on the linked open vocabulary (LOV) platform in the linked data environment to offer a better understanding of the reusing behaviour during the process of vocabulary construction and further explain why DC has become a popular vocabulary.

The authors selected LOV, as a typical linked data platform. The SPARQL language was used to acquire and parse data to examine the reuse types of DCMI terms, the reuse distribution of classes and properties in different semantic relation types among vocabularies, the subject and size of the reused vocabularies and the correlation between vocabulary reuse and data set reuse.

Results showed that DCMI metadata terms were reused by 83.7% of LOV vocabularies and became the core nodes on the vocabulary-linked network. Among the six relationships between vocabularies and the DCMI metadata terms, the metadata relationship is the most frequently used. DCMI metadata terms are reused by small- and medium-sized vocabularies and are not limited to subject domain.

This is one of the first studies focussing on the roles of DCMI metadata terms in vocabulary reusing. Furthermore, it provides a systematic view of how these DCMI terms participate in the construction of other vocabularies and in features of reused vocabularies.

The purpose of this paper is to identify the concepts, component parts and relationships between vocabularies, linked data and knowledge graphs (KGs) from the perspectives of data and knowledge transitions.

This paper uses conceptual analysis methods. This study focuses on distinguishing concepts and analyzing composition and intercorrelations to explore data and knowledge transitions.

Vocabularies are the cornerstone for accurately building understanding of the meaning of data. Vocabularies provide for a data-sharing model and play an important role in supporting the semantic expression of linked data and defining the schema layer; they are also used for entity recognition, alignment and linkage for KGs. KGs, which consist of a schema layer and a data layer, are presented as cubes that organically combine vocabularies, linked data and big data.

This paper first describes the composition of vocabularies, linked data and KGs. More importantly, this paper innovatively analyzes and summarizes the interrelatedness of these factors, which comes from frequent interactions between data and knowledge. The three factors empower each other and can ultimately empower the Semantic Web.