Bibliometrics and Mapping of Co-words in the Field of Linked Data

The research aims to visualize and analyze co-word network and thematic clusters in the field of linked data during 1986-2018.

The study is an applied research in terms of the purpose, which conducted by using co-word analysis as a methodology and descriptive approach. Clusters determined by three methods. VOS Viewer, SPSS, and UCINet were used for data analysis and network visualization.

The keywords linked data and semantic web in terms of  co-word pairs had the highest frequencies. Co-word clustering generated five clusters, while hierarchical clustering produced two clusters. The USA was the most productive country and the highest share of  documents published in various sub-categories of the Computer Sciences. Studies mostly published in health and cultural heritage contexts. The cluster core concepts of the semantic web was the most mature and   central cluster, while linked data usage in the context of cultural heritage was a well-developed but isolated cluster.

Conclustion:

 The results can identify underlying trends and core themes by highlighting thematic gaps to avoid duplicate studies. Policymakers, researchers, and designers of the semantic technologies can plan predictably to develop themes in balance for the future and increase the quality and quantity of scientific outputs.