Mapping a decade of linked data progress through co-word analysis

Abstract

Linked data describes a method of publishing structured data which it can be interlinked and become more effective through semantic queries. This enables data from different sources to be connected and queried. It builds upon standard web technologies such as HTTP, RDF and URIs. This method helps human readers to share information in a way that can be read automatically by computers. Regarding the importance of Linked data, the main aim of this article is visualizing scientific mapping of linked data to show its progress through one decade. The scientometric study employs hierarchical cluster analysis, strategic diagrams and network analysis to map and visualize the linked data landscape of the "Scopus" publications through the use of co-word analysis. The study quantifies and describes the thematic evolution of the field based on a total of 717 Scopus articles and their associated 19977 keywords published between 1970s and 2014. According to the results the thematic visualization and the clusters show most concepts concentrated around computer related terms, such as big data; cloud computing semantic data; semantic technologies; semantic web; artificial intelligence; computer programming; semantic search, etc. In addition, we found that in recent years after librarians and information scientists doing researches in linked data on the behalf of computer scientist the “user” studies became important.

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No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 7 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.