Currently, large databases of open bibliographic metadata have been created and made available online in the age of open science. These resources are easily findable, accessible, interoperable, and reusable thanks to their online hosting and their representation as RDF (Resource Description Framework) knowledge graphs, and could be easily aligned together thanks to a set of unique identifiers such as ORCID ID, Wikidata ID, and DOI. The integration of heterogeneous bibliographic data such as author information (e.g., ORCID and DBLP), citations (e.g., OpenCitations and Crossref), research findings (e.g., ORKG), and the semantic knowledge about prizes, countries, research venues, publications and institutions (e.g., Wikidata) can be useful to develop high-level interpretations for the research assessment of entities.The structured format of RDF knowledge graphs can allow the automatic generation of real-time research evaluation outputs through the use of programming tools (e.g., APIs) and query services (e.g., SPARQL) as well as the enrichment and validation of bibliographic information through the use of machine learning, logical constraints, bibliometric-enhanced information retrieval, and shape expressions.In this themed article collection, we are keen to include original research, opinions, and application notes about the use of linked open bibliographic data for supporting timely research evaluation.
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|Abstract||06 June 2022|
|Manuscript||05 August 2022|