Call for contribution to the research topic: Linked Open Bibliographic Data for Real-time Research Assessment

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.

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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Submission Deadlines

Abstract06 June 2022
Manuscript05 August 2022
Mohamed Ben Aouicha
Mohamed Ben Aouicha
Associate professor in computer science

My research interests concern information retrieval, semantic technologies, social media analytics, knowledge representation, Big Data and graph embedding.

Mohamed Ali Hadj Taieb
Mohamed Ali Hadj Taieb
Assistant professor in computer science

My research interests include semantic similarity, semantic relatedness, knowledge representation, Big Data, social media, data management systems and graph embedding.

Houcemeddine Turki
Houcemeddine Turki
Medical student

My research interests include the development of a large-scale framework for using open resources and semantic technologies for driving biomedical informatics and research evaluation at a low cost.