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The European Bureau of Library, Information and Documentation Associations is an independent umbrella association of library, information, documentation and archive associations and institutions in Europe.

Scientific Knowledge Graphs 2020

Date: 25 August 2020 to 28 August 2020
Location: Lyon, France

In the last decade, we experienced an urgent need for a flexible, context-sensitive, fine-grained, and machine-actionable representation of scholarly knowledge and corresponding infrastructures for knowledge curation, publishing and processing. Such technical infrastructures are becoming increasingly popular in representing scholarly knowledge as structured, interlinked, and semantically rich Scientific Knowledge Graphs (SKG). Knowledge graphs are large networks of entities and relationships, usually expressed in W3C standards such as OWL and RDF. SKGs focus on the scholarly domain and describe the actors (e.g., authors, organizations), the documents (e.g., publications, patents), and the research knowledge (e.g., research topics, tasks, technologies) in this space as well as their reciprocal relationships. These resources provide substantial benefits to researchers, companies, and policymakers by powering several data-driven services for navigating, analysing, and making sense of research dynamics. Some examples include Microsoft Academic Graph (MAG), Open Academic Graph (combining MAG and AMiner), ScholarlyData, PID Graph, Open Research Knowledge Graph, OpenCitations, and OpenAIRE research graph. Current challenges in this area include: i) the design of ontologies able to conceptualise scholarly knowledge, ii) (semi-)automatic extraction of entities and concepts, integration of information from heterogeneous sources, identification of duplicates, finding connections between entities, and iii) the development of new services using this data, that allow to explore this information, measure research impact and accelerate science. This workshop aims at bringing together researchers and practitioners from different fields (including, but not limited to, Digital Libraries, Information Extraction, Machine Learning, Semantic Web, Knowledge Engineering, Natural Language Processing, Scholarly Communication, and Bibliometrics) in order to explore innovative solutions and ideas for the production and consumption of Scientific Knowledge Graphs (SKGs).

 

 
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