Building a knowledge graph with automatically acquired publication classifications
Author(s)
Issue Date
November 19, 2019
Publisher
euroCRIS
Type
Presentation
Abstract
Research information systems represent an optimal data source for research analyses of universities. In order to guarantee these analysis qualities, it is necessary to provide a data structure that covers a large information content. An important part of this is the classification of researchers into research categories in order to build up research profiles. Since such a classification process is very time-consuming, it is necessary that such a classification takes place automatically. For automation, the research information system of Leipzig University (leuris) has available as text data source the research papers with titles and abstracts, as well as partly the full text papers. Using state of the art text analytical methods, we generate a Topic Model which can assign one or more of its Topics to the individual publications. By linking the publications with the authors, a knowledge graph can be built up, which provides a good structure for detailed search queries. One of the key features of the knowledge graph is the ability to extract research profiles of researchers. Leveraging these opportunities, it is possible to find researchers with similar research interests and promote collaboration, if they have not been aware that there are other researchers doing research in the same direction.
Description
12 slides.-- Presentation delivered within the session on 'Research classifications'
Live demo on automated research classification assignment for EU-funded projects available at https://sven-bl.shinyapps.io/Visualization/
URI
https://dspacecris.eurocris.org/handle/11366/1222
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Name
euroCRIS_MuensterSMM_Automated_research_classification_Leipzig_Blanck.pdf
Description
PDF presentation
Size
435.3 KB
Format
Adobe PDF
Checksum (MD5)
b17a7d535f700db0b660a04386b48481
Conference(s)