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HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles


HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles



Published: 2021

Buchtitel: In Proc. of 1st International Workshop on Scientific Knowledge: Representation, Discovery, and Assessment (Sci-K 2021). Co-located with The Web Conference 2021.
Seiten: 436-440
Verlag: Association for Computing Machinery

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BibTeX

Kurzfassung
A huge number of scholarly articles published every day in different domains makes it hard for the experts to organize and stay updated with the new research in a particular domain. This study gives an overview of a new approach, HierClasSArt, for knowledge aware hierarchical classification of the scholarly articles for mathematics into a predefined taxonomy. The method uses combination of neural networks and Knowledge Graphs for better document representation along with the meta-data information. This position paper further discusses the open problems about incorporation of new articles and evolving hierarchies in the pipeline. Mathematics domain has been used as a use-case.

ISSN: 9781450383134
Download: Media:2021 - HierClasSArt Knowledge-Aware Hierarchical Classification of Scholarly Articles.pdf
DOI Link: 10.1145/3442442.3451365



Forschungsgruppe

Information Service Engineering


Forschungsgebiet