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HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles

Alam, Mehwish; Biswas, Russa ORCID iD icon; Chen, Yiyi; Dessì, Danilo; Gesese, Genet Asefa ORCID iD icon; Hoppe, Fabian ORCID iD icon; Sack, Harald


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.

Verlagsausgabe §
DOI: 10.5445/IR/1000134160
Veröffentlicht am 21.06.2021
DOI: 10.1145/3442442.3451365
Zitationen: 3
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsmonat/-jahr 04.2021
Sprache Englisch
Identifikator ISBN: 978-1-4503-8313-4
KITopen-ID: 1000134160
Erschienen in WWW '21: The Web Conference 2021, 19th - 23rd April 2021, Ljubljana Slovenia
Veranstaltung 30. The Web Conference (WWW 2021), Ljubljana, Slowenien, 19.04.2021 – 23.04.2021
Verlag Association for Computing Machinery (ACM)
Seiten 436–440
Vorab online veröffentlicht am 19.04.2021
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