@inproceedings{57921b61165e486fb69bfe5f94a175af,
title = "HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles",
abstract = "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.",
keywords = "Deep Learning, Hierarchical Classification, Knowledge Graphs, Scholarly Data",
author = "Mehwish Alam and Russa Biswas and Yiyi Chen and Danilo Dess{\`i} and Gesese, \{Genet Asefa\} and Fabian Hoppe and Harald Sack",
note = "Publisher Copyright: {\textcopyright} 2021 ACM.; 30th Companion of the World Wide Web Conference, WWW 2021 ; Conference date: 19-04-2021 Through 23-04-2021",
year = "2021",
month = jun,
day = "3",
doi = "10.1145/3442442.3451365",
language = "English",
series = "The Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021",
publisher = "Association for Computing Machinery, Inc",
number = "03-06-21",
pages = "436--440",
booktitle = "The Web Conference 2021 - Companion of the World Wide Web Conference, WWW 2021",
edition = "03-06-21",
}