HierClasSArt: Knowledge-Aware Hierarchical Classification of Scholarly Articles

Companion Proceedings of the Web Conference 2021

Alam, Mehwish and Biswas, Russa and Chen, Yiyi and Dessì, Danilo and Gesese, Genet Asefa and Hoppe, Fabian and 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.

@inproceedings{alam_hierclassart_2021,
  title = {{HierClasSArt}: {Knowledge}-{Aware} {Hierarchical} {Classification} of {Scholarly} {Articles}},
  booktitle = {Companion {Proceedings} of the {Web} {Conference} 2021},
  author = {Alam, Mehwish and Biswas, Russa and Chen, Yiyi and Dessì, Danilo and Gesese, Genet Asefa and Hoppe, Fabian and Sack, Harald},
  year = {2021},
  doi = {10.1145/3442442.3451365},
  pages = {436–440},
  keywords = {Deep Learning, Scholarly Data, Knowledge Graphs, Hierarchical Classification}
}