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Deep Learning meets Knowledge Graphs for Scholarly Data Classification

Hoppe, Fabian ORCID iD icon; Dessı̀, Danilo; Sack, Harald


The amount of scientific literature continuously grows, which poses an increasing challenge for researchers to manage, find and explore research results. Therefore, the classification of scientific work is widely applied to enable the retrieval, support the search of suitable reviewers during the reviewing process, and in general to organize the existing literature according to a given schema. The automation of this classification process not only simplifies the submission process for authors, but also ensures the coherent assignment of classes. However, especially fine-grained classes and new research fields do not provide sufficient training data to automatize the process. Additionally, given the large number of not mutual exclusive classes, it is often difficult and computationally expensive to train models able to deal with multi-class multi-label settings. To overcome these issues, this work presents a preliminary Deep Learning framework as a solution for multi-label text classification for scholarly papers about Computer Science. The proposed model addresses the issue of insufficient data by utilizing the semantics of classes, which is explicitly provided by latent representations of class labels. ... mehr

DOI: 10.1145/3442442.3451361
Zitationen: 10
Zitationen: 8
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Publikationstyp Proceedingsbeitrag
Publikationsdatum 19.04.2021
Sprache Englisch
Identifikator ISBN: 978-1-4503-8313-4
KITopen-ID: 1000134438
Erschienen in WWW '21: Companion Proceedings of the Web Conference 2021, April 2021
Veranstaltung 30. The Web Conference (WWW 2021), Ljubljana, Slowenien, 19.04.2021 – 23.04.2021
Verlag Association for Computing Machinery (ACM)
Seiten 417–421
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