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Deep Learning meets Knowledge Graphs for Scholarly Data Classification

Deep Learning meets Knowledge Graphs for Scholarly Data Classification

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: 417-421
Verlag: Association for Computing Machinery

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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. This study uses Knowledge Graphs as a source of these required external class definitions by identifying corresponding entities in DBpedia to improve the overall classification.

ISBN: 9781450383134
Download: Media:2021 - Deep Learning meets Knowledge Graphs for Scholarly Data Classification.pdf
DOI Link: 10.1145/3442442.3451361


Information Service Engineering