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Improving access to science for social good

: Ali, M.; Vahdati, S.; Singh, S.; Dasgupta, S.; Lehmann, J.


Cellier, P.:
Machine Learning and Knowledge Discovery in Databases. Proceedings. Pt.I : International Workshops of ECML PKDD 2019, Würzburg, Germany, September 16-20, 2019
Cham: Springer Nature, 2020 (Communications in computer and information science 1167)
ISBN: 978-3-030-43822-7 (Print)
ISBN: 978-3-030-43823-4 (Online)
European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) <2019, Würzburg>
Fraunhofer IAIS ()

One of the major goals of science is to make the world socially a good place to live. The old paradigm of scholarly communication through publishing has generated enormous amount of heterogeneous data and metadata. However, most of the scientific results are not easily discoverable, in particular those results which benefit social good and are also targeted by non-scientists. In this paper, we showcase a knowledge graph embedding (KGE) based recommendation system to be used by students involved in activities aiming at social good. The proposed recommendation system has been trained on a scholarly knowledge graph constructed for this specific goal. The obtained results highlight that the KGEs successfully encoded the structure of the KG, and therefore, our system could provide valuable recommendations.