Abstract
Linked open data movements have been followed successfully in different domains; thus, the number of publicly available RDF datasets and linked data based applications have increased considerably during the last decade. Particularly in Life Sciences, RDF datasets are utilized to represent diverse concepts, e.g., proteins, genes, mutations, diseases, drugs, and side effects. Albeit publicly accessible, the exploration of these RDF datasets requires the understanding of their main characteristics, e.g., their vocabularies and the connections among them. To tackle these issues, we present and demonstrate FedSDM, a semantic data manager for federations of RDF datasets. Attendees will be able to explore the relationships among the RDF datasets in a federation, as well as the characteristics of the RDF classes included in each RDF dataset (https://github.com/SDM-TIB/FedSDM).
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Acknowledgements
This work has been funded by the EU H2020 RIA under the Marie Skłodowska-Curie grant agreement No. 642795 (WDAqua) and EU H2020 Programme for the projects with GA No. 727658 (IASIS).
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Endris, K.M., Vidal, ME., Auer, S. (2019). FedSDM: Semantic Data Manager for Federations of RDF Datasets. In: Auer, S., Vidal, ME. (eds) Data Integration in the Life Sciences. DILS 2018. Lecture Notes in Computer Science(), vol 11371. Springer, Cham. https://doi.org/10.1007/978-3-030-06016-9_8
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