Abstract
Metadata of scientific events has become increasingly available on the Web, albeit often as raw data in various formats, disregarding its semantics and interlinking relations. This leads to restricting the usability of this data for, e.g., subsequent analyses and reasoning. Therefore, there is a pressing need to represent this data in a semantic representation, i.e., Linked Data. We present the new release of the EVENTSKG dataset, comprising comprehensive semantic descriptions of scientific events of eight computer science communities. Currently, EVENTSKG is a 5-star dataset containing metadata of 73 top-ranked event series (almost 2,000 events) established over the last five decades. The new release is a Linked Open Dataset adhering to an updated version of the Scientific Events Ontology, a reference ontology for event metadata representation, leading to richer and cleaner data. To facilitate the maintenance of EVENTSKG and to ensure its sustainability, EVENTSKG is coupled with a Java API that enables users to add/update events metadata without going into the details of the representation of the dataset. We shed light on events characteristics by analyzing EVENTSKG data, which provides a flexible means for customization in order to better understand the characteristics of renowned CS events.
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These communities have been identified using the ACM Computing Classification System: https://dl.acm.org/ccs/ccs.cfm.
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A namespace look-up tool for RDF developers: http://prefix.cc/.
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the date of the oldest events in the dataset.
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public-lod@w3.org.
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open-science@lists.okfn.org.
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Acknowledgments
This work has been supported by the European Union through the H2020 ERC ScienceGRAPH project (GA no. 819536). First author would like to thank the Ministry of Higher Education of Egypt for the financial support to conduct this work.
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Fathalla, S., Lange, C., Auer, S. (2019). EVENTSKG: A 5-Star Dataset of Top-Ranked Events in Eight Computer Science Communities. In: Hitzler, P., et al. The Semantic Web. ESWC 2019. Lecture Notes in Computer Science(), vol 11503. Springer, Cham. https://doi.org/10.1007/978-3-030-21348-0_28
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DOI: https://doi.org/10.1007/978-3-030-21348-0_28
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