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EVENTSKG: A knowledge graph representation for top-prestigious computer science events metadata

: Fathalla, Said; Lange, Christoph

Postprint urn:nbn:de:0011-n-5159866 (341 KByte PDF)
MD5 Fingerprint: 273a0f53a7bc4e0d75cd8f2d5c3894e8
Created on: 1.12.2018

Nguyen, T.H.:
Computational collective intelligence. 10th International Conference, ICCCI 2018. Proceedings. Pt.1 : Bristol, UK, September 5-7, 2018
Cham: Springer International Publishing, 2018 (Lecture Notes in Computer Science 11055)
ISBN: 978-3-319-98442-1 (Print)
ISBN: 978-3-319-98443-8 (Online)
ISBN: 978-3-319-98444-5
International Conference on Computational Collective Intelligence (ICCCI) <10, 2018, Bristol>
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
scientific event dataset; Scholarly Communication; linked data; Semantic Web; metadata analysis; knowledge graph

Digitization has made the preparation of manuscripts as well as the organization of scientific events considerably easier and efficient. In addition, data about scientific events is increasingly published on the Web, albeit often as raw dumps in unstructured formats, immolating its semantics and relationships to other data and thus restricting the reusability of the data for, e.g., subsequent analyses. Therefore, there is a great demand to represent this data in a semantic representation using Semantic Web technologies. In this paper, we present the EVENTSKG dataset to offer a comprehensive semantic descriptions of scientific events of six computer science communities for 40 top-prestigious event series over the last five decades. We created a new, publicly available and improved release of the EVENTSKG dataset as a unified knowledge graph based on our Scientific Events Ontology (SEO). It is of primary interest to event organizers, as it helps them to assess the progress of their event over time and compare it to competing events. Furthermore, it helps potential authors looking for venues to publish their work. We shed light on these events by analyzing the EVENTSKG data.