Semantically Mapping Science (SMS) Platform

Ali Khalili, Peter van den Besselaar, Idrissou Al Koudous, Klaas Andries de Graaf, Frank van Harmelen

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Up to now, STI (Science, Technology, Innovation) studies
are either rich but small scale (qualitative case studies) or large scale
and under-complex – because they generally use only a single dataset
like Patstat, Scopus, WoS (Web of Science), OECD STI indicators, etc.,
and therefore deploying only a few variables – determined by the data
available. However, progress in the STI research field (and the social
sciences in general) depends in our view on the ability to do large-scale
studies with often many variables specified by relevant theories. There is
a need for studies which are at the same time big and rich. The aim of
the Semantically Mapping Science (SMS) platform is to enable enriching
and integration of heterogeneous data, ranging from tabular statistical
data to unstructured data found on the Web, in order to exploit the huge
amount of data that are ‘out there’ in an innovative and meaningful way
Original languageEnglish
Title of host publicationSemSci 2017: Enabling Open Semantic Science
Subtitle of host publicationProceedings of the First Workshop on Enabling Open Semantic Science co-located with 16th International Semantic Web Conference (ISWC 2017) Vienna, Austria, October 21st, 2017
EditorsDaniel Garijo, Willem Robert van Hage, Tomi Kauppinen, Tobias Kuhn, Jun Zhao
PublisherCEUR Workshop Proceedings
Pages1-6
Number of pages6
Publication statusPublished - 2017

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