ISWC metadata

As it has been tradition in the previous years, all metadata of the conference will be made publicly available.

This is both an announcement of the final release of the ISWC2017 metadata and a call for action to apply your own tools for visualising or exploring the ISWC2017 conference data. The final dump is already available at the link below. In order to put this data to use, we invite submissions of apps/tools which visualise and/or support exploration of the data. The ISWC2017 organisation team intends to link to your applications from the ISWC2017 Website and provide some publicity, so this provides an opportunity to showcase your RDF/Linked Data visualization tool to the ISWC2017 audience. Should you intend to submit or have any questions, please get in touch with the ISWC2017 metadata chairs.

Detailed info and data download: https://iswc2017.ai.wu.ac.at/calls/iswc2017-metadata-call-for-visualisations/

Note: This metadata will be published also integrated in scholarlydata.org.

Metadata Chairs

Visualizations

Linked Data Reactor (LD-R)

Contact: Ali Khalili

Enrichment and visualization of the ISWC2017 metadata using the Linked Data Reactor (LD-R) framework (http://ld-r.org). For enrichment of data, authors are linked with their previous Semantic Web-related contributions from http://data.semanticweb.org. Author’s affiliations were linked to the GRID knowledge base (http://grid.ac) which includes data about more than 76K research-related organizations. Using the LD-R annotator UI and DBpedia Spotlight, named entities were extracted

Access

SemSpect

Contact: Thorsten Liebig

With SemSpect the ISWC metadata – or any other RDF/OWL data sets – are
visualized following the approach of “overview first and details on
demand”. The tools allows to explore large knowledge graphs by expanding
relationships between objects on demand.

Access

Sparklis

Contact: Sébastien Ferré

Sparklis is a guided query builder in natural language that allows people to explore and query any SPARQL endpoint with all the power of SPARQL 1.1 and without any knowledge of SPARQL. Results are presented as tables, and also on maps. Sparklis also includes the YASGUI editor to let advanced users access and modify the SPARQL translation of the query.

A number of pre-configured datasets are proposed – including ISWC 2017 metadata – and the Examples page contains 100+ example queries over several datasets, including a few YouTube screencasts.

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