Designing scientific SPARQL queries using autocompletion by snippets

Abstract : SPARQL is the standard query language used to access RDF linked data sets available on the Web. However, designing a SPARQL query can be a tedious task, even for experienced users. This is often due to imperfect knowledge by the user of the ontologies involved in the query. To overcome this problem, a growing number of query editors offer autocompletion features. Such features are nevertheless limited and mostly focused on typo checking. In this context, our contribution is four-fold. First, we analyze several autocompletion features proposed by the main editors, highlighting the needs currently not taken into account while met by a user community we work with, scientists. Second, we introduce the first (to our knowledge) autocompletion approach able to consider snippets (fragments of SPARQL query) based on queries expressed by previous users, enriching the user experience. Third, we introduce a usable, open and concrete solution able to consider a large panel of SPARQL autocompletion features that we have implemented in an editor. Last but not least, we demonstrate the interest of our approach on real biomedical queries involving services offered by the Wikidata collaborative knowledge base.
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https://hal.archives-ouvertes.fr/hal-01874780
Contributor : Karima Rafes <>
Submitted on : Saturday, September 15, 2018 - 8:26:23 AM
Last modification on : Monday, March 4, 2019 - 11:36:08 AM
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  • HAL Id : hal-01874780, version 2

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Karima Rafes, Serge Abiteboul, Sarah Cohen-Boulakia, Bastien Rance. Designing scientific SPARQL queries using autocompletion by snippets. 14th IEEE International Conference on eScience, Oct 2018, Amsterdam, Netherlands. ⟨hal-01874780v2⟩

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