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A Human-friendly Query Generation Frontend for a Scientific Events Knowledge Graph

: Fathalla, Said; Lange, Christoph; Auer, Soeren

Postprint urn:nbn:de:0011-n-5617623 (3.5 MByte PDF)
MD5 Fingerprint: c4bb69c261856bae85c5a8a918808c54
The original publication is available at
Created on: 30.08.2020

Doucet, A.:
Digital Libraries for Open Knowledge. 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019. Proceedings : Oslo, Norway, September 9-12, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11799)
ISBN: 978-3-030-30759-2 (Print)
ISBN: 978-3-030-30760-8 (Online)
International Conference on Theory and Practice of Digital Libraries (TPDL) <23, 2019, Oslo>
European Commission EC
H2020; 819536; ScienceGRAPH
Knowledge Graph based Representation, Augmentation and Exploration of Scholarly Communication
Conference Paper, Electronic Publication
Fraunhofer IAIS ()
scientific events; SPARQL endpoint; query builder; user interaction; EVENTSKG dataset

Recently, semantic data have become more distributed. Available datasets should serve non-technical as well as technical audience. This is also the case with our EVENTSKG dataset, a comprehensive knowledge graph about scientific events, which serves the entire scientific and library community. A common way to query such data is via SPARQL queries. Non-technical users, however, have difficulties with writing SPARQL queries, because it is a time-consuming and error-prone task, and it requires some expert knowledge. This opens the way to natural language interfaces to tackle this problem by making semantic data more accessible to a wider audience, i.e., not restricted to experts. In this work, we present SPARQL-AG, a human-Friendly front-end that automatically generates and executes SPARQL queries for querying EVENTSKG. SPARQL-AG helps potential semantic data consumers, including non-experts and experts, by generating SPARQL queries, ranging from simple to complex ones, using an interactive web interface. The eminent feature of SPARQL-AG is that users neither need to know the schema of the knowledge graph being queried nor to learn the SPARQL syntax, as SPARQL-AG offers them a familiar and intuitive interface for query generation and execution. It maintains separate clients to query three public SPARQL endpoints when asking for particular entities. The service is publicly available online and has been extensively tested.