TY - GEN

T1 - A Human-Friendly Query Generation Frontend for a Scientific Events Knowledge Graph

AU - Fathalla, Said

AU - Lange, Christoph

AU - Auer, Sören

N1 - Funding information: This work was co-funded by the European Research Council for the project ScienceGRAPH (Grant agreement ID: 819536).

PY - 2019/8/30

Y1 - 2019/8/30

N2 - 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.

AB - 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.

KW - EVENTSKG dataset

KW - Query builder

KW - Scientific events

KW - SPARQL endpoint

KW - User Interaction

U2 - 10.1007/978-3-030-30760-8_18

DO - 10.1007/978-3-030-30760-8_18

M3 - Conference contribution

AN - SCOPUS:85072873693

SN - 9783030307592

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 200

EP - 214

BT - Digital Libraries for Open Knowledge

A2 - Doucet, Antoine

A2 - Isaac, Antoine

A2 - Golub, Koraljka

A2 - Aalberg, Trond

A2 - Jatowt, Adam

PB - Springer Verlag

CY - Cham

T2 - 23rd International Conference on Theory and Practice of Digital Libraries, TPDL 2019

Y2 - 9 September 2019 through 12 September 2019

ER -