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SPARQL is a highly powerful query language for an ever-growing number ofLinked Data resources and Knowledge Graphs. Using it requires a certainfamiliarity with the entities in the domain to be queried as well as expertisein the language's syntax and semantics, none of which average human web userscan be assumed to possess. To overcome this limitation, automaticallytranslating natural language questions to SPARQL queries has been a vibrantfield of research. However, to this date, the vast success of deep learningmethods has not yet been fully propagated to this research problem. This papercontributes to filling this gap by evaluating the utilization of eightdifferent Neural Machine Translation (NMT) models for the task of translatingfrom natural language to the structured query language SPARQL. Whilehighlighting the importance of high-quantity and high-quality datasets, theresults show a dominance of a CNN-based architecture with a BLEU score of up to98 and accuracy of up to 94%.

Neural Machine Translating from Natural Language to SPARQL