SPARQL as a Foreign Language

Tommaso Soru, Edgard Marx, Diego Moussallem, Gustavo Publio, Andre Valdestilhas, Diego Esteves, Ciro Baron Neto: SPARQL as a Foreign Language.
In: J. Fern√°ndez, S. Hellmann (eds.): Proceedings of the Posters and Demos Track of the 13th International Conference on Semantic Systems - SEMANTiCS2017,
Amsterdam, The Netherlands, September 11-14, 2017,
online: http://ceur-ws.org/Vol-2044/paper14

Recently, the Linked Data Cloud has achieved a size of more than 100 billion facts pertaining to a multitude of domains. However, accessing this information has been significantly challenging for lay users. Approaches to problems such as Question Answering on Linked Data and Link Discovery have notably played a role in increasing information access. These approaches are often based on handcrafted and/or statistical models derived from data observation. Recently, Deep Learning architectures based on Neural Networks called seq2seq have shown to achieve the state-of-the-art results at translating sequences into sequences. In this direction, we propose Neural SPARQL Machines, end-to-end deep architectures to translate any natural language expression into sentences encoding SPARQL queries. Our preliminary results, restricted on selected DBpedia classes, show that Neural SPARQL Machines are a promising approach for Question Answering on Linked Data, as they can deal with known problems such as vocabulary mismatch and perform graph pattern composition.


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