TY - GEN
T1 - SPARQL as a foreign language
AU - Soru, Tommaso
AU - Marx, Edgard
AU - Moussallem, Diego
AU - Publio, Gustavo
AU - Valdestilhas, André
AU - Esteves, Diego
AU - Neto, Ciro Baron
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85045919177&partnerID=8YFLogxK
M3 - Conference contribution
VL - 2044
T3 - CEUR Workshop Proceedings
BT - SEMPDS 2017 - Proceedings of the Posters and Demos Track of the 13th International Conference on Semantic Systems SEMANTiCS 2017, co-located with the 13th International Conference on Semantic Systems, SEMANTiCS 2017
A2 - Hellmann, S.
A2 - Fernandez, J.D.
PB - CEUR-WS
T2 - Posters and Demos Track of the 13th International Conference on Semantic Systems SEMANTiCS, SEMPDS 2017
Y2 - 11 September 2017 through 14 September 2017
ER -