  Semantic parsing is the process of mapping a natural language sentence into a
formal representation of its meaning. In this work we use the neural network
approach to transform natural language sentence into a query to an ontology
database in the SPARQL language. This method does not rely on handcraft-rules,
high-quality lexicons, manually-built templates or other handmade complex
structures. Our approach is based on vector space model and neural networks.
The proposed model is based in two learning steps. The first step generates a
vector representation for the sentence in natural language and SPARQL query.
The second step uses this vector representation as input to a neural network
(LSTM with attention mechanism) to generate a model able to encode natural
language and decode SPARQL.
