  In this paper we present a question answering system using a neural network
to interpret questions learned from the DBpedia repository. We train a
sequence-to-sequence neural network model with n-triples extracted from the
DBpedia Infobox Properties. Since these properties do not represent the natural
language, we further used question-answer dialogues from movie subtitles.
Although the automatic evaluation shows a low overlap of the generated answers
compared to the gold standard set, a manual inspection of the showed promising
outcomes from the experiment for further work.
