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.
Cite
@article{arxiv.1803.02914,
title = {Translating Questions into Answers using DBPedia n-triples},
author = {Mihael Arcan},
journal= {arXiv preprint arXiv:1803.02914},
year = {2018}
}