# Translating Questions into Answers using DBPedia n-triples

Source: [Translating Questions into Answers using DBPedia n-triples](<https://www.sota2.com/research/paper/68fa64291e209131cea0300c>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T20:57:11.341Z

Authors: Mihael Arcan

Published: 2018-03-07T23:29:31.000Z

## Abstract

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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1803.02914>)

[PDF](<https://arxiv.org/pdf/1803.02914.pdf>)