Inference Approach to Enhance a Portuguese Open Information Extraction

Cleiton Fernando Lima Sena, Rafael Glauber, Daniela Barreiro Claro

2017

Abstract

Open Information Extraction (Open IE) enables the extraction of facts in large quantities of texts written in natural language. Despite the fact that almost research has been doing in English texts, methods and techniques for other languages have been less frequent. However, those languages other than English correspond to 48% of content available on websites around the world. In this work, we propose a method for extracting facts in Portuguese without pre-determining the types of the facts. Additionally, we increased the quantity of those extracted facts by the use of an inference approach. Our inference method is composed of two issues: a transitive and a symmetric mechanism. To the best of our knowledge, this is the first time that inference approach is used to extract facts in Portuguese texts. Our proposal allowed an increase of 36% in quantity of valid facts extracted in a Portuguese Open IE system, and it is compatible in the quality of facts with English approaches.

Paper Citation

in Harvard Style

Lima Sena C., Glauber R. and Barreiro Claro D. (2017). Inference Approach to Enhance a Portuguese Open Information Extraction . In Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-758-247-9, pages 442-451. DOI: 10.5220/0006338204420451

in Bibtex Style

@conference{iceis17,
author={Cleiton Fernando Lima Sena and Rafael Glauber and Daniela Barreiro Claro},
title={Inference Approach to Enhance a Portuguese Open Information Extraction},
booktitle={Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2017},
pages={442-451},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006338204420451},
isbn={978-989-758-247-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 19th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Inference Approach to Enhance a Portuguese Open Information Extraction
SN - 978-989-758-247-9
AU - Lima Sena C.
AU - Glauber R.
AU - Barreiro Claro D.
PY - 2017
SP - 442
EP - 451
DO - 10.5220/0006338204420451