LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain
Abstract:
This paper presents Linked Health Answers, a natural language question answering systems that utilizes health data drawn from the Linked Data Cloud. The contributions of this paper are three-fold: Firstly, we review existing state-of-the-art NLP platforms and components, with a special focus on components that allow or support an automatic SPARQL construction. Secondly, we present the implemented architecture of the Linked Health Answers systems. Thirdly, we propose an statistical bootstrap approach for the identification and disambiguation of RDF-based predicates using a machine learning-based classifier. The evaluation focuses on predicate detection in sentence statements, as well as within the scenario of natural language questions.
Title: LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain
Authors: Artem Ostankov, Florian Rohrbein, Ulli Waltinger
Pub/Conf: Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
BibTeX:
@inproceedings{OSTANKOV14.902.L14-1691,
author = {Artem Ostankov and Florian R{\"o}hrbein and Ulli Waltinger},
url = {http://www.lrec-conf.org/proceedings/lrec2014/pdf/902_Paper.pdf},
note = {ACL Anthology Identifier: L14-1691},
title = {LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain},
booktitle = {Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)},
year = {2014},
month = {May},
date = {26-31},
address = {Reykjavik, Iceland},
editor = {Nicoletta Calzolari and Khalid Choukri and Thierry Declerck and Hrafn Loftsson and Bente Maegaard and Joseph Mariani and Asuncion Moreno and Jan Odijk and Stelios Piperidis},
publisher = {European Language Resources Association (ELRA)},
isbn = {978-2-9517408-8-4},
language = {english},
pages = {2613--2620}
}
