@inproceedings{ostankov-etal-2014-linkedhealthanswers,
title = "{L}inked{H}ealth{A}nswers: Towards Linked Data-driven Question Answering for the Health Care Domain",
author = {Ostankov, Artem and
R{\"o}hrbein, Florian and
Waltinger, Ulli},
editor = "Calzolari, Nicoletta and
Choukri, Khalid and
Declerck, Thierry and
Loftsson, Hrafn and
Maegaard, Bente and
Mariani, Joseph and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Ninth International Conference on Language Resources and Evaluation ({LREC}'14)",
month = may,
year = "2014",
address = "Reykjavik, Iceland",
publisher = "European Language Resources Association (ELRA)",
url = "http://www.lrec-conf.org/proceedings/lrec2014/pdf/902_Paper.pdf",
pages = "2613--2620",
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.",
}
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%0 Conference Proceedings
%T LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain
%A Ostankov, Artem
%A Röhrbein, Florian
%A Waltinger, Ulli
%Y Calzolari, Nicoletta
%Y Choukri, Khalid
%Y Declerck, Thierry
%Y Loftsson, Hrafn
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC’14)
%D 2014
%8 May
%I European Language Resources Association (ELRA)
%C Reykjavik, Iceland
%F ostankov-etal-2014-linkedhealthanswers
%X 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.
%U http://www.lrec-conf.org/proceedings/lrec2014/pdf/902_Paper.pdf
%P 2613-2620
Markdown (Informal)
[LinkedHealthAnswers: Towards Linked Data-driven Question Answering for the Health Care Domain](http://www.lrec-conf.org/proceedings/lrec2014/pdf/902_Paper.pdf) (Ostankov et al., LREC 2014)
ACL