Medical semantic question answering framework on RDF data cubes

Usman Akhtar; Jamil Hussain; Sungyoung Lee

doi:10.1007/978-3-319-66188-9_23

Medical semantic question answering framework on RDF data cubes

Usman Akhtar
, Jamil Hussain
, Sungyoung Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

In this paper, we have proposed a framework to support the semantic question answering over the RDF data cube that is published according to the Linked Open Data (LOD) principles. As statistical data published all over the Internet there is a need to empowers the non-experts to query in the form of the natural language. But, the existing question answering system unable to support query on the statistical data in the form of the RDF cube. The current research is motivated by the need of the clinical organizations, who wish to develop a platform for analyzing the clinical data across multiple clinical sites. Linked open data (LOD) provides a support to published statistical data in the form of the RDF cube. Our proposed framework will provide a support to interact in the form of the natural language question answering that will produce the SPARQL query to extract the answer from the RDF data cube. In future, we will develop the benchmark to calculate the accuracy of the answer.

Original language	English
Title of host publication	Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings
Editors	Bessam Abdulrazak, Hamdi Aloulou, Mounir Mokhtari
Publisher	Springer Verlag
Pages	255-260
Number of pages	6
ISBN (Print)	9783319661872
DOIs	https://doi.org/10.1007/978-3-319-66188-9_23
Publication status	Published - 2017
Event	15th International Conference on Smart Homes and Health Telematics, ICOST 2017 - Paris, France Duration: 29 Aug 2017 → 31 Aug 2017

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	10461 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Smart Homes and Health Telematics, ICOST 2017
Country/Territory	France
City	Paris
Period	29/08/17 → 31/08/17

Bibliographical note

Publisher Copyright:
© 2017, Springer International Publishing AG.

Keywords

Question answering framework
RDF data cubes
Semantic question answering (SQA)

Access to Document

10.1007/978-3-319-66188-9_23

Cite this

Akhtar, U., Hussain, J., & Lee, S. (2017). Medical semantic question answering framework on RDF data cubes. In B. Abdulrazak, H. Aloulou, & M. Mokhtari (Eds.), Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings (pp. 255-260). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10461 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-66188-9_23

Akhtar, Usman ; Hussain, Jamil ; Lee, Sungyoung. / Medical semantic question answering framework on RDF data cubes. Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings. editor / Bessam Abdulrazak ; Hamdi Aloulou ; Mounir Mokhtari. Springer Verlag, 2017. pp. 255-260 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we have proposed a framework to support the semantic question answering over the RDF data cube that is published according to the Linked Open Data (LOD) principles. As statistical data published all over the Internet there is a need to empowers the non-experts to query in the form of the natural language. But, the existing question answering system unable to support query on the statistical data in the form of the RDF cube. The current research is motivated by the need of the clinical organizations, who wish to develop a platform for analyzing the clinical data across multiple clinical sites. Linked open data (LOD) provides a support to published statistical data in the form of the RDF cube. Our proposed framework will provide a support to interact in the form of the natural language question answering that will produce the SPARQL query to extract the answer from the RDF data cube. In future, we will develop the benchmark to calculate the accuracy of the answer.",

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author = "Usman Akhtar and Jamil Hussain and Sungyoung Lee",

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Akhtar, U, Hussain, J & Lee, S 2017, Medical semantic question answering framework on RDF data cubes. in B Abdulrazak, H Aloulou & M Mokhtari (eds), Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10461 LNCS, Springer Verlag, pp. 255-260, 15th International Conference on Smart Homes and Health Telematics, ICOST 2017, Paris, France, 29/08/17. https://doi.org/10.1007/978-3-319-66188-9_23

Medical semantic question answering framework on RDF data cubes. / Akhtar, Usman; Hussain, Jamil; Lee, Sungyoung.
Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings. ed. / Bessam Abdulrazak; Hamdi Aloulou; Mounir Mokhtari. Springer Verlag, 2017. p. 255-260 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10461 LNCS).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Akhtar, Usman

AU - Hussain, Jamil

AU - Lee, Sungyoung

PY - 2017

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N2 - In this paper, we have proposed a framework to support the semantic question answering over the RDF data cube that is published according to the Linked Open Data (LOD) principles. As statistical data published all over the Internet there is a need to empowers the non-experts to query in the form of the natural language. But, the existing question answering system unable to support query on the statistical data in the form of the RDF cube. The current research is motivated by the need of the clinical organizations, who wish to develop a platform for analyzing the clinical data across multiple clinical sites. Linked open data (LOD) provides a support to published statistical data in the form of the RDF cube. Our proposed framework will provide a support to interact in the form of the natural language question answering that will produce the SPARQL query to extract the answer from the RDF data cube. In future, we will develop the benchmark to calculate the accuracy of the answer.

AB - In this paper, we have proposed a framework to support the semantic question answering over the RDF data cube that is published according to the Linked Open Data (LOD) principles. As statistical data published all over the Internet there is a need to empowers the non-experts to query in the form of the natural language. But, the existing question answering system unable to support query on the statistical data in the form of the RDF cube. The current research is motivated by the need of the clinical organizations, who wish to develop a platform for analyzing the clinical data across multiple clinical sites. Linked open data (LOD) provides a support to published statistical data in the form of the RDF cube. Our proposed framework will provide a support to interact in the form of the natural language question answering that will produce the SPARQL query to extract the answer from the RDF data cube. In future, we will develop the benchmark to calculate the accuracy of the answer.

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings

A2 - Abdulrazak, Bessam

A2 - Aloulou, Hamdi

A2 - Mokhtari, Mounir

PB - Springer Verlag

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Y2 - 29 August 2017 through 31 August 2017

ER -

Akhtar U, Hussain J, Lee S. Medical semantic question answering framework on RDF data cubes. In Abdulrazak B, Aloulou H, Mokhtari M, editors, Enhanced Quality of Life and Smart Living - 15th International Conference, ICOST 2017, Proceedings. Springer Verlag. 2017. p. 255-260. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-66188-9_23

Medical semantic question answering framework on RDF data cubes

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Keywords

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