2017
Enayat Rajabi; Syed Sibte Raza Abidi
Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics Proceedings Article
In: Informatics for Health (MIE2017), Manchester, UK, pp. 486-490, European Federation for Medical Informatics (EFMI) and IOS Press, 2017, ISSN: 1879-8365.
Abstract | Links | BibTeX | Tags: Semantic Web, Semantic Web Analytics, Social Network Analysis
@inproceedings{Rajabi2017,
title = {Discovering Central Practitioners in a Medical Discussion Forum Using Semantic Web Analytics },
author = {Enayat Rajabi and Syed Sibte Raza Abidi },
doi = {10.3233/978-1-61499-753-5-486},
issn = {1879-8365},
year = {2017},
date = {2017-04-24},
booktitle = {Informatics for Health (MIE2017), Manchester, UK},
pages = {486-490},
publisher = {European Federation for Medical Informatics (EFMI) and IOS Press},
abstract = {The aim of this paper is to investigate semantic web based methods to
enrich and transform a medical discussion forum in order to perform semanticsdriven
social network analysis. We use the centrality measures as well as semantic
similarity metrics to identify the most influential practitioners within a discussion
forum. The centrality results of our approach are in line with centrality measures
produced by traditional SNA methods, thus validating the applicability of semantic
web based methods for SNA, particularly for analyzing social networks for
specialized discussion forums. },
keywords = {Semantic Web, Semantic Web Analytics, Social Network Analysis},
pubstate = {published},
tppubtype = {inproceedings}
}
The aim of this paper is to investigate semantic web based methods to
enrich and transform a medical discussion forum in order to perform semanticsdriven
social network analysis. We use the centrality measures as well as semantic
similarity metrics to identify the most influential practitioners within a discussion
forum. The centrality results of our approach are in line with centrality measures
produced by traditional SNA methods, thus validating the applicability of semantic
web based methods for SNA, particularly for analyzing social networks for
specialized discussion forums.
enrich and transform a medical discussion forum in order to perform semanticsdriven
social network analysis. We use the centrality measures as well as semantic
similarity metrics to identify the most influential practitioners within a discussion
forum. The centrality results of our approach are in line with centrality measures
produced by traditional SNA methods, thus validating the applicability of semantic
web based methods for SNA, particularly for analyzing social networks for
specialized discussion forums.