Detecting inner-ear anatomical and clinical datasets in the linked open data (LOD) cloud
Date
2015-10-15Author
Mehdi, Muntazir
Iqbal, Aftab
Khan, Yasar
Decker, Stefan
Sahay, Ratnesh
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Mehdi, Muntazir , Iqbal, Aftab , Khan, Yasar , Decker, Stefan , & Sahay, Ratnesh (2015). Detecting Inner-Ear Anatomical and Clinical Datasets in the Linked Open Data (LOD) Cloud. Paper presented at the 14th International Semantic Web Conference (ISWC), Pennsylvania, USA, 15 October.
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Abstract
Linked Open Data (LOD) Cloud is a mesh of open datasets
coming from different domains. Among these datasets, a notable amount
of datasets belong to the life sciences domain linked together forming an
interlinked “Life Sciences Linked Open Data (LSLOD) Cloud”. One of the
key challenges for data publishers is to identify and establish links between
newly generated domain specific datasets and LSLOD Cloud. While a
number of publishing tools exist for creating links from new to existing
datasets, tools to detect domain-specific relevant datasets for linking
purposes are missing. In this paper, we propose an extended technique for
automatically identifying relevant datasets in LSLOD Cloud for inner-ear
anatomical and clinical terminologies. We validate the proposed technique
with experiments over the publicly accessible LSLOD Cloud using realworld
terminologies and datasets provided by clinical organizations.