A Roadmap for Navigating the Life Sciences Linked Open Data Cloud

Author(s):  
Ali Hasnain ◽  
Syeda Sana e Zainab ◽  
Maulik R. Kamdar ◽  
Qaiser Mehmood ◽  
Claude N. Warren ◽  
...  
2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Ali Hasnain ◽  
Qaiser Mehmood ◽  
Syeda Sana e Zainab ◽  
Muhammad Saleem ◽  
Claude Warren ◽  
...  

2014 ◽  
Vol 30 (9) ◽  
pp. 1338-1339 ◽  
Author(s):  
S. Jupp ◽  
J. Malone ◽  
J. Bolleman ◽  
M. Brandizi ◽  
M. Davies ◽  
...  

2018 ◽  
Vol 14 (4) ◽  
pp. 110-128 ◽  
Author(s):  
Nosheen Fayyaz ◽  
Irfan Ullah ◽  
Shah Khusro

This article describes how Linked Open Data (LOD), under the umbrella of the Semantic Web, integrates the openly-published semantic information making it easily understandable and consumable by humans and machines. Currently, researchers have applied the principles of LOD in several domains including e-government, media, publications, geography, and life sciences. Besides the fast pace of research, the field is still an emerging one, where researchers face several prominent challenges and issues that need to resolve to exploit LOD to its fullest. In this article, the authors have identified challenges, issues, and research opportunities in the publishing, management, linking, and consumption of LOD. The research work presented here will grab the attention of researchers and may aid to the current state-of-the-art in this area.


2014 ◽  
Vol 29 (4) ◽  
pp. 356-363
Author(s):  
Yusuke Komiyama ◽  
Masaki Banno ◽  
Masayuki Yarimizu ◽  
Fumihiro Kato ◽  
Ikki Ohmukai ◽  
...  

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Maulik R. Kamdar ◽  
Mark A. Musen

AbstractWhile the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.


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