RIKEN MetaDatabase: A Database Platform as a Microcosm of Linked Open Data Cloud in the Life Sciences

2016 ◽  
pp. 99-115 ◽  
Author(s):  
Norio Kobayashi ◽  
Kai Lenz ◽  
Hiroshi Masuya
Keyword(s):  
Life Sciences ◽  
Open Data ◽  
Linked Open Data ◽  
Database Platform

A Roadmap for Navigating the Life Sciences Linked Open Data Cloud

2015 ◽  
pp. 97-112 ◽  
Author(s):  
Ali Hasnain ◽  
Syeda Sana e Zainab ◽  
Maulik R. Kamdar ◽  
Qaiser Mehmood ◽  
Claude N. Warren ◽  
...  
Keyword(s):  
Life Sciences ◽  
Open Data ◽  
Linked Open Data

scholarly journals BioFed: federated query processing over life sciences linked open data

2017 ◽  
Vol 8 (1) ◽  
Author(s):  
Ali Hasnain ◽  
Qaiser Mehmood ◽  
Syeda Sana e Zainab ◽  
Muhammad Saleem ◽  
Claude Warren ◽  
...  
Keyword(s):  
Query Processing ◽  
Life Sciences ◽  
Open Data ◽  
Linked Open Data ◽  
Federated Query Processing

scholarly journals The EBI RDF platform: linked open data for the life sciences

2014 ◽  
Vol 30 (9) ◽  
pp. 1338-1339 ◽  
Author(s):  
S. Jupp ◽  
J. Malone ◽  
J. Bolleman ◽  
M. Brandizi ◽  
M. Davies ◽  
...  
Keyword(s):  
Life Sciences ◽  
Open Data ◽  
Linked Open Data

On the Current State of Linked Open Data

2018 ◽  
Vol 14 (4) ◽  
pp. 110-128 ◽  
Author(s):  
Nosheen Fayyaz ◽  
Irfan Ullah ◽  
Shah Khusro
Keyword(s):  
Semantic Web ◽  
Semantic Information ◽  
State Of The Art ◽  
Research Work ◽  
Life Sciences ◽  
Open Data ◽  
Linked Open Data ◽  
Research Opportunities ◽  
Current State ◽  
Fast Pace

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.

scholarly journals Linked Open Data Construction of Purpose Oriented Interactomics by Integration of Life Sciences LOD

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

scholarly journals An empirical meta-analysis of the life sciences linked open data on the web

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Maulik R. Kamdar ◽  
Mark A. Musen
Keyword(s):  
Meta Analysis ◽  
Life Sciences ◽  
Open Data ◽  
Heterogeneous Data ◽  
Linked Open Data ◽  
Data Sources ◽  
Semantic Heterogeneity ◽  
Multiple Sources ◽  
Schema Graph ◽  
The Web

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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