Mining the Web of Life Sciences Linked Open Data for Mechanism-Based Pharmacovigilance

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observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective.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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Discovering and linking with life sciences linked open data cloud

Proceedings of the Symposium on Applied Computing - SAC '17 ◽

10.1145/3019612.3019933 ◽

2017 ◽

Author(s):

Muntazir Mehdi

Keyword(s):

Life Sciences ◽

Open Data ◽

Linked Open Data

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The read–write Linked Data Web

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2012.0513 ◽

2013 ◽

Vol 371 (1987) ◽

pp. 20120513 ◽

Cited By ~ 15

Author(s):

Tim Berners-Lee ◽

Kieron O’Hara

Keyword(s):

Future Development ◽

Linked Data ◽

Open Data ◽

Linked Open Data ◽

The Future ◽

The Web

This paper discusses issues that will affect the future development of the Web, either increasing its power and utility, or alternatively suppressing its development. It argues for the importance of the continued development of the Linked Data Web, and describes the use of linked open data as an important component of that. Second, the paper defends the Web as a read–write medium, and goes on to consider how the read–write Linked Data Web could be achieved.

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Publishing Statistical Data following the Linked Open Data Principles

Cases on Open-Linked Data and Semantic Web Applications ◽

10.4018/978-1-4666-2827-4.ch011 ◽

2013 ◽

pp. 199-226 ◽

Cited By ~ 5

Author(s):

Jose María Alvarez Rodríguez ◽

Jules Clement ◽

José Emilio Labra Gayo ◽

Hania Farhan ◽

Patricia Ordoñez de Pablos

Keyword(s):

Linked Data ◽

Statistical Data ◽

Open Data ◽

Linked Open Data ◽

Dimensional Measure ◽

The Web

This chapter introduces the promotion of statistical data to the Linked Open Data initiative in the context of the Web Index project. A framework for the publication of raw statistics and a method to convert them to Linked Data are also presented following the W3C standards RDF, SKOS, and OWL. This case study is focused on the Web Index project; launched by the Web Foundation, the Index is the first multi-dimensional measure of the growth, utility, and impact of the Web on people and nations. Finally, an evaluation of the advantages of using Linked Data to publish statistics is also presented in conjunction with a discussion and future steps sections.

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Creation and Integration of Reference Ontologies for Efficient LOD Management

Semi-Automatic Ontology Development ◽

10.4018/978-1-4666-0188-8.ch007 ◽

2012 ◽

pp. 162-199 ◽

Cited By ~ 1

Author(s):

Mariana Damova ◽

Atanas Kiryakov ◽

Maurice Grinberg ◽

Michael K. Bergman ◽

Frédérick Giasson ◽

...

Keyword(s):

Real Life ◽

Open Data ◽

Real Data ◽

Heterogeneous Data ◽

Linked Open Data ◽

Data Space ◽

Upper Level ◽

Reference Knowledge ◽

Global Data ◽

The Web

The chapter introduces the process of design of two upper-level ontologies—PROTON and UMBEL—into reference ontologies and their integration in the so-called Reference Knowledge Stack (RKS). It is argued that RKS is an important step in the efforts of the Linked Open Data (LOD) project to transform the Web into a global data space with diverse real data, available for review and analysis. RKS is intended to make the interoperability between published datasets much more efficient than it is now. The approach discussed in the chapter consists of developing reference layers of upper-level ontologies by mapping them to certain LOD schemata and assigning instance data to them so they cover a reasonable portion of the LOD datasets. The chapter presents the methods (manual and semi-automatic) used in the creation of the RKS and gives examples that illustrate its advantages for managing highly heterogeneous data and its usefulness in real life knowledge intense applications.

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