Creation and Integration of Reference Ontologies for Efficient LOD Management

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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Enabling Web-scale data integration in biomedicine through Linked Open Data

npj Digital Medicine ◽

10.1038/s41746-019-0162-5 ◽

2019 ◽

Vol 2 (1) ◽

Cited By ~ 3

Author(s):

Maulik R. Kamdar ◽

Javier D. Fernández ◽

Axel Polleres ◽

Tania Tudorache ◽

Mark A. Musen

Keyword(s):

Data Integration ◽

Biomedical Research ◽

Semantic Processing ◽

Open Data ◽

Heterogeneous Data ◽

Research Community ◽

Linked Open Data ◽

Biomedical Data ◽

Semantic Web Technologies ◽

The Web

Abstract The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems.

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

Scientific Data ◽

10.1038/s41597-021-00797-y ◽

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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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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Linked Data - The Story So Far

International Journal on Semantic Web and Information Systems ◽

10.4018/jswis.2009081901 ◽

2009 ◽

Vol 5 (3) ◽

pp. 1-22 ◽

Cited By ~ 1986

Author(s):

Christian Bizer ◽

Tom Heath ◽

Tim Berners-Lee

Keyword(s):

Best Practices ◽

Research Agenda ◽

Linked Data ◽

Structured Data ◽

Data Space ◽

Web Of Data ◽

Technological Developments ◽

Technical Principles ◽

Global Data ◽

The Web

The term “Linked Data” refers to a set of best practices for publishing and connecting structured data on the Web. These best practices have been adopted by an increasing number of data providers over the last three years, leading to the creation of a global data space containing billions of assertions— the Web of Data. In this article, the authors present the concept and technical principles of Linked Data, and situate these within the broader context of related technological developments. They describe progress to date in publishing Linked Data on the Web, review applications that have been developed to exploit the Web of Data, and map out a research agenda for the Linked Data community as it moves forward.

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