ReDD-Observatory: Using the Web of Data for Evaluating the Research-Disease Disparity

The ReDD-Observatory is a project to evaluate the disparity between active areas of biomedical research and the global burden of disease using Linked Data and data-driven discovery.


It is widely accepted that there is a large disparity between the availability of treatment options and the prevalence of diseases in the world, thus placing individuals in danger. This disparity is partially caused by the restricted access to information that would allow health- care and research policy makers to formulate more appropriate measures to mitigate this disparity. Specifically, this shortage of information is caused by the difficulty in reliably obtaining and integrating data regarding the disease burden for a given nation and the respective research investments.

In response to these challenges, the Linked Data paradigm provides a simple mechanism for publishing and interlinking structured information on the Web. In conjunction with the ever increasing data on diseases and healthcare research available as Linked Data, an opportunity is created to reduce this information gap that would allow for better policy in response to these disparities.

We present the ReDD-Observatory, an approach for evaluating the Research-Disease Disparity based on the interlinking and integrating of various biomedical data sources.


The figure below provides a birds eye-view of the methodology involved in the ReDD-Observatory.

We first identified relevant datasets to be included that provided relevant information to evaluate the disparity. We not only consider the datasets already present as RDF but also those that are present in unstructured formats. These datasets are:

  1. LinkedCT - the RDF representation of, which is the database of all clinical trials around the world.
  2. Bio2RDF's PubMed - the RDF representation of PubMed, which is a service of the US National Library of Medicine that includes bibliographic information and abstracts of over 19 million publications from MEDLINE and other life science journals.
  3. WHO's Global Health Observatory (GHO), which contains statistical information regarding the mortality and burden of disease classified according to the death and DALY (disability-adjusted life year) estimates grouped by countries and regions. However, since GHO is not available as Linked Data, as the next step we devised a method for representing unstructured data as RDF. We devised a plug-in in OntoWiki to represent statistical data from GHO as RDF. We used the Data Cube Vocabulary for this conversion. More information is present here. In order to ensure the completeness, conciseness and consistency for the selected datasets our next step is to assess the data quality of the datasets. The next challenging step is to interlink the datasets for a number of concepts such as (a) countries, (b) diseases and (c) publications. The assessment of the disparity is then performed with a number of parametrized SPARQL queries. We evaluate the results wrt. information quality and interlinking precision. As a consequence, we are, for the first time, able to provide reliable indicators for the extent of the research-disease disparity around the world in an semi- automated fashion, thus enabling healthcare professionals and policy makers to make more informed decisions.

Further Information

Current Team


by (Editors: ) [BibTex of ]


AKSW Colloquium, 27-04-2015, Ontotext’s RDF database-as-a-service (DBaaS) via Self-Service Semantic Suite (S4) platform via & Knowledge-Based Trust ( 2015-04-23T17:21:00+02:00 JoergUnbehauen)

2015-04-23T17:21:00+02:00 JoergUnbehauen

This colloquium features two talks. First the Self-Service Semantic Suite (S4) platform is presented by Marin Dimitrov (Ontotext), followed up by Jörg Unbehauens report on Googles effort on using factual correctness as a ranking factor. Read more about "AKSW Colloquium, 27-04-2015, Ontotext’s RDF database-as-a-service (DBaaS) via Self-Service Semantic Suite (S4) platform via & Knowledge-Based Trust"

Talk by Kleanthi Georgala ( 2015-04-22T22:48:27+02:00 by Dr. Amrapali Zaveri)

2015-04-22T22:48:27+02:00 by Dr. Amrapali Zaveri

Last week on Friday, 17th April, Kleanthi Georgala visited AKSW and gave a talk entitled “Traces Through Time: Probabilistic Record Linkage – Medieval and Early Modern”. More information below. Read more about "Talk by Kleanthi Georgala"

SAKE Projekt website goes live ( 2015-04-22T11:50:43+02:00 by Simon Bin)

2015-04-22T11:50:43+02:00 by Simon Bin

Hi all! The project website for the BMWi funded Smart Data Web Project “SAKE” is now on-line at Read more about "SAKE Projekt website goes live"

AKSW Colloquium, 20-04-2015, OWL/DL approaches to improve POS tagging ( 2015-04-20T08:21:00+02:00 Markus Ackermann)

2015-04-20T08:21:00+02:00 Markus Ackermann

In this colloquium Markus Ackermann will touch on the ‘linguistic gap‘ of recent POS tagging endeavours (as perceived by C. Manning, [1]). Read more about "AKSW Colloquium, 20-04-2015, OWL/DL approaches to improve POS tagging"

AKSW Colloquium, 13-04-2015, Effective Caching Techniques for Accelerating Pattern Matching Queries ( 2015-04-13T11:51:58+02:00 by Claus Stadler)

2015-04-13T11:51:58+02:00 by Claus Stadler

In this colloquium, Claus Stadler will present the paper Effective Caching Techniques for Accelerating Pattern Matching Queries by Arash Fard, Satya Manda, Lakshmish Ramaswamy, and John A. Miller. Read more about "AKSW Colloquium, 13-04-2015, Effective Caching Techniques for Accelerating Pattern Matching Queries"