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.

Background

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.

Methodology

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 ClinicalTrials.gov, 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

Publications

by (Editors: ) [BibTex of ]

News

AKSW Colloquium: Edgard Marx and Tommaso Soru on Monday, February 23, 3.00 p.m. ( 2015-02-19T22:53:47+01:00 TommasoSoru)

2015-02-19T22:53:47+01:00 TommasoSoru

On Monday, 23rd of February 2015, Edgard Marx will introduce Smart, a search engine designed over the Semantic Search paradigm; subsequently, Tommaso Soru will present ROCKER, a refinement operator approach for key discovery. Abstract – Smart Since the conception of the Web, search engines play a key role in making content available. Read more about "AKSW Colloquium: Edgard Marx and Tommaso Soru on Monday, February 23, 3.00 p.m."

Call for Feedback on LIDER Roadmap ( 2015-02-17T15:38:54+01:00 by Amrapali Zaveri)

2015-02-17T15:38:54+01:00 by Amrapali Zaveri

The LIDER project is gathering feedback on a roadmap for the use of Linguistic Linked Data for content analytics.  We invite you to give feedback in the following ways: Attend and discuss with us at the public conference call on 19 February 3 p.m. Read more about "Call for Feedback on LIDER Roadmap"

AKSW Colloquium: Konrad Höffner and Michael Röder on Monday, February 16, 3.00 p.m. ( 2015-02-16T13:45:51+01:00 by Konrad Höffner)

2015-02-16T13:45:51+01:00 by Konrad Höffner

CubeQA—Question Answering on Statistical Linked Data by Konrad Höffner Abstract Question answering systems provide intuitive access to data by translating natural language queries into SPARQL, which is the native query language of RDF knowledge bases. Statistical data, however, is structurally very different from other data and cannot be queried using existing approaches. Read more about "AKSW Colloquium: Konrad Höffner and Michael Röder on Monday, February 16, 3.00 p.m."

Kick-off of the FREME project ( 2015-02-16T12:50:49+01:00 by Amrapali Zaveri)

2015-02-16T12:50:49+01:00 by Amrapali Zaveri

Hi all ! A new InfAI project, FREME, kicked off in Berlin. FREME – Open Framework of E-Services for Multilingual and Semantic Enrichment of Digital Content is an H2020 funded project with the objective of building an open, innovative, commercial-grade framework of e-services for multilingual and semantic enrichment of digital content. Read more about "Kick-off of the FREME project"

DL-Learner 1.0 (Supervised Structured Machine Learning Framework) Released ( 2015-02-13T10:38:26+01:00 by Dr. Jens Lehmann)

2015-02-13T10:38:26+01:00 by Dr. Jens Lehmann

Dear all, we are happy to announce DL-Learner 1.0. DL-Learner is a framework containing algorithms for supervised machine learning in RDF and OWL. DL-Learner can use various RDF and OWL serialization formats as well as SPARQL endpoints as input, can connect to most popular OWL reasoners and is easily and flexibly configurable. Read more about "DL-Learner 1.0 (Supervised Structured Machine Learning Framework) Released"