GHO: Publishing and Interlinking the Global Health Observatory Dataset

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The improvement of public health is one of the main indicators for societal progress. Statistical data for monitoring public health is highly relevant for a number of sectors, such as research (e.g. in the life sciences or economy), policy making, health care, pharmaceutical industry, insurances etc. Such data is meanwhile available even on a global scale, e.g. in the Global Health Observatory (GHO) of the United Nations’s World Health Organization (WHO). GHO comprises more than 50 different datasets, it covers all 198 WHO member countries and is updated as more recent or revised data becomes available or when there are changes to the methodology being used. However, this data is only accessible via complex spreadsheets and, therefore, queries over the 50 different datasets as well as combinations with other datasets are very tedious and require a significant amount of manual work. By making the data available as RDF, we lower the barrier for data re-use and integration.

Homepage Download

The converted GHO data is now available at http://gho.aksw.org/. After converting the entire GHO data, an RDF dataset containing almost 8 million triples was obtained. Following is the example of a single statistical item, the death value of 1098, from the GHO dataset represented using the Data Cube vocabulary:

gho:o1     a           qb:Observation;
       gho:Country     Afghanistan;
       gho:stat_pop    24076;
       gho:Disease     All Causes;
       gho:incidence   18437.

gho:Country    a    qb:DimensionProperty;
                    rdfs:label    "Country".

gho:Disease    a    qb:DimensionProperty;
                    rdfs:label    "Disease".

Further Information

  • This is a short presentation describing the process of conversion of the CSV files to RDF using SCOVO (Statistical Core Vocabulary) in OntoWiki. SCOVO is an earlier version of the Data Cube Vocabulary and the conversion process is similar for both.
  • This is a position paper that was accepted for a presentation at the Ontologies in Biomedicine and Life Sciences workshop held at Mannheim (Germany) from September 9 – 10, 2010.
  • The dataset description has been published here.
  • This dataset is also part of the LODD datasets.

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News

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2017-05-11T13:01:45+02:00 by Patrick Westphal

Dear all, we are happy to announce the 0.2 release of SML-Bench, our Structured Machine Learning benchmark framework. SML-Bench provides full benchmarking scenarios for inductive supervised machine learning covering different knowledge representation languages like OWL and Prolog. Read more about "SML-Bench 0.2 Released"

AKSW Colloquium, 08.05.2017, Scalable RDF Graph Pattern Matching ( 2017-05-08T09:42:49+02:00 by Lorenz Bühmann)

2017-05-08T09:42:49+02:00 by Lorenz Bühmann

At the AKSW Colloquium, on Monday 8th of May 2017, 3 PM, Lorenz Bühmann will discuss a paper titled “Type-based Semantic Optimization for Scalable RDF Graph Pattern Matching” of Kim et al. Read more about "AKSW Colloquium, 08.05.2017, Scalable RDF Graph Pattern Matching"

ESWC 2017 accepted two Demo Papers by AKSW members ( 2017-04-19T10:19:43+02:00 Christopher Schulz)

2017-04-19T10:19:43+02:00 Christopher Schulz

Hello Community! The 14th ESWC, which takes place from May 28th to June 1st 2017 in Portoroz, Slovenia, accepted two demos to be presented at the conference. Read more about them in the following:                                                                         1. Read more about "ESWC 2017 accepted two Demo Papers by AKSW members"

AKSW Colloquium, 10.04.2017, GeoSPARQL on geospatial databases ( 2017-04-07T10:43:55+02:00 by Dr. Matthias Wauer)

2017-04-07T10:43:55+02:00 by Dr. Matthias Wauer

At the AKSW Colloquium, on Monday 10th of April 2017, 3 PM, Matthias Wauer will discuss a paper titled “Ontop of Geospatial Databases“. Read more about "AKSW Colloquium, 10.04.2017, GeoSPARQL on geospatial databases"

AKSW Colloquium, 03.04.2017, RDF Rule Mining ( 2017-03-31T13:39:28+02:00 TommasoSoru)

2017-03-31T13:39:28+02:00 TommasoSoru

At the AKSW Colloquium, on Monday 3rd of April 2017, 3 PM, Tommaso Soru will present the state of his ongoing research titled “Efficient Rule Mining on RDF Data”, where he will introduce Horn Concerto, a novel scalable SPARQL-based approach … Continue reading → Read more about "AKSW Colloquium, 03.04.2017, RDF Rule Mining"