News from the LODUM headquarters and all things Linked Open Data.

Most Geographic Information Systems (GIS) do not support the usage of Linked Open Data thus, we believe that combining both worlds; Linked Open Data (LOD) and Geographic Information Systems has a great potential to improve accessibility and interoperability in Geographic Information Systems.
Linked Open Data approach links data from different sources in the Web, and so is creating a rich network of information, a former master student from MSc. Geoinformatics course, at the University of Münster, Germany, demonstrated that the usage of geographic LOD datasets as data sources for WFS is perfectly feasible.
Jim Jones is the author of the master thesis and is now Research Associate at the Institute for Geoinformatics in the University of Munster for the LIFE project. In his master thesis explored and discussed the benefits of Linked Open Data as a data source for OGC Web services, giving a solution on how it can be technically linked in GIS. His Master thesis called “Making the Web of Data Available via Web Feature Services” goes trough an overview of Linked Geographic Data, showing how it is described in different vocabularies, describing also the Web Feature Service standards and exploring it’s capabilities through its standard operations. He provides a Server application for publishing Geographic Linked Open Datasets via Web Feature Services as a result of his thesis, which can be found on github.
To share your thoughts and future work initiatives you can address him at <jim.jones@uni-muenster.de> .



We are looking for a highly motivated Master students who would like to develop an indoor/outdoor navigation tool for the campus university of Munster.


 The aim of the thesis is to come up with a tool (mobile/web) , that will enable navigation trough the campus buildings based on a linked open data graph. A first idea is to start by selecting one building of the campus, e.g., the library and create a graph indoor environment with accessibility for disable persons, using linked open data (LOD).


 The Linked Data for eScience Services (LIFE) project, publishes resources as LOD, addressing all kinds of resources, ranged from articles and books across maps and raw data.


 The overall goal of LIFE project is to facilitate sharing the research of data and thus improve interdisciplinary collaboration in science and education. It is a two-year project, that was funded by the German Research Foundation, and is jointly carried out by the Semantic Interoperability Lab (MUSIL) at Institute for Geoinformatics (http://ifgi.uni-muenster.de), the University Library at University of Münster and a wide range of partners.


 If you are interested on knowing more about it email us: Simon <simonscheider@web.de>  or/and Auriol <degbelo@uni-muenster.de>

The goal of the work (jointly conducted with the Institute for Geoinformatics at the University of Münster) is to develop a spatial recommender system which assists in exploring cause-effect relationships of significant incidence elevations of selected cancer types in a predefined geographic region. The system should draw on Linked Data techniques to answer two types of queries:

  • Given a significant elevation of the cancer risk (parameterized through the standardized incidence ratio) for a certain tumour and at a certain spatial unit (e.g. community level), what are possible (spatial) cancer causes and cancer risk factors?
  • Given an elevated cancer cause/risk factor in a geographic region, what are types of cancer likely to occur?

The tasks of the student include both a modelling component and an implementation component. The modelling aspect involves:

  • The specification of a use case (together with the IES – Institut für Epidemiologie und Sozialmedizin);
  • Identify useful taxonomies for cancer research as well as known causes and risk factors for selected cancer types in the monographies of the International Agency for Research on Cancer (IARC).

The implementation aspect includes:

  • The encoding of data from the use case using Linked Data techniques;
  • The development of a Web Interface.

The successful candidate is expected to write the Master’s thesis in the Summer Semester 2014. S/he should have epidemiological background knowledge about cancer etiologies or willingness to learn about them. Programming knowledge (HTML, CSS, PhP, Javascript) as well as knowledge of Semantic Web Technologies such as RDF, SPARQL is desirable but not mandatory.

Contact: degbelo@uni-muenster.de or simonscheider@web.de or dorothea.lemke@uni-muenster.de

We are looking for a highly motivated Master student who would like to develop a Geonames geocoding tool and API for linked data in LIFE. Even though library and other data is being published as linked open data in LIFE, currently, spatial reference of this data is implicit in the form of strings. In LIFE, resources should be searchable by places in a gazetteer, such as Geonames.org, as linked open data (LOD). The master thesis should address requirements for such a tool and an API reusable in the library context, discuss and analyse available solutions, and the development of an easy-to-use geocoder.

Linked Data for eScience Services (LIFE) is a two-year project funded by the German Research Foundation, jointly carried out by the Semantic Interoperability Lab (MUSIL) at Institute for Geoinformatics (http://ifgi.uni-muenster.de) and the University Library (http://ulb.uni-muenster.de) at University of Münster.The overall goal of LIFE is to facilitate sharing of research data and thus improve interdisciplinary collaboration in science and education. The approach addresses all kinds of resources, ranging from articles and books through maps to raw data.

Interested students should contact Simon Scheider.

Our colleagues at the spatio-temporal modelling lab offer an MSc thesis on “A Linked Open Data portal for annotating statistical datasets”:

The statistical datasets currently available in the Web often lack important information such as

  • descriptions where the spatial coordinates can be found,
  • which spatial coordinate system is used,
  • whether the data represents objects or a continuous phenomenon in space and time (fields),
  • what the observation window for a point pattern variable is.

In order to enable automated integration of this information in statistical software and hence to ensure meaningful analysis, the missing information should be queried from data providers or users and needs to be made accessible in the Web in a structured way.

The student will work on a Website that allows users to upload links to datasets available in the Web and add descriptions to these datasets. Useful description items need to be identified and existing methods for annotating spatio-temporal datasets need to be analysed. The dataset descriptions will be made accessible in the Web as Linked Open Data. To illustrate usage of the descriptions in statistical software, the SPARQL R package will be used to retrieve the descriptions and automatically import the annotated dataset in R.

Required Skills: Interest in spatial statistical analysis. Knowledge of common spatio-temporal data formats and Web technologies such as JavaScript and/or PHP. Knowledge of Linked Data technologies is an advantage, but can also be acquired during the thesis development.

Contact: Christoph Stasch, Simon Scheider, Edzer Pebesma.