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One year of PiLOD project

Yesterday was the closing event of the Pilot Linked Open Data project. A significantly big crowd of politicians, civil servants, hackers, SME owners, open data activists and researchers gathered in the very nice building of the RCE in Amersfoort to hear about what has been done within this one year project lead by Erwin Folmer. But not only that, the participants also got some more insights into Linked Data usage outside of the project and a guided tour through the RCE. More information, photos, and links to the slides, can be found in the report about the event.

Oliver Bartlett and John Walker gave two keynotes explaining how Linked Data is put into use respectively at the BBC and at NXP. Both companies are using this technology to better describe their content and interconnect separated data sources. A shared objective besides having better and more efficient internal processes is to provide better services to the customers. Thanks to the harmonization and linkage of the data, these customers can expect to get more coherent data about what they care, be it a chip or a football player. The two presentations also highlighted two important facts about Linked Data: it’s versatile enough to be applied to two very different business domains such as media and chip manufacturing, 2) the data does not have to be open to be benefit form Semantic Web technologies – as of now, a lot of data at the BBC is becoming LD but none of this LD is LOD.

My activity within the project was around chatting (a lot, as I usually do :-p), writing two book chapters (“Publishing Open Data on the Web”, and “How-to: Linking resources from two datasets” ) and giving an hand on the “HuisKluis” work package managed by Paul Francissen.  I spoke a bit about the latest, showing a demo and some slides to explain how data is managed in the back-end. In short, the “HuisKluis” is a place where information about a house is found and shared. See the following video for a better introduction:

The prototype can be found at http://pilod-huiskluis.appspot.com/ . It works only for houses in the Netherlands but there are a few examples that can be used too:

huiskluis

Here are the few slides giving more details about the implementation:

If you want to really know everything about how things work, feel free to just look at the source code.

This PiLOD project was a pleasant and enriching experience, I’m very much looking forward to a PiLOD2 for a second year of LOD brainstorming and hacking together with Marcel, Arjen, Erwin, Paul, Lieke, Hans, Bart, Dimitri, … and the rest of the (the rather big) group 🙂 This post is also a good opportunity to thank again the Network Institute for having supported this collaboration with a generous research voucher. Thanks!

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CKAN->network exporter for the LOD Cloud

The LOD cloud as rendered by Gephi

One year ago, we posted on the LarkC blog a first network model of the LOD cloud. Network analysis software can highlight some aspects of the cloud that are not directly visible otherwise. In particular, the presence of dense sub-groups and several hubs – whereas in the classical picture, DBPedia is easily perceived as being the only hub.

Computing network measures such as centralities, clustering coefficient or the average path length can reveal much more about the content of a graph and the interplay of its nodes. As shown since that blog post, these information can be used to appreciate the evolution of the Web of Data and devise actions to improve it (see the WoD analysis page for more information about our research on this topic). Unfortunately, the picture provided by Richard and Anja on lod-cloud.net can not be fitted directly into a network analysis software which expects a .net or CSVs files instead. Fortunately, thanks to the very nice API of CKAN.net it is easy to write a script generating such files. We made such a script and thought it would be a good idea to share it 🙂

The script is hosted on GitHub. It produces a “.net” file according to the format of Pajek and two CSV files, one for the nodes and one for the edges. These CSV can then easily be imported into Gephi, for instance, or any other software of your choice. We also made a dump of the cloud as of today and packaged the resulting files.

Have fun analysing the graph and let us know if you find something interesting 😉

 

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