Kasabi directory matrix

Kasabi is a recent player in the data marketplace space. What distinguishes Kasabi from other marketplaces (and make it closer to my heart) is that it is based on Linked Data. All the datasets in Kasabi are represented in RDF and provide Linked Data capabilities (with additional set of standard and customised APIs for each dataset… more details).

A recent dataset on Kasabi is the directory of datasets on Kasabi itself. Having worked on related stuff before, especially dcat, I decided to spend this weekend playing with this dataset (not the best plan for a weekend you think hah?!).

To make the long story short, I built a (currently-not-very-helpful) visualization of the distribution of the classes in the datasets which you can see here.

In details:
I queried the SPARQL endpoint for a list of datasets and the classes used in each of them along with their count (the Python code I used is on github, however you need to provided your own Kasabi key and subscribe to the API).
Using Protovis I visualized the data in a matrix. Datasets are sorted alphabetically while classes are sorted descendingly according to the number of datasets they are used in. Clicking on a cell currently shows count of the corresponding dataset,class pair.

Note: I filtered out common classes like rdfs:class, owl:DatatypeProperty, etc… and I also didn’t include classes that appear in only one dataset.

Quick observations:
Not surprisingly, skos:Concept and foaf:Person are the most used classes. In general, the matrix is sparse as most of the datasets are ”focused”. Hampshire dataset, containing various information about Hampshire, uses a large number of classes.

This is still of limited value, but I have my ambitious plan below 🙂
1. set the colour hue of each cell according to the corresponding count i.e. entities of the class in the dataset
2. group (and may be colour) datasets based on their category
3. replace classes URIs with curies (using prefix.cc?)
4. when clicking on a cell, show the class structure in the corresponding dataset i.e. what properties are used to describe instances of the class in the corresponding dataset (problem here is that I need to subscribe to the dataset to query it). This can be a good example about smooth transition in RDF from schema to instance data


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