Amongst the best incentives for data authors are applications that use that data. One sort of data that especially interests me is dataset metadata, for which the voiD vocabulary was developed; I think this kind of data has the potential to enable the future generation of web apps to join together the ever-growing web of data in wild and exciting new ways. So I was pretty pleased when I saw the voiD store from RKB Explorer. This store provides a SPARQL endpoint over all the voiD descriptions RKB Explorer have produced about their datasets, plus some descriptions they’ve gathered about other datasets. It also provides a list of source documents, sample queries, and a service that takes a list of URIs, and returns a list of SPARQL endpoints that might be able to return triples about them.

This, together with a rainy weekend, prompted me to try out some simple voiD-related things I’d been thinking of. I’ve also been aggregating voiD data in one of my dev stores. This is done partly by creating templated descriptions from a list of Talis Platform stores and poking at them with some SPARQL queries. The rest of the data I found either manually, or by querying Sindice for a list of void:Dataset URIs found in the documents they’ve crawled.

The Sindice API allows you to specify triple patterns with wildcards, and will return you an Atom feed: * rdf:type void:Dataset . I page through the results, importing the RDF from the URIs into my store.

One of my favourite terms from voiD is void:uriRegexPattern, which can be used to indicate that if a URI matches the pattern, the dataset might contain some triples about that URI. You can do this with a bit of SPARQL:

    
prefix void: <http://rdfs.org/ns/void#>
DESCRIBE ?dataset {
     ?dataset void:uriRegexPattern ?regex ; void:sparqlEndpoint ?sparql ; a void:Dataset .

    FILTER(REGEX("http://example.com/my/uri", ?regex))
}

    

The novel thing here is that normally, when you use REGEX() in SPARQL, you put a variable binding in the first parameter position, and hardcode a regular expression into the query in the 2nd position. Here though, the regex is in the data, and it is the string against which it is evaluated which is hardcoded, and the variable binding contains the regex. (Unfortunately, while this works with ARQ, it doesn’t appear to work with 3Store – which is perhaps why the rkbexplorer voiD Store provides this as a separate web service).

So, I’ve used this to create a page that will take a URI, and query my voiD store for void:sparqlEndpoints and void:uriLookupEndpoints, which it will then call to retrieve triples and render them on the page. Here is a query for the URI http://climb.dataincubator.org/dataset .

Another query that interested me, which has become possible since the Platform introduced support for the COUNT() function from SPARQL 1.1, is, which are the most commonly used vocabularies? (SIOC and FOAF so far! – thought this is because I generated many of these triples based on scripted prodding of endpoints with ASK queries) But then I wanted to be able to see easily which datasets used which vocabularies, so I created some pages to let me browse datasets by vocabulary.

1. SIOC Core Ontology Namespace(54)
2. Friend of a Friend (FOAF) vocabulary(42)
3. Coreference Ontology (35)

4. http://www.aktors.org/ontology/portal# (34)

5. http://www.aktors.org/ontology/support# (30)
6. http://www.rkbexplorer.com/ontologies/resist# (30)
7. void (25)
8. http://purl.org/NET/scovo# (24)
9. http://acm.rkbexplorer.com/ontologies/acm# (22)
10. http://courseware.rkbexplorer.com/ontologies/courseware# (21)

Then I made some pages to do the same thing with dct:subjects. Here, the largest category by some way, is category: online_social_networking. This is because I generated ?dataset dct:subject <http://dbpedia.org/resource/Category:Online_social_networking> . triples automatically for all the platform stores which made a certain use of terms from the SIOC ontology.

These automatically generated voiD descriptions will not, of course, present such a balanced picture of what is out there, and skew the results somewhat. The most interesting descriptions are those which are handcrafted to some extent, describing something of the nature of the dataset’s domains.

I’ve also provided a form for submitting voiD URLs to. My hope is that this simple application, together with the rkbexplorer voiD Store, might encourage more people to describe their linked data datatsets with voiD, or perhaps add more detail to the descriptions they already publish, in order to see their dataset come up in the appropriate queries. And I hope that this, in turn, will encourage others to build more sophisticated and exciting applications using that data.