A use-case for SPARQL and Nepomuk

As I got contacted by two different companies last few days who both had questions about integrating Tracker into their device, I started thinking that perhaps I should illustrate what Tracker can already do today.

I’m going to make a demo for the public transportation industry in combination with contacts and places of interest. Tracker’s ontologies cross many domains, of course (this is just an example).

I agree that in principle what I’m showing here isn’t rocket science. You can do this with almost any database technology. What is interesting is that as soon as many domains start sharing the ontology and store their data in a shared way, interesting queries and use-cases are made possible.

So let’s first insert a place of interest: the Pizza Hut in Nossegem

tracker-sparql -uq "
INSERT { _:1 a nco:PostalAddress ; nco:country 'Belgium';
               nco:streetAddress 'Weiveldlaan 259 Zaventem' ;
               nco:postalcode '1930' .
        _:2 a slo:Landmark; nie:title 'Pizza Hut Nossegem';
              slo:location [ a slo:GeoLocation;
                  slo:latitude '50.869949'; slo:longitude '4.490477';
                  slo:postalAddress _:1 ];
              slo:belongsToCategory slo:predefined-landmark-category-food-beverage  }"

And let’s add some busstops:

tracker-sparql -uq "
INSERT { _:1 a nco:PostalAddress ; nco:country 'Belgium';
               nco:streetAddress 'Leuvensesteenweg 544 Zaventem' ;
               nco:postalcode '1930' .
         _:2 a slo:Landmark; nie:title 'Busstop Sint-Martinusweg';
               slo:location [ a slo:GeoLocation;
                   slo:latitude '50.87523'; slo:longitude '4.49426';
                   slo:postalAddress _:1 ];
               slo:belongsToCategory slo:predefined-landmark-category-transport  }"
tracker-sparql -uq "
INSERT  { _:1 a nco:PostalAddress ; nco:country 'Belgium';
                nco:streetAddress 'Leuvensesteenweg 550 Zaventem' ;
                nco:postalcode '1930' .
          _:2 a slo:Landmark; nie:title 'Busstop Hoge-Wei';
                slo:location [ a slo:GeoLocation;
                    slo:latitude '50.875988'; slo:longitude '4.498208';
                    slo:postalAddress _:1 ];
                slo:belongsToCategory slo:predefined-landmark-category-transport  }"
tracker-sparql -uq "
INSERT  { _:1 a nco:PostalAddress ; nco:country 'Belgium';
                nco:streetAddress 'Guldensporenlei Turnhout' ;
                nco:postalcode '2300' .
          _:2 a slo:Landmark; nie:title 'Busstop Guldensporenlei';
                slo:location [ a slo:GeoLocation;
                    slo:latitude '51.325463'; slo:longitude '4.938047';
                    slo:postalAddress _:1 ];
                slo:belongsToCategory slo:predefined-landmark-category-transport  }"

Let’s now get all the busstops nearby the Pizza Hut in Nossegem:

tracker-sparql -q "
SELECT ?name ?lati ?long WHERE {
   ?p slo:belongsToCategory slo:predefined-landmark-category-food-beverage;
       slo:location [ slo:latitude ?plati; slo:longitude ?plong ] .
   ?b slo:belongsToCategory slo:predefined-landmark-category-transport ;
       slo:location [ slo:latitude ?lati; slo:longitude ?long ] ;
      nie:title ?name .
   FILTER (tracker:cartesian-distance (?lati, ?plati, ?long, ?plong) < 1000)
}"
Results:
  Busstop Sint-Martinusweg, 50.87523, 4.49426
  Busstop Hoge-Wei, 50.875988, 4.498208

This of course was an example with only slo:Landmark. But that slo:location property can be placed on any nie:InformationElement. Meaning that for example a nco:PersonContact can also be involved in such a cartesian-distance query (which is of course just an example).

Let’s make an example use-case: We want contact details of friends (with publicized coordinates) who are nearby a slo:Landmark that is in a food and beverage landmark category, so that the messenger application can prepare a text message window where you’ll type that you want to get together to get lunch at the Pizza Hut.

Ok, so let’s add some nco:PersonContact to our SPARQL endpoint who are nearby the Pizza Hut:

tracker-sparql -uq "
INSERT { _:1 a nco:PersonContact ; nco:fullname 'John Carmack';
               slo:location [ a slo:GeoLocation;
                   slo:latitude '51.325413'; slo:longitude '4.938037' ];
               nco:hasEmailAddress [ a nco:EmailAddress;
                 nco:emailAddress 'john.carmack@somewhere.com'] }"
tracker-sparql -uq "
INSERT { _:1 a nco:PersonContact ; nco:fullname 'Greg Kroah-Hartman';
               slo:location [ a slo:GeoLocation;
                   slo:latitude '51.325453'; slo:longitude '4.938027' ];
               nco:hasEmailAddress [ a nco:EmailAddress;
                 nco:emailAddress 'greg.kroah@somewhere.com'] }"

And let’s add one person who isn’t nearby the Pizza Hut in Nossegem:

tracker-sparql -uq "
INSERT { _:1 a nco:PersonContact ; nco:fullname 'Jean Pierre';
               slo:location [ a slo:GeoLocation;
                   slo:latitude '50.718091'; slo:longitude '4.880134' ];
               nco:hasEmailAddress [ a nco:EmailAddress;
                 nco:emailAddress 'jean.pierre@somewhere.com'] }"

And now, the query:

tracker-sparql -q "
SELECT ?name ?email ?lati ?long WHERE {
   ?p slo:belongsToCategory slo:predefined-landmark-category-food-beverage;
       slo:location [ slo:latitude ?plati; slo:longitude ?plong ] ;
      nie:title ?pname .
   ?b a nco:PersonContact;
        slo:location [ slo:latitude ?lati; slo:longitude ?long ] ;
      nco:fullname ?name ; nco:hasEmailAddress [ nco:emailAddress ?email ].
   FILTER (tracker:cartesian-distance (?lati, ?plati, ?long, ?plong) < 10000)
}"
Results:
  Greg Kroah-Hartman, greg.kroah@somewhere.com, 50.874715, 4.49158
  John Carmack, john.carmack@somewhere.com, 50.874715, 4.49154

These use-cases of course only illustrate the simplified location ontology in combination with the Nepomuk contacts ontology. There are many such domains in Nepomuk and when defining your own platform and/or a new domain on the desktop you can add (your own) ontologies. Mind that for the desktop you should preferably talk to Nepomuk first.

The strength of such a platform is also its weakness: if no information sources put their data into the SPARQL endpoint, no information sink can do queries that’ll yield meaningful results. You of course don’t have this problem in a contained environment where you define what does and what doesn’t get stored and where, like an embedded device.

A desktop like KDE or GNOME shouldn’t have this problem either, if only everybody would agree on the technology and share the ontologies. Which isn’t necessarily happening (fair point), although both KDE with Nepomuk-KDE and GNOME with Tracker share most of Nepomuk.

But indeed; if you don’t store anything in Tracker, it’s useless. That’s why Tracker comes with a file system miner and provides a framework for writing your own miners. The idea is that with time more and more applications will use Tracker, making it increasingly useful. Hopefully.

 

2 thoughts on “A use-case for SPARQL and Nepomuk

    1. pvanhoof

      Yep, I noticed that. That’s indeed really good news. It might be a good idea to further document this effort and to talk about it more on your blog and in the Tracking mailing list. Perhaps it attracts more people who’ll join development effort and get more desktop projects involved?

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