Let’s start with a relatively simple graph. The graph shows the relationships between John, Fred, Max and Picca. John and Fred are humans who we’ll refer to as contacts. Max and Picca are pets. Max is a dog and Picca is a parrot. Both Picca and Max are owned by John. Fred claims that John is his friend.
If we would want to represent this story semantically we would first need to make an dictionary that describes pets, contacts, dogs, parrots. The dictionary would also describe possible relationships like ownership of a pet and the friendship between two contacts. Don’t forget, making something semantic means that you want to give meaning to the things that interest you.
Giving meaning is exactly what we’ll start with. We will write the schema for making this story possible. We will call this an ontology.
We describe our ontology using the Turtle format. In Turtle you can have prefixes. The prefix test: for example is the same as using <http://test.org/ontologies/tracker#>.
In Turtle you describe statements by giving a subject, a predicate and then an object. The subject is what you are talking about. The predicate is what about the subject your are talking about. And finally the object is the value. This value can be a resource or a literal.
When you write a . (a dot) in Turtle it means that you end describing the subject. When you write a ; (semicolon) it means that you continue with the same subject, but will start describing a new predicate. When you write a , (comma) it means that you even continue with the same predicate. The same rules apply in the WHERE section of a SPARQL query. But first things first: the ontology.
Note that the “test” ontology is not officially registered at tracker-project.org. It serves merely as an example.
@prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> .
@prefix xsd: <http://www.w3.org/2001/XMLSchema#> .
@prefix tracker: <http://www.tracker-project.org/ontologies/tracker#> .
@prefix test: <http://www.tracker-project.org/ontologies/test#> .
test: a tracker:Namespace ;
tracker:prefix "test" .
test:Entity a rdfs:Class .
test:Contact a rdfs:Class ;
rdfs:subClassOf test:Entity .
test:Pet a rdfs:Class ;
rdfs:subClassOf test:Entity .
test:Dog a rdfs:Class ;
rdfs:subClassOf test:Entity .
test:Parrot a rdfs:Class ;
rdfs:subClassOf test:Entity .
test:name a rdf:Property ;
rdfs:domain test:Entity ;
rdfs:range xsd:string .
test:owns a rdf:Property ;
rdfs:domain test:Contact ;
rdfs:range test:Pet .
test:hasFriend a rdf:Property ;
rdfs:domain test:Contact ;
rdfs:range test:Contact .
Now that we have meaning, we will introduce the actors: Picca, Max, John and Fred. Copy the @prefix lines of the ontology file from above, put the ontology file in the share/tracker/ontologies directory and run tracker-processes -r before restarting tracker-store in master. After doing all that you can actually store this as a /tmp/import.ttl file and then run tracker-import /tmp/import.ttl and it should import just fine. Ready for the queries below to be executed with the tracker-sparql -q ‘$query’ command.
Note that tracker-processes -r destroys all your RDF data in Tracker. We don’t yet support adding custom ontologies at runtime, so for doing this test you have to start everything from scratch.
<test:Picca> a test:Parrot, test:Pet ;
test:name "Picca" .
<test:Max> a test:Dog, test:Pet ;
test:name "Max" .
<test:John> a test:Contact ;
test:owns <test:Max> ;
test:owns <test:Picca> ;
test:name "John" .
<test:Fred> a test:Contact ;
test:hasFriend <test:John> ;
test:name "Fred" .
Let’s do some simple SPARQL queries. You can execute these queries this way:
tracker-sparql -q "SELECT ?subject WHERE { ?subject a test:Parrot }"
In this query we ask for the subject of each entity that is a parrot. The query will yield test:Picca because Picca is the only parrot in our situation.
test:Picca
Usually we aren’t interested in the subject, but in a real property of the parrot. We can ask for such a property this way:
SELECT ?subject ?name WHERE { ?subject a test:Parrot ; test:name ?name}
test:Picca, Picca
Another simple example, give me all the contacts:
SELECT ?subject WHERE { ?subject a test:Contact }"
test:John
test:Fred
Just the contacts doesn’t illustrate much. Give me all contacts that have a friend. And display the contact and the friend’s names:
SELECT ?name ?friend
WHERE { ?subject test:hasFriend ?f ;
test:name ?name .
?f test:name ?friend }
Fred, John
Let’s ask for all the pets that are owned:
SELECT ?subject WHERE { ?unknown test:owns ?subject }
test:Max
test:Picca
Oh, not the subject. The names. How did we do that again? Right:
SELECT ?name
WHERE { ?unknown test:owns ?subject .
?subject test:name ?name }
Max
Picca
This will of course yield the same results in our situation:
SELECT ?name
WHERE { <test:John> test:owns ?subject .
?subject test:name ?name }
Max
Picca
But this wont, Fred doesn’t own any pets. Only John owns pets.
SELECT ?name
WHERE { <test:Fred> test:owns ?subject .
?subject test:name ?name }
Let’s print the owner’s and the pet’s names:
SELECT ?owner ?name
WHERE { ?unknown test:owns ?subject ;
test:name ?owner .
?subject test:name ?name }"
John, Max
John, Picca
Still with me? Let’s now conclude with requesting the names of the contacts who are a friend of the person who owns Picca:
SELECT ?name
WHERE { ?subject test:owns <test:Picca> .
?unknown test:hasFriend ?subject ;
test:name ?name }
Fred
Invitation for Jürg and Rob: How about you guys writing a introduction to OPTIONAL, SUM, COUNT, GROUP-BY and FILTER, etc in SPARQL? :-) The more advanced stuff.
