SPARQL Examples: Difference between revisions

Revision as of 20:07, 14 February 2022

This page will be updated with SPARQL examples as the course progresses.

SPARQL Examples from Session 3: SPARQL

Prefixes used

The examples below will assume that these are in place.

PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX owl: <http://www.w3.org/2002/07/owl#>
PREFIX dc: <http://purl.org/dc/terms/>
PREFIX bibo: <http://purl.org/ontology/bibo/>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX skos: <http://www.w3.org/2004/02/skos/core#>
PREFIX ss: <http://semanticscholar.org/>
PREFIX kg: <http://i2s.uib.no/kg4news/>
PREFIX sp: <http://i2s.uib.no/kg4news/science-parse/>
PREFIX th: <http://i2s.uib.no/kg4news/theme/>

PREFIX ex: <http://example.org/> 
PREFIX xml: <http://www.w3.org/XML/1998/namespace> 
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>

select all triplets in graph

SELECT ?s ?p ?o
WHERE {
   ?s ?p ?o .
}

select the interestes of Cade

SELECT ?cadeInterest
WHERE {
   ex:Cade ex:interest ?cadeInterest .
}

select the country and city where Emma lives

SELECT ?emmaCity ?emmaCountry
WHERE {
   ex:Emma ex:address ?address .
 	?address ex:city ?emmaCity .
 	?address ex:country ?emmaCountry .
}

select the people who are over 26 years old

SELECT ?person ?age
WHERE {
   ?person ex:age ?age .
 	FILTER(?age > 26) .     
}

select people who graduated with Bachelor

SELECT ?person ?degree
WHERE {
   ?person ex:degree ?degree .
 	?degree ex:degreeLevel "Bachelor" .
         
}

delete cades photography interest

DELETE DATA
{
   ex:Cade ex:interest ex:Photography .
}

delete and insert university of valencia

DELETE { ?s ?p ex:University_of_Valencia }
INSERT { ?s ?p ex:Universidad_de_Valencia }
WHERE  { ?s ?p ex:University_of_Valencia }

check if the deletion worked

SELECT ?s ?o2
WHERE  { 
 ?s ex:degree ?o .
 ?o ex:degreeSource ?o2 .
      	}

insert Sergio

INSERT DATA {
 ex:Sergio a foaf:Person ;
   ex:address [ a ex:Address ;
           ex:city ex:Valenciay ;
           ex:country ex:Spain ;
           ex:postalCode "46021"^^xsd:string ;
           ex:state ex:California ;
           ex:street "4_Carrer_del_Serpis"^^xsd:string ] ;
   ex:degree [ ex:degreeField ex:Computer_science ;
           ex:degreeLevel "Master"^^xsd:string ;
           ex:degreeSource ex:University_of_Valencia ;
           ex:year "2008"^^xsd:gYear ] ;
   ex:expertise ex:Big_data,
       ex:Semantic_technologies,
       ex:Machine_learning;
   foaf:name "Sergio_Pastor"^^xsd:string .

}

describe Sergio

DESCRIBE ex:Sergio ?o
WHERE {
 ex:Sergio ?p ?o .
  ?o ?p2 ?o2 .
 }

construct that any city is in the country in an address

CONSTRUCT {?city ex:locatedIn ?country}
Where {
 ?s rdf:type ex:Address .
 ?s ex:city ?city .
 ?s ex:country ?country.
 }

SELECT DISTINCT ?p WHERE {
  ?s rdf:type ss:Paper .
  ?s ?p ?o .
 } LIMIT 100

Explain all types and properties

SELECT ?pt ?e WHERE {
  ?pt rdfs:comment ?e .
} LIMIT 100

List main papers

SELECT * WHERE {
 
  ?paper rdf:type kg:MainPaper .
  ?paper dc:date ?year .
 
}

The data are available in this Blazegraph triple store: http://sandbox.i2s.uib.no , but you may need to be inside the UiB network (or on VPN.)

List properties

SELECT DISTINCT ?p WHERE {
  ?s ?p ?o .
} LIMIT 100

List types

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>

SELECT DISTINCT ?t WHERE {
  ?s rdf:type ?t .
} LIMIT 100

List authors

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>

SELECT DISTINCT ?p WHERE {
  ?s rdf:type foaf:Person .
  ?s ?p ?o .
} LIMIT 100

Add this to show datatypes!

  BIND ( DATATYPE(?year) AS ?type )

Add this to only show years with the right type.

  FILTER ( DATATYPE(?year) = xsd:gYear )

Group and count main papers by year

SELECT ?year (COUNT(?paper) AS ?count) WHERE {

  ?paper rdf:type kg:MainPaper .
  ?paper dc:date ?year .
  FILTER ( DATATYPE(?year) = xsd:gYear )

}
GROUP BY ?year

Add this to order the results

ORDER BY ?year

Add this to order and only show years with more than 5 papers.

HAVING (?count > 5)
ORDER BY DESC(?count)

Show papers

SELECT ?paper ?year WHERE {

  ?paper rdf:type kg:MainPaper .
  ?paper dc:date ?year .
  FILTER ( DATATYPE(?year) = xsd:gYear )

}

Change last lines to show papers without an xsd:gYear too.

  OPTIONAL {
      ?paper dc:date ?year .
      FILTER ( DATATYPE(?year) = xsd:gYear )
  }

Alternative values for variables

SELECT ?p ?n ?year WHERE {
  ?p rdf:type kg:MainPaper .
  ?p dc:contributor ?a .
  ?a foaf:name ?n .
  ?p dc:date ?year .
  FILTER ( CONTAINS( ?n, ?str ) )
  FILTER ( CONTAINS( STR(?year), ?yr) )
 
  VALUES ?str { "Andreas" "David" }
  VALUES ?yr { "2020" "2019" }
}

Property paths (composite properties)

This query:

SELECT ?p ?n WHERE {
  ?p rdf:type kg:MainPaper .
  ?p dc:contributor ?c .
  ?c foaf:name ?n .
}

Can be simplified by eliminating ?c:

SELECT ?p ?n WHERE {
  ?p rdf:type kg:MainPaper .
  ?p dc:contributor / foaf:name ?n .
}

Can be further simplified by first reversing rdf:type:

SELECT ?p ?n WHERE {
  kg:MainPaper ^rdf:type ?p .
  ?p dc:contributor / foaf:name ?n .
}

...and the eliminating ?p:

SELECT ?n WHERE {
  kg:MainPaper ^rdf:type / dc:contributor / foaf:name ?n .
}

Retrieve titles of papers that mention SPARQL

Get papers with topics labelled "SPARQL":

SELECT ?t WHERE {
  ?t ^dc:title / dc:subject / skos:prefLabel "SPARQL" .
}

Some labels also go via a theme:

SELECT ?t WHERE {
  ?t ^dc:title / dc:subject / th:theme / skos:prefLabel "SPARQL" .
}

We can get both using a path with an optional element (the '?'):

SELECT ?t WHERE {
  ?t ^dc:title / dc:subject / th:theme? / skos:prefLabel "SPARQL" .
}

Using an external SPARQL endpoint

We limit to a single label to avoid time-outs and rate limitations:

SELECT ?a ?n ?r WHERE {
  ?a rdf:type ss:Topic .
  ?a skos:prefLabel ?n .
  FILTER ( ?n = "SPARQL" )
  BIND ( STRLANG( ?n, "en" ) AS ?n2 )
    SERVICE <https://dbpedia.org/sparql> {
    ?r rdfs:label ?n2 .
    }
} LIMIT 1

Insert 4-digit years for all main papers

Main papers that do not have an xsd:gYear:

SELECT * WHERE {
 
  ?p rdf:type kg:MainPaper .
  ?p dc:date ?d .
  FILTER ( DATATYPE(?d) = xsd:gYear )
 
}

Show the datatypes:

SELECT * WHERE {
 
  ?p rdf:type kg:MainPaper .
  ?p dc:date ?d .
  FILTER ( DATATYPE(?d) = xsd:dateTime )
  BIND ( year( ?d ) AS ?dt )
 
}

Insert 4-digit years:

INSERT { ?paper dc:date ?year } 
WHERE {
  
 ?paper rdf:type kg:MainPaper .
 ?paper dc:date ?date .
  FILTER( DATATYPE(?date) != xsd:gYear )
  BIND ( YEAR(?date) AS ?year ) 
  
}

(Actually, these years are xsd:integer- s, not quite xsd:gYear-s.)

SPARQL Examples from Session 7: RDFS

Turn on inference!

Make sure inference is on in your triple store, or that you compute closures if you run this in Python with rdflib and OWL-RL.

In Blazegraph, create a new "Namespace" with the "Inference" box checked. Remember to "Use" the new namespace.

In Python, install the OWL-RL package (pip install owlrl). Explicitly compute RDFS closure like this:

import owlrl.RDFSClosure

...

rdfs = owlrl.RDFSClosure.RDFS_Semantics(g, False, False, False)
rdfs.closure()
rdfs.flush_stored_triples()

rdfs:subClassOf entailment

Update:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX kg: <http://i2s.uib.no/kg4news/>

INSERT DATA {
    kg:TimBernersLee rdf:type kg:Author .
    kg:Author rdfs:subClassOf foaf:Person . 
}

Query:

PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX kg: <http://i2s.uib.no/kg4news/>

ASK {
    kg:TimBernersLee rdf:type foaf:Person . 
}

rdfs:domain entailment