Feedback

Share, discover and reuse the Linked Data has never been easier with a LinkedWiki Platform.

Reuse these data in your code

Query, endpoint and code for reusing the same data
https://query.wikidata.org/sparql
PREFIX bd: <http://www.bigdata.com/rdf#>

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wikibase: <http://wikiba.se/ontology#>
#added before 2016-10

# Each composer’s most used tonality, with number of works in that tonality.
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out… follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count > 1) # 7. Identical to 3.
  }
  # 8. That’s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –
  #    that is, where this tonality was used as often as the composer’s most-used tonality.
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement).
Howto write a query SPARQL? (in French)
{{#sparql:PREFIX bd: <http://www.bigdata.com/rdf#>

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wikibase: <http://wikiba.se/ontology#>
#added before 2016-10

# Each composer’s most used tonality, with number of works in that tonality.
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out… follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count > 1) # 7. Identical to 3.
  }
  # 8. That’s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –
  #    that is, where this tonality was used as often as the composer’s most-used tonality.
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement).
| endpoint = https://query.wikidata.org/sparql
| chart=bordercloud.visualization.DataTable
| options=
| log=2
}}
Howto install LinkedWiki in my wiki? Howto insert this graph in my wiki?
Test this script in a new tab.
<html>
    <head>
        <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.4.1/jquery.min.js"  async></script>
    </head>
    <body onload="testQuery();">
        <script>
function testQuery(){
    var endpoint = "https://query.wikidata.org/sparql";
    var query = "PREFIX bd: <http://www.bigdata.com/rdf#>\n\
\n\
PREFIX wdt: <http://www.wikidata.org/prop/direct/>\n\
PREFIX wikibase: <http://wikiba.se/ontology#>\n\
#added before 2016-10\n\
\n\
# Each composer’s most used tonality, with number of works in that tonality.\n\
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)\n\
#\n\
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).\n\
# To understand it, you have to go inside out… follow the numbers.\n\
\n\
SELECT ?composerLabel ?tonalityLabel ?count\n\
WHERE\n\
{\n\
  {\n\
    # 4. Group again, this time just by the composer.\n\
    #    We also select the highest count of a tonality.\n\
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.\n\
    #    So now we have each composer, along with how often they used whatever tonality they used most.\n\
    SELECT ?composer (MAX(?count) AS ?count)\n\
    WHERE\n\
    {\n\
      {\n\
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.\n\
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)\n\
        WHERE\n\
        {\n\
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.\n\
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)\n\
          ?composition wdt:P86 ?composer;\n\
                       wdt:P826 ?tonality.\n\
        }\n\
        GROUP BY ?composer ?tonality\n\
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.\n\
      }\n\
    }\n\
    GROUP BY ?composer\n\
  }\n\
  {\n\
    # 6. Identical to 2.\n\
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)\n\
    WHERE\n\
    {\n\
      # 5. Identical to 1.\n\
      ?composition wdt:P86 ?composer;\n\
                   wdt:P826 ?tonality.\n\
    }\n\
    GROUP BY ?composer ?tonality\n\
    HAVING(?count > 1) # 7. Identical to 3.\n\
  }\n\
  # 8. That’s it. Wait, what?\n\
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.\n\
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.\n\
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,\n\
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –\n\
  #    that is, where this tonality was used as often as the composer’s most-used tonality.\n\
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).\n\
  SERVICE wikibase:label { bd:serviceParam wikibase:language \"en\". }\n\
}\n\
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement)."

   // $('#bodyContentResearch').append(queryDataset);
    $.ajax({
                url: endpoint,
                dataType: 'json',
                data: {
                    queryLn: 'SPARQL',
                    query: query ,
                    limit: 'none',
                    infer: 'true',
                    Accept: 'application/sparql-results+json'
                },
                success: displayResult,
                error: displayError
        });
}

function displayError(xhr, textStatus, errorThrown) {
    console.log(textStatus);
    console.log(errorThrown);
}

function displayResult(data) {
    $.each(data.results.bindings, function(index, bs) {
        console.log(bs);
        $("body").append(JSON.stringify(bs) + "<hr/>");
    });
}

        </script>
    </body>
</html>
Test this script in a new tab (Careful, several charts need a API key).
Howto insert this graph in my html page?
<html>
    <head>
     <link rel="stylesheet" href="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/css/bootstrap.min.css" integrity="sha384-ggOyR0iXCbMQv3Xipma34MD+dH/1fQ784/j6cY/iJTQUOhcWr7x9JvoRxT2MZw1T" crossorigin="anonymous">

     <link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.9.0/css/all.min.css">

     <script
            src="https://code.jquery.com/jquery-3.4.1.min.js"
            integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo="
            crossorigin="anonymous"> </script>
     <script src="https://cdnjs.cloudflare.com/ajax/libs/popper.js/1.14.7/umd/popper.min.js" integrity="sha384-UO2eT0CpHqdSJQ6hJty5KVphtPhzWj9WO1clHTMGa3JDZwrnQq4sF86dIHNDz0W1" crossorigin="anonymous"> </script>
     <script src="https://stackpath.bootstrapcdn.com/bootstrap/4.3.1/js/bootstrap.min.js" integrity="sha384-JjSmVgyd0p3pXB1rRibZUAYoIIy6OrQ6VrjIEaFf/nJGzIxFDsf4x0xIM+B07jRM" crossorigin="anonymous"> </script>

     <script type="text/javascript" src="https://bordercloud.github.io/sgvizler2/sgvizler2/sgvizler2.js" defer> </script>
     <script type="text/javascript" src="https://linkedwiki.com/js/initExampleHTML.js"  defer > </script>
    </head>
<body style="margin:0;">
<div id="sgvzl_example_query"
   data-sgvizler-endpoint="https://query.wikidata.org/sparql"
   data-sgvizler-query="PREFIX bd: &lt;http://www.bigdata.com/rdf#&gt;

PREFIX wdt: &lt;http://www.wikidata.org/prop/direct/&gt;
PREFIX wikibase: &lt;http://wikiba.se/ontology#&gt;
#added before 2016-10

# Each composer&rsquo;s most used tonality, with number of works in that tonality.
# (If this is ambiguous &ndash; multiple tonalities with the same number &ndash; there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out&hellip; follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don&rsquo;t know what tonality this count is associated with &ndash; we&rsquo;ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I&rsquo;m not bothering with any &ldquo;instance of&rdquo; because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count &gt; 1) # 3. Limit that to counts &gt; 1, because using a tonality once is hardly &ldquo;most used&rdquo;.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count &gt; 1) # 7. Identical to 3.
  }
  # 8. That&rsquo;s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 &ndash;
  #    that is, where this tonality was used as often as the composer&rsquo;s most-used tonality.
  #    In other words, this must *be* the composer&rsquo;s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language &quot;en&quot;. }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn&rsquo;t very meaningful for low counts (many compositions aren&rsquo;t on Wikidata or don&rsquo;t have a tonality statement)."
    data-sgvizler-chart='bordercloud.visualization.DataTable'
    data-sgvizler-chart-options=''
    data-sgvizler-endpoint_output_format='json'
    data-sgvizler-log='2'
    style='width:100%; height:auto;'  />

<script>
/*$(function() {
   sgvizler2.containerDrawAll({
       // Google Api key
       googleApiKey : "GOOGLE_MAP_API_KEY",
       // OpenStreetMap Access Token
       //  https://www.mapbox.com/api-documentation/#access-tokens
       osmAccessToken : "OSM_MAP_API_KEY"
     });
});*/
</script>

</body>
</html>
from SPARQLWrapper import SPARQLWrapper, JSON

sparql = SPARQLWrapper("https://query.wikidata.org/sparql")
sparql.setQuery("""
    PREFIX bd: <http://www.bigdata.com/rdf#>

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wikibase: <http://wikiba.se/ontology#>
#added before 2016-10

# Each composer’s most used tonality, with number of works in that tonality.
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out… follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count > 1) # 7. Identical to 3.
  }
  # 8. That’s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –
  #    that is, where this tonality was used as often as the composer’s most-used tonality.
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement).""")
sparql.setReturnFormat(JSON)
results = sparql.query().convert()

for result in results["results"]["bindings"]:
    print(result)
    #print(result["label"]["value"])
Howto use SPARQL with Python ?
library(SPARQL) # SPARQL querying package
library(ggplot2)

# Step 1 - Set up preliminaries and define query
# Define the data.gov endpoint
    endpoint <- "https://query.wikidata.org/sparql"
# create query statement
    query <- "PREFIX bd: <http://www.bigdata.com/rdf#>

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wikibase: <http://wikiba.se/ontology#>
#added before 2016-10

# Each composer’s most used tonality, with number of works in that tonality.
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out… follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count > 1) # 7. Identical to 3.
  }
  # 8. That’s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –
  #    that is, where this tonality was used as often as the composer’s most-used tonality.
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement)."
# Step 2 - Use SPARQL package to submit query and save results to a data frame
qd <- SPARQL(endpoint,query)
df <- qd$results
SPARQL with R in less than 5 minutes
#!/usr/bin/env ruby
#
# Install sparql for Ruby
#   gem update --system
#   gem install sparql
#
require 'sparql/client'

endpoint = "https://query.wikidata.org/sparql"
sparql = <<-EOT
PREFIX bd: <http://www.bigdata.com/rdf#>

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wikibase: <http://wikiba.se/ontology#>
#added before 2016-10

# Each composer’s most used tonality, with number of works in that tonality.
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out… follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count > 1) # 7. Identical to 3.
  }
  # 8. That’s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –
  #    that is, where this tonality was used as often as the composer’s most-used tonality.
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement).
EOT

#For Wikidata, the method get is required
#For other SPARQL endpoints, the method post is prefered
client = SPARQL::Client.new(endpoint, :method => :get)
rows = client.query(sparql)

puts "Number of rows: #{rows.size}"
for row in rows
  for key,val in row do
    # print "#{key.to_s.ljust(10)}: #{val}\t"
    print "#{key}: #{val}\t"
  end
  print "\n"
end
Doc Ruby for SPARQL 1.1
endpoint = 'https://query.wikidata.org/sparql';

query = ['PREFIX bd: <http://www.bigdata.com/rdf#> '...
' '...
'PREFIX wdt: <http://www.wikidata.org/prop/direct/> '...
'PREFIX wikibase: <http://wikiba.se/ontology#> '...
'#added before 2016-10 '...
' '...
'# Each composer’s most used tonality, with number of works in that tonality. '...
'# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.) '...
'# '...
'# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another). '...
'# To understand it, you have to go inside out… follow the numbers. '...
' '...
'SELECT ?composerLabel ?tonalityLabel ?count '...
'WHERE '...
'{ '...
'  { '...
'    # 4. Group again, this time just by the composer. '...
'    #    We also select the highest count of a tonality. '...
'    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that. '...
'    #    So now we have each composer, along with how often they used whatever tonality they used most. '...
'    SELECT ?composer (MAX(?count) AS ?count) '...
'    WHERE '...
'    { '...
'      { '...
'        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality. '...
'        SELECT ?composer ?tonality (COUNT(?composition) AS ?count) '...
'        WHERE '...
'        { '...
'          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality. '...
'          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.) '...
'          ?composition wdt:P86 ?composer; '...
'                       wdt:P826 ?tonality. '...
'        } '...
'        GROUP BY ?composer ?tonality '...
'        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”. '...
'      } '...
'    } '...
'    GROUP BY ?composer '...
'  } '...
'  { '...
'    # 6. Identical to 2. '...
'    SELECT ?composer ?tonality (COUNT(?composition) AS ?count) '...
'    WHERE '...
'    { '...
'      # 5. Identical to 1. '...
'      ?composition wdt:P86 ?composer; '...
'                   wdt:P826 ?tonality. '...
'    } '...
'    GROUP BY ?composer ?tonality '...
'    HAVING(?count > 1) # 7. Identical to 3. '...
'  } '...
'  # 8. That’s it. Wait, what? '...
'  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most. '...
'  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality. '...
'  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined, '...
'  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 – '...
'  #    that is, where this tonality was used as often as the composer’s most-used tonality. '...
'  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count). '...
'  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } '...
'} '...
'ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement). '];

url_head = strcat(endpoint,'?query=');
url_query = urlencode(query);
format = 'text/tab-separated-values';
url_tail = strcat('&format=', format);

url = strcat(url_head, url_query, url_tail);

% get the data from the endpoint
query_results = urlread(url);

% write the data to a file so that tdfread can parse it
fid = fopen('query_results.txt','w');
if fid>=0
    fprintf(fid, '%s\n', query_results)
    fclose(fid)
end

% this reads the tsv file into a struct
sparql_data = tdfread('query_results.txt')
Project Github MatlabSPARQL
<?php
require __DIR__ . '/../vendor/autoload.php';
use BorderCloud\SPARQL\SparqlClient;

$endpoint ="https://query.wikidata.org/sparql";
$sp_readonly = new SparqlClient();
$sp_readonly->setEndpointRead($endpoint);
$q = <<<EOD
PREFIX bd: <http://www.bigdata.com/rdf#>

PREFIX wdt: <http://www.wikidata.org/prop/direct/>
PREFIX wikibase: <http://wikiba.se/ontology#>
#added before 2016-10

# Each composer’s most used tonality, with number of works in that tonality.
# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.)
#
# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another).
# To understand it, you have to go inside out… follow the numbers.

SELECT ?composerLabel ?tonalityLabel ?count
WHERE
{
  {
    # 4. Group again, this time just by the composer.
    #    We also select the highest count of a tonality.
    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that.
    #    So now we have each composer, along with how often they used whatever tonality they used most.
    SELECT ?composer (MAX(?count) AS ?count)
    WHERE
    {
      {
        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality.
        SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
        WHERE
        {
          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality.
          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.)
          ?composition wdt:P86 ?composer;
                       wdt:P826 ?tonality.
        }
        GROUP BY ?composer ?tonality
        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”.
      }
    }
    GROUP BY ?composer
  }
  {
    # 6. Identical to 2.
    SELECT ?composer ?tonality (COUNT(?composition) AS ?count)
    WHERE
    {
      # 5. Identical to 1.
      ?composition wdt:P86 ?composer;
                   wdt:P826 ?tonality.
    }
    GROUP BY ?composer ?tonality
    HAVING(?count > 1) # 7. Identical to 3.
  }
  # 8. That’s it. Wait, what?
  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most.
  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality.
  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined,
  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 –
  #    that is, where this tonality was used as often as the composer’s most-used tonality.
  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count).
  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". }
}
ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement).EOD;
$rows = $sp_readonly->query($q, 'rows');
$err = $sp_readonly->getErrors();
if ($err) {
      print_r($err);
      throw new Exception(print_r($err,true));
}

foreach($rows["result"]["variables"] as $variable){
        printf("%-20.20s",$variable);
        echo '|';
 }
 echo "\n";

foreach ($rows["result"]["rows"] as $row){
        foreach($rows["result"]["variables"] as $variable){
                printf("%-20.20s",$row[$variable]);
        echo '|';
        }
        echo "\n";
 }
 ?>
Project Github BorderCloud/SPARQL
import com.bordercloud.sparql.Endpoint;
import java.util.ArrayList;
import java.util.HashMap;

public class Main {

    public static void main(String[] args) {
        try {
            Endpoint sp = new Endpoint("https://query.wikidata.org/sparql";, false);

            String querySelect = 'PREFIX bd: <http://www.bigdata.com/rdf#> \n'
                    + ' \n'
                    + 'PREFIX wdt: <http://www.wikidata.org/prop/direct/> \n'
                    + 'PREFIX wikibase: <http://wikiba.se/ontology#> \n'
                    + '#added before 2016-10 \n'
                    + ' \n'
                    + '# Each composer’s most used tonality, with number of works in that tonality. \n'
                    + '# (If this is ambiguous – multiple tonalities with the same number – there are multiple results for one composer.) \n'
                    + '# \n'
                    + '# The SPARQL for this is an evil perversion of three subqueries (one of them nested in another). \n'
                    + '# To understand it, you have to go inside out… follow the numbers. \n'
                    + ' \n'
                    + 'SELECT ?composerLabel ?tonalityLabel ?count \n'
                    + 'WHERE \n'
                    + '{ \n'
                    + '  { \n'
                    + '    # 4. Group again, this time just by the composer. \n'
                    + '    #    We also select the highest count of a tonality. \n'
                    + '    #    Notice that we don’t know what tonality this count is associated with – we’ll get to that. \n'
                    + '    #    So now we have each composer, along with how often they used whatever tonality they used most. \n'
                    + '    SELECT ?composer (MAX(?count) AS ?count) \n'
                    + '    WHERE \n'
                    + '    { \n'
                    + '      { \n'
                    + '        # 2. Group by composer and tonality, so that for each composer and tonality, we get a count of how often the composer used this tonality. \n'
                    + '        SELECT ?composer ?tonality (COUNT(?composition) AS ?count) \n'
                    + '        WHERE \n'
                    + '        { \n'
                    + '          # 1. Extremely straightforward the ?composition has the composer ?composer and the tonality ?tonality. \n'
                    + '          #    (I’m not bothering with any “instance of” because the presence of these two properties is a sufficient indicator of ?composition being a composition.) \n'
                    + '          ?composition wdt:P86 ?composer; \n'
                    + '                       wdt:P826 ?tonality. \n'
                    + '        } \n'
                    + '        GROUP BY ?composer ?tonality \n'
                    + '        HAVING(?count > 1) # 3. Limit that to counts > 1, because using a tonality once is hardly “most used”. \n'
                    + '      } \n'
                    + '    } \n'
                    + '    GROUP BY ?composer \n'
                    + '  } \n'
                    + '  { \n'
                    + '    # 6. Identical to 2. \n'
                    + '    SELECT ?composer ?tonality (COUNT(?composition) AS ?count) \n'
                    + '    WHERE \n'
                    + '    { \n'
                    + '      # 5. Identical to 1. \n'
                    + '      ?composition wdt:P86 ?composer; \n'
                    + '                   wdt:P826 ?tonality. \n'
                    + '    } \n'
                    + '    GROUP BY ?composer ?tonality \n'
                    + '    HAVING(?count > 1) # 7. Identical to 3. \n'
                    + '  } \n'
                    + '  # 8. That’s it. Wait, what? \n'
                    + '  #    From 4, we now have ?composer, any composer, and ?count, the count of how often they used whatever tonality they used most. \n'
                    + '  #    From 6, we also have a ?composer, as well as a ?tonality, and the count of how often they used that particular tonality. \n'
                    + '  #    The trick is that ?composer and ?count are the same variable in each subquery, and so now, when the two subqueries are joined, \n'
                    + '  #    we select only that ?tonality from 6 where the ?composer and the ?count are identical to those from 4 – \n'
                    + '  #    that is, where this tonality was used as often as the composer’s most-used tonality. \n'
                    + '  #    In other words, this must *be* the composer’s most-used tonality (except when there are multiple tonalities with the same count). \n'
                    + '  SERVICE wikibase:label { bd:serviceParam wikibase:language "en". } \n'
                    + '} \n'
                    + 'ORDER BY DESC(?count) # 9. Order by count (highest first), because the result isn’t very meaningful for low counts (many compositions aren’t on Wikidata or don’t have a tonality statement). \n';

            HashMap rs = sp.query(querySelect);
            printResult(rs,30);

        }catch(EndpointException eex) {
            System.out.println(eex);
            eex.printStackTrace();
        }
    }

    public static void printResult(HashMap rs , int size) {

      for (String variable : (ArrayList) rs.get("result").get("variables")) {
        System.out.print(String.format("%-"+size+"."+size+"s", variable ) + " | ");
      }
      System.out.print("\n");
      for (HashMap value : (ArrayList>) rs.get("result").get("rows")) {
        //System.out.print(value);
        /* for (String key : value.keySet()) {
         System.out.println(value.get(key));
         }*/
        for (String variable : (ArrayList) rs.get("result").get("variables")) {
          //System.out.println(value.get(variable));
          System.out.print(String.format("%-"+size+"."+size+"s", value.get(variable)) + " | ");
        }
        System.out.print("\n");
      }
    }
}
Project Github BorderCloud/SPARQL-JAVA
Very soon !
TODO