Kingsley Idehen's Blog Data Space

What?

A simple guide usable by any Perl developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing.

Steps:

1. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
2. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

#
# Demonstrating use of a single query to populate a 
# Virtuoso Quad Store via Perl. 
#

# 
# HTTP URL is constructed accordingly with CSV query results format as the default via mime type.
#

use CGI qw/:standard/;
use LWP::UserAgent;
use Data::Dumper;
use Text::CSV_XS;

sub sparqlQuery(@args) {
  my $query=shift;
  my $baseURL=shift;
  my $format=shift;
	
	%params=(
		"default-graph" => "", "should-sponge" => "soft", "query" => $query,
		"debug" => "on", "timeout" => "", "format" => $format,
		"save" => "display", "fname" => ""
	);
	
	@fragments=();
	foreach $k (keys %params) {
		$fragment="$k=".CGI::escape($params{$k});
		push(@fragments,$fragment);
	}
	$query=join("&", @fragments);
	
	$sparqlURL="${baseURL}?$query";
	
	my $ua = LWP::UserAgent->new;
	$ua->agent("MyApp/0.1 ");
	my $req = HTTP::Request->new(GET => $sparqlURL);
	my $res = $ua->request($req);
	$str=$res->content;
	
	$csv = Text::CSV_XS->new();
	
	foreach $line ( split(/^/, $str) ) {
		$csv->parse($line);
		@bits=$csv->fields();
	  push(@rows, [ @bits ] );
	}
	return \@rows;
}


# Setting Data Source Name (DSN)

$dsn="http://dbpedia.org/resource/DBpedia";

# Virtuoso pragmas for instructing SPARQL engine to perform an HTTP GET using the IRI in
# FROM clause as Data Source URL en route to DBMS
# record Inserts.

$query="DEFINE get:soft \"replace\"\n

# Generic (non Virtuoso specific SPARQL
# Note: this will not add records to the 
# DBMS 

SELECT DISTINCT * FROM <$dsn> WHERE {?s ?p ?o}"; 

$data=sparqlQuery($query, "http://localhost:8890/sparql/", "text/csv");

print "Retrieved data:\n";
print Dumper($data);

Output

Retrieved data:
$VAR1 = [
          [
            's',
            'p',
            'o'
          ],
          [
            'http://dbpedia.org/resource/DBpedia',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://www.w3.org/2002/07/owl#Thing'
          ],
          [
            'http://dbpedia.org/resource/DBpedia',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://dbpedia.org/ontology/Work'
          ],
          [
            'http://dbpedia.org/resource/DBpedia',
            'http://www.w3.org/1999/02/22-rdf-syntax-ns#type',
            'http://dbpedia.org/class/yago/Software106566077'
          ],
...

Conclusion

CSV was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Perl developer that already knows how to use Perl for HTTP based data access within HTML. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• RDF::Query::Client Guide
• SPARQL Guide for the Perl Developer
• SPARQL Guide for the PHP Developer
• SPARQL Guide for the Python Developer
• SPARQL Guide for the Ruby Developer
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any Javascript developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing.

Steps:

1. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
2. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

/*
Demonstrating use of a single query to populate a # Virtuoso Quad Store via Javascript. 
*/

/* 
HTTP URL is constructed accordingly with JSON query results format as the default via mime type.
*/

function sparqlQuery(query, baseURL, format) {
	if(!format)
		format="application/json";
	var params={
		"default-graph": "", "should-sponge": "soft", "query": query,
		"debug": "on", "timeout": "", "format": format,
		"save": "display", "fname": ""
	};
	
	var querypart="";
	for(var k in params) {
		querypart+=k+"="+encodeURIComponent(params[k])+"&";
	}
	var queryURL=baseURL + '?' + querypart;
	if (window.XMLHttpRequest) {
  	xmlhttp=new XMLHttpRequest();
  }
  else {
  	xmlhttp=new ActiveXObject("Microsoft.XMLHTTP");
  }
  xmlhttp.open("GET",queryURL,false);
  xmlhttp.send();
  return JSON.parse(xmlhttp.responseText);
}

/*
setting Data Source Name (DSN)
*/

var dsn="http://dbpedia.org/resource/DBpedia";

/*
Virtuoso pragma "DEFINE get:soft "replace" instructs Virtuoso SPARQL engine to perform an HTTP GET using the IRI in FROM clause as Data Source URL with regards to 
DBMS record inserts
*/

var query="DEFINE get:soft \"replace\"\nSELECT DISTINCT * FROM <"+dsn+"> WHERE {?s ?p ?o}"; 
var data=sparqlQuery(query, "/sparql/");

Output

Place the snippet above into the <script/> section of an HTML document to see the query result.

Conclusion

JSON was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Javascript developer that already knows how to use Javascript for HTTP based data access within HTML. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL Guide for the PHP Developer
• SPARQL Guide for the Python Developer
• SPARQL Guide for the Ruby Developer
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any Javascript developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing.

Steps:

1. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
2. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

/*
Demonstrating use of a single query to populate a # Virtuoso Quad Store via Javascript. 
*/

/* 
HTTP URL is constructed accordingly with JSON query results format as the default via mime type.
*/

function sparqlQuery(query, baseURL, format) {
	if(!format)
		format="application/json";
	var params={
		"default-graph": "", "should-sponge": "soft", "query": query,
		"debug": "on", "timeout": "", "format": format,
		"save": "display", "fname": ""
	};
	
	var querypart="";
	for(var k in params) {
		querypart+=k+"="+encodeURIComponent(params[k])+"&";
	}
	var queryURL=baseURL + '?' + querypart;
	if (window.XMLHttpRequest) {
  	xmlhttp=new XMLHttpRequest();
  }
  else {
  	xmlhttp=new ActiveXObject("Microsoft.XMLHTTP");
  }
  xmlhttp.open("GET",queryURL,false);
  xmlhttp.send();
  return JSON.parse(xmlhttp.responseText);
}

/*
setting Data Source Name (DSN)
*/

var dsn="http://dbpedia.org/resource/DBpedia";

/*
Virtuoso pragma "DEFINE get:soft "replace" instructs Virtuoso SPARQL engine to perform an HTTP GET using the IRI in FROM clause as Data Source URL with regards to 
DBMS record inserts
*/

var query="DEFINE get:soft \"replace\"\nSELECT DISTINCT * FROM <"+dsn+"> WHERE {?s ?p ?o}"; 
var data=sparqlQuery(query, "/sparql/");

Output

Place the snippet above into the <script/> section of an HTML document to see the query result.

Conclusion

JSON was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Javascript developer that already knows how to use Javascript for HTTP based data access within HTML. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL Guide for the PHP Developer
• SPARQL Guide for the Python Developer
• SPARQL Guide for the Ruby Developer
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any PHP developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing e.g. local object binding re. PHP.

Steps:

1. From your command line execute: aptitude search '^PHP26', to verify PHP is in place
2. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
3. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

#!/usr/bin/env php
<?php
#
# Demonstrating use of a single query to populate a # Virtuoso Quad Store via PHP. 
#

# HTTP URL is constructed accordingly with JSON query results format in mind.

function sparqlQuery($query, $baseURL, $format="application/json")

  {
	$params=array(
		"default-graph" =>  "",
		"should-sponge" =>  "soft",
		"query" =>  $query,
		"debug" =>  "on",
		"timeout" =>  "",
		"format" =>  $format,
		"save" =>  "display",
		"fname" =>  ""
	);

	$querypart="?";	
	foreach($params as $name => $value) 
  {
		$querypart=$querypart . $name . '=' . urlencode($value) . "&";
	}
	
	$sparqlURL=$baseURL . $querypart;
	
	return json_decode(file_get_contents($sparqlURL));
};



# Setting Data Source Name (DSN)
$dsn="http://dbpedia.org/resource/DBpedia";

#Virtuoso pragmas for instructing SPARQL engine to perform an HTTP GET
#using the IRI in FROM clause as Data Source URL

$query="DEFINE get:soft \"replace\"
SELECT DISTINCT * FROM <$dsn> WHERE {?s ?p ?o}"; 

$data=sparqlQuery($query, "http://localhost:8890/sparql/");

print "Retrieved data:\n" . json_encode($data);

?>

Output

Retrieved data:
  {"head":
  {"link":[],"vars":["s","p","o"]},
  "results":
		{"distinct":false,"ordered":true,
		"bindings":[
			{"s":
			{"type":"uri","value":"http:\/\/dbpedia.org\/resource\/DBpedia"},"p":
			{"type":"uri","value":"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#type"},"o":
			{"type":"uri","value":"http:\/\/www.w3.org\/2002\/07\/owl#Thing"}},
			{"s":
			{"type":"uri","value":"http:\/\/dbpedia.org\/resource\/DBpedia"},"p":
			{"type":"uri","value":"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#type"},"o":
			{"type":"uri","value":"http:\/\/dbpedia.org\/ontology\/Work"}},
			{"s":
			{"type":"uri","value":"http:\/\/dbpedia.org\/resource\/DBpedia"},"p":
			{"type":"uri","value":"http:\/\/www.w3.org\/1999\/02\/22-rdf-syntax-ns#type"},"o":
			{"type":"uri","value":"http:\/\/dbpedia.org\/class\/yago\/Software106566077"}},
...

Conclusion

JSON was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a PHP developer that already knows how to use PHP for HTTP based data access. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL Guide for the Python Developer
• SPARQL Guide for the Ruby Developer
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any Python developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing e.g. local object binding re. Python.

Steps:

1. From your command line execute: aptitude search '^python26', to verify Python is in place
2. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
3. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

#!/usr/bin/env python
#
# Demonstrating use of a single query to populate a # Virtuoso Quad Store via Python. 
#

import urllib, json

# HTTP URL is constructed accordingly with JSON query results format in mind.

def sparqlQuery(query, baseURL, format="application/json"):
	params={
		"default-graph": "",
		"should-sponge": "soft",
		"query": query,
		"debug": "on",
		"timeout": "",
		"format": format,
		"save": "display",
		"fname": ""
	}
	querypart=urllib.urlencode(params)
	response = urllib.urlopen(baseURL,querypart).read()
	return json.loads(response)

# Setting Data Source Name (DSN)
dsn="http://dbpedia.org/resource/DBpedia"

# Virtuoso pragmas for instructing SPARQL engine to perform an HTTP GET
# using the IRI in FROM clause as Data Source URL

query="""DEFINE get:soft "replace"
SELECT DISTINCT * FROM <%s> WHERE {?s ?p ?o}""" % dsn 

data=sparqlQuery(query, "http://localhost:8890/sparql/")

print "Retrieved data:\n" + json.dumps(data, sort_keys=True, indent=4)

#
# End

Output

Retrieved data:
{
    "head": {
        "link": [], 
        "vars": [
            "s", 
            "p", 
            "o"
        ]
    }, 
    "results": {
        "bindings": [
            {
                "o": {
                    "type": "uri", 
                    "value": "http://www.w3.org/2002/07/owl#Thing"
                }, 
                "p": {
                    "type": "uri", 
                    "value": "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"
                }, 
                "s": {
                    "type": "uri", 
                    "value": "http://dbpedia.org/resource/DBpedia"
                }
            }, 
...

Conclusion

JSON was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Python developer that already knows how to use Python for HTTP based data access. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL Guide for the Ruby Developer
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any Python developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing e.g. local object binding re. Python.

Steps:

1. From your command line execute: aptitude search '^python26', to verify Python is in place
2. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
3. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

#!/usr/bin/env python
#
# Demonstrating use of a single query to populate a # Virtuoso Quad Store via Python. 
#

import urllib, json

# HTTP URL is constructed accordingly with JSON query results format in mind.

def sparqlQuery(query, baseURL, format="application/json"):
	params={
		"default-graph": "",
		"should-sponge": "soft",
		"query": query,
		"debug": "on",
		"timeout": "",
		"format": format,
		"save": "display",
		"fname": ""
	}
	querypart=urllib.urlencode(params)
	response = urllib.urlopen(baseURL,querypart).read()
	return json.loads(response)

# Setting Data Source Name (DSN)
dsn="http://dbpedia.org/resource/DBpedia"

# Virtuoso pragmas for instructing SPARQL engine to perform an HTTP GET
# using the IRI in FROM clause as Data Source URL

query="""DEFINE get:soft "replace"
SELECT DISTINCT * FROM <%s> WHERE {?s ?p ?o}""" % dsn 

data=sparqlQuery(query, "http://localhost:8890/sparql/")

print "Retrieved data:\n" + json.dumps(data, sort_keys=True, indent=4)

#
# End

Output

Retrieved data:
{
    "head": {
        "link": [], 
        "vars": [
            "s", 
            "p", 
            "o"
        ]
    }, 
    "results": {
        "bindings": [
            {
                "o": {
                    "type": "uri", 
                    "value": "http://www.w3.org/2002/07/owl#Thing"
                }, 
                "p": {
                    "type": "uri", 
                    "value": "http://www.w3.org/1999/02/22-rdf-syntax-ns#type"
                }, 
                "s": {
                    "type": "uri", 
                    "value": "http://dbpedia.org/resource/DBpedia"
                }
            }, 
...

Conclusion

JSON was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Python developer that already knows how to use Python for HTTP based data access. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL Guide for the Ruby Developer
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any Ruby developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing e.g. local object binding re. Ruby.

Steps:

1. From your command line execute: aptitude search '^ruby', to verify Ruby is in place
2. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
3. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

#!/usr/bin/env ruby
#
# Demonstrating use of a single query to populate a # Virtuoso Quad Store. 
#

require 'net/http'
require 'cgi'
require 'csv'

#
# We opt for CSV based output since handling this format is straightforward in Ruby, by default.
# HTTP URL is constructed accordingly with CSV as query results format in mind.

def sparqlQuery(query, baseURL, format="text/csv")
	params={
		"default-graph" => "",
		"should-sponge" => "soft",
		"query" => query,
		"debug" => "on",
		"timeout" => "",
		"format" => format,
		"save" => "display",
		"fname" => ""
	}
	querypart=""
	params.each { |k,v|
		querypart+="#{k}=#{CGI.escape(v)}&"
	}
  
	sparqlURL=baseURL+"?#{querypart}"
	
	response = Net::HTTP.get_response(URI.parse(sparqlURL))

	return CSV::parse(response.body)
	
end

# Setting Data Source Name (DSN)

dsn="http://dbpedia.org/resource/DBpedia"

#Virtuoso pragmas for instructing SPARQL engine to perform an HTTP GET
#using the IRI in FROM clause as Data Source URL

query="DEFINE get:soft \"replace\"
SELECT DISTINCT * FROM <#{dsn}> WHERE {?s ?p ?o} "

#Assume use of local installation of Virtuoso 
#otherwise you can change URL to that of a public endpoint
#for example DBpedia: http://dbpedia.org/sparql

data=sparqlQuery(query, "http://localhost:8890/sparql/")

puts "Got data:"
p data

#
# End

Output

Got data:
[["s", "p", "o"], 
  ["http://dbpedia.org/resource/DBpedia", 
   "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", 
   "http://www.w3.org/2002/07/owl#Thing"], 
  ["http://dbpedia.org/resource/DBpedia", 
   "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", 
   "http://dbpedia.org/ontology/Work"], 
  ["http://dbpedia.org/resource/DBpedia", 
   "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", 
   "http://dbpedia.org/class/yago/Software106566077"],
...

Conclusion

CSV was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Ruby developer that already knows how to use Ruby for HTTP based data access. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL and Ruby SPARQL Client Library Example
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

What?

A simple guide usable by any Ruby developer seeking to exploit SPARQL without hassles.

Why?

SPARQL is a powerful query language, results serialization format, and an HTTP based data access protocol from the W3C. It provides a mechanism for accessing and integrating data across Deductive Database Systems (colloquially referred to as triple or quad stores in Semantic Web and Linked Data circles) -- database systems (or data spaces) that manage proposition oriented records in 3-tuple (triples) or 4-tuple (quads) form.

How?

SPARQL queries are actually HTTP payloads (typically). Thus, using a RESTful client-server interaction pattern, you can dispatch calls to a SPARQL compliant data server and receive a payload for local processing e.g. local object binding re. Ruby.

Steps:

1. From your command line execute: aptitude search '^ruby', to verify Ruby is in place
2. Determine which SPARQL endpoint you want to access e.g. DBpedia or a local Virtuoso instance (typically: http://localhost:8890/sparql).
3. If using Virtuoso, and you want to populate its quad store using SPARQL, assign "SPARQL_SPONGE" privileges to user "SPARQL" (this is basic control, more sophisticated WebID based ACLs are available for controlling SPARQL access).

Script:

#!/usr/bin/env ruby
#
# Demonstrating use of a single query to populate a # Virtuoso Quad Store. 
#

require 'net/http'
require 'cgi'
require 'csv'

#
# We opt for CSV based output since handling this format is straightforward in Ruby, by default.
# HTTP URL is constructed accordingly with CSV as query results format in mind.

def sparqlQuery(query, baseURL, format="text/csv")
	params={
		"default-graph" => "",
		"should-sponge" => "soft",
		"query" => query,
		"debug" => "on",
		"timeout" => "",
		"format" => format,
		"save" => "display",
		"fname" => ""
	}
	querypart=""
	params.each { |k,v|
		querypart+="#{k}=#{CGI.escape(v)}&"
	}
  
	sparqlURL=baseURL+"?#{querypart}"
	
	response = Net::HTTP.get_response(URI.parse(sparqlURL))

	return CSV::parse(response.body)
	
end

# Setting Data Source Name (DSN)

dsn="http://dbpedia.org/resource/DBpedia"

#Virtuoso pragmas for instructing SPARQL engine to perform an HTTP GET
#using the IRI in FROM clause as Data Source URL

query="DEFINE get:soft \"replace\"
SELECT DISTINCT * FROM <#{dsn}> WHERE {?s ?p ?o} "

#Assume use of local installation of Virtuoso 
#otherwise you can change URL to that of a public endpoint
#for example DBpedia: http://dbpedia.org/sparql

data=sparqlQuery(query, "http://localhost:8890/sparql/")

puts "Got data:"
p data

#
# End

Output

Got data:
[["s", "p", "o"], 
  ["http://dbpedia.org/resource/DBpedia", 
   "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", 
   "http://www.w3.org/2002/07/owl#Thing"], 
  ["http://dbpedia.org/resource/DBpedia", 
   "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", 
   "http://dbpedia.org/ontology/Work"], 
  ["http://dbpedia.org/resource/DBpedia", 
   "http://www.w3.org/1999/02/22-rdf-syntax-ns#type", 
   "http://dbpedia.org/class/yago/Software106566077"],
...

Conclusion

CSV was chosen over XML (re. output format) since this is about a "no-brainer installation and utilization" guide for a Ruby developer that already knows how to use Ruby for HTTP based data access. SPARQL just provides an added bonus to URL dexterity (delivered via URI abstraction) with regards to constructing Data Source Names or Addresses.

Related

• SPARQL and Ruby SPARQL Client Library Example
• Simple Guide for using SPARQL with Virtuoso
• General SPARQL Tutorial Collection
• Virtuoso Specific SPARQL Tutorial Collection
• The URI, URL, and Linked Data Meme's Generic HTTP URI.

Injecting Linked Data into the Web has been a major pain point for those who seek personal, service, or organization-specific variants of DBpedia. Basically, the sequence goes something like this:

1. You encounter DBpedia or the LOD Cloud Pictorial.
2. You look around (typically following your nose from link to link).
3. You attempt to publish your own stuff.
4. You get stuck.

The problems typically take the following form:

1. Functionality confusion about the complementary Name and Address functionality of a single URI abstraction
2. Terminology confusion due to conflation and over-loading of terms such as Resource, URL, Representation, Document, etc.
3. Inability to find robust tools with which to generate Linked Data from existing data sources such as relational databases, CSV files, XML, Web Services, etc.

To start addressing these problems, here is a simple guide for generating and publishing Linked Data using Virtuoso.

Step 1 - RDF Data Generation

Existing RDF data can be added to the Virtuoso RDF Quad Store via a variety of built-in data loader utilities.

Many options allow you to easily and quickly generate RDF data from other data sources:

• Install the Sponger Bookmarklet for the URIBurner service. Bind this to your own SPARQL-compliant backend RDF database (in this scenario, your local Virtuoso instance), and then Sponge some HTTP-accessible resources.
• Convert relational DBMS data to RDF using the Virtuoso RDF Views Wizard.
• Starting with CSV files, you can
  • Place them at an HTTP-accessible location, and use the Virtuoso Sponger to convert them to RDF or;
  • Use the CVS import feature to import their content into Virtuoso's relational data engine; then use the built-in RDF Views Wizard as with other RDBMS data.
• Starting from XML files, you can
  • Use Virtuoso's inbuilt XSLT-Processor for manual XML to RDF/XML transformation or;
  • Leverage the Sponger Cartridge for GRDDL, if there is a transformation service associated with your XML data source, or;
  • Let the Sponger analyze the XML data source and make a best-effort transformation to RDF.

Step 2 - Linked Data Deployment

Install the Faceted Browser VAD package (fct_dav.vad) which delivers the following:

1. Faceted Browser Engine UI
2. Dynamic Hypermedia Resource Generator
   • delivers descriptor resources for every entity (data object) in the Native or Virtual Quad Stores
   • supports a broad array of output formats, including HTML+RDFa, RDF/XML, N3/Turtle, NTriples, RDF-JSON, OData+Atom, and OData+JSON.

Step 3 - Linked Data Consumption & Exploitation

Three simple steps allow you, your enterprise, and your customers to consume and exploit your newly deployed Linked Data --

1. Load a page like this in your browser: http://<cname>[:<port>]/describe/?uri=<entity-uri>
   • <cname>[:<port>] gets replaced by the host and port of your Virtuoso instance
   • <entity-uri> gets replaced by the URI you want to see described -- for instance, the URI of one of the resources you let the Sponger handle.
2. Follow the links presented in the descriptor page.
3. If you ever see a blank page with a hyperlink subject name in the About: section at the top of the page, simply add the parameter "&sp=1" to the URL in the browser's Address box, and hit [ENTER]. This will result in an "on the fly" resource retrieval, transformation, and descriptor page generation.
4. Use the navigator controls to page up and down the data associated with the "in scope" resource descriptor.

Related

• Sample Descriptor Page (what you see post completion of the steps in this post)
• What is Linked Data, really?
• Painless Linked Data Generation via URIBurner
• How To Load RDF Data Into Virtuoso (various methods)
• Virtuoso Bulk Loader Script for RDF
• Bulk Loader Script for CSV
• Wizard based generation of RDF based Linked Data from ODBC accessible Relational Databases

Linked Data is simply hypermedia-based structured data.

Linked Data offers everyone a Web-scale, Enterprise-grade mechanism for platform-independent creation, curation, access, and integration of data.

The fundamental steps to creating Linked Data are as follows:

1. Choose a Name Reference Mechanism — i.e., URIs.

2. Choose a Data Model with which to Structure your Data — minimally, you need a model which clearly distinguishes

   1. Subjects (also known as Entities)
   2. Subject Attributes (also known as Entity Attributes), and
   3. Attribute Values (also known as Subject Attribute Values or Entity Attribute Values).

3. Choose one or more Data Representation Syntaxes (also called Markup Languages or Data Formats) to use when creating Resources with Content based on your chosen Data Model. Some Syntaxes in common use today are HTML+RDFa, N3, Turtle, RDF/XML, TriX, XRDS, GData, OData, OpenGraph, and many others.

4. Choose a URI Scheme that facilitates binding Referenced Names to the Resources which will carry your Content -- your Structured Data.

5. Create Structured Data by using your chosen Name Reference Mechanism, your chosen Data Model, and your chosen Data Representation Syntax, as follows:

   1. Identify Subject(s) using Resolvable URI(s).
   2. Identify Subject Attribute(s) using Resolvable URI(s).
   3. Assign Attribute Values to Subject Attributes. These Values may be either Literals (e.g., STRINGs, BLOBs) or Resolvable URIs.

You can create Linked Data (hypermedia-based data representations) Resources from or for many things. Examples include: personal profiles, calendars, address books, blogs, photo albums; there are many, many more.

Related

1. Linked Data an Introduction -- simple introduction to Linked Data and its virtues
2. How Data Makes Corporations Dumb -- Jeff Jonas (IBM) interview
3. Hypermedia Types -- evolving information portal covering different aspects of Hypermedia resource types
4. URIBurner -- service that generates Linked Data from a plethora of heterogeneous data sources
5. Linked Data Meme -- TimbL design issues note about Linked Data
6. Data 3.0 Manifesto -- note about format agnostic Linked Data
7. DBpedia -- large Linked Data Hub
8. Linked Open Data Cloud -- collection of Linked Data Spaces
9. Linked Open Commerce Cloud -- commerce (clicks & mortar and/or clicks & clicks) oriented Linked Data Space
10. LOD Cloud Cache -- massive Linked Data Space hosting most of the LOD Cloud Datasets
11. LOD2 Initiative -- EU Co-Funded Project to develop global knowledge space from LOD
.