SlideShare une entreprise Scribd logo
1  sur  41
Télécharger pour lire hors ligne
Tutorial: Querying DBpedia
Web Technology
2ID60
14 November 2013
Dr. Katrien Verbert
Dr. ir. Natasha Stash
Dr. George Fletcher
Overview
• 
• 
• 
• 
• 

Introduction to Jena
Setting up the environment
Querying DBpedia
Other APIs
PHP example
Jena
•  Jena is a Java framework for the creation of applications
for the Semantic Web
•  Provides interfaces and classes for the creation and
manipulation of RDF repositories
RDF concepts
Capabilities of Jena
• 
• 
• 
• 

RDF API
Reading and writing in RDF/XML, N-Triples
In-memory and persistent storage
SPARQL query engine
RDF concepts
•  The Jena RDF API contains classes and interfaces for every
important aspect of the RDF specification
•  They can be used in order to construct RDF graphs from
scratch, or edit existent graphs
•  These classes/interfaces reside in the
com.hp.hpl.jena.rdf.model package
•  In Jena, the Model interface is used to represent RDF
graphs
•  Through Model, statements can be obtained/ created/
removed etc
RDF concepts
// Create an empty model
Model model = ModelFactory.createDefaultModel();
String ns = new String("http://www.example.com/example#");
// Create two Resources
Resource john = model.createResource(ns + "John");
Resource jane = model.createResource(ns + "Jane");
// Create the 'hasBrother' Property declaration
Property hasBrother = model.createProperty(ns, "hasBrother");
// Associate jane to john through 'hasBrother'
jane.addProperty(hasBrother, john);
// Create the 'hasSister' Property declaration
Property hasSister = model.createProperty(ns, "hasSister");
// Associate john and jane through 'hasSister' with a Statement
Statement sisterStmt = model.createStatement(john, hasSister, jane);
model.add(sisterStmt);
SPARQL query processing
•  Jena uses the ARQ engine for the processing of
SPARQL queries
•  The ARQ API classes are found in com.hp.hpl.jena.query

•  Basic classes in ARQ:
•  Query: Represents a single SPARQL query.
•  Dataset: The knowledge base on which queries are executed
(Equivalent to RDF Models)
•  QueryFactory: Can be used to generate Query objects from
SPARQL strings
•  QueryExecution: Provides methods for the execution of queries
•  ResultSet: Contains the results obtained from an executed query
•  QuerySolution: Represents a row of query results.
•  If there are many answers to a query, a ResultSet is returned after
the query is executed. The ResultSet contains many QuerySolutions
SPARQL query processing
// Prepare query string
String queryString =
"PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>n" +
"PREFIX : <http://www.example.com/onto1#>n" +
"SELECT ?married ?spouse WHERE {" +
"?married rdf:type :MarriedPerson.n" +
"?married :hasSpouse ?spouse." +
"}";
// Use the ontology model to create a Dataset object
// Note: If no reasoner has been attached to the model, no results
// will be returned (MarriedPerson has no asserted instances)
Dataset dataset = DatasetFactory.create(ontModel);
// Parse query string and create Query object
Query q = QueryFactory.create(queryString);
// Execute query and obtain result set
QueryExecution qexec = QueryExecutionFactory.create(q, dataset);
ResultSet resultSet = qexec.execSelect();
SPARQL query processing

// Print results
while(resultSet.hasNext()) {
// Each row contains two fields: ‘married’ and ‘spouse’,
// as defined in the query string
QuerySolution row = (QuerySolution)resultSet.next();
RDFNode nextMarried = row.get("married");
System.out.print(nextMarried.toString());
System.out.print(" is married to ");
RDFNode nextSpouse = row.get("spouse");
System.out.println(nextSpouse.toString());
}
ARQ Application API
http://jena.apache.org/documentation/query/app_api.html
Overview
• 
• 
• 
• 

Introduction to Jena
Setting up the environment
Querying Dbpedia
Other APIs
Setting up the environment
Download Netbeans Java EE version:
https://netbeans.org/downloads/
Downloading Jena

http://jena.apache.org
Download binary distribution
http://www.apache.org/dist/jena/
Getting started with Jena in Netbeans
Create a new Java project
Create a Java project
Add Jena libraries to class path
Add Jena libraries to class path
Add all jars in lib folder of Jena distribution
Add all jars in lib folder
Using Jena with Eclipse
•  http://www.iandickinson.me.uk/articles/jena-eclipsehelloworld/
Tutorials

http://jena.apache.org/getting_started/
Overview
• 
• 
• 
• 

Introduction to Jena
Setting up the environment
Querying Dbpedia
Other APIs
QueryFactory
•  has various create() methods to read a textual query
•  these create() methods
•  return a Query object,
•  which encapsulates a parsed query.
QueryExecutionFactory
Create a QueryExecution that will access a SPARQL
service over HTTP
QueryExecutionFactory.sparqlService(String service,
Query query)
Querying Dbpedia
SPARQL endpoint
http://dbpedia.org/sparql
Example
String service = "http://dbpedia.org/sparql";
String query = "ASK { }";
QueryExecution qe = QueryExecutionFactory.sparqlService(service,
query);
Test connection
import com.hp.hpl.jena.query.QueryExecution;
import com.hp.hpl.jena.query.QueryExecutionFactory;
import com.hp.hpl.jena.sparql.engine.http.QueryExceptionHTTP;
public class QueryTest {
public static void main(String[] args) {
String service = "http://dbpedia.org/sparql";
String query = "ASK { }";
QueryExecution qe = QueryExecutionFactory.sparqlService(service, query);
try {
if (qe.execAsk()) {
System.out.println(service + " is UP");
} // end if
} catch (QueryExceptionHTTP e) {
System.out.println(service + " is DOWN");
} finally {
qe.close();
}
}
}
Example queries
http://wiki.dbpedia.org/OnlineAccess#h28-5
Example query: people who were born in Eindhoven
String service="http://dbpedia.org/sparql";
String query="PREFIX dbo:<http://dbpedia.org/ontology/>"
+ "PREFIX : <http://dbpedia.org/resource/>"
+ "select ?person where {?person dbo:birthPlace :Eindhoven.}";
QueryExecution qe=QueryExecutionFactory.sparqlService(service, query);
ResultSet rs=qe.execSelect();
while (rs.hasNext()){
QuerySolution s=rs.nextSolution();
System.out.println(s.getResource("?person").toString());
}

03/28/11
Processing results
QuerySolution soln = results.nextSolution() ;
RDFNode x = soln.get("varName") ; // Get a result variable by name.
Resource r = soln.getResource("VarR") ; // Get a result variable - must be a resource
Literal l = soln.getLiteral("VarL") ; // Get a result variable - must be a literal
Example
String service="http://dbpedia.org/sparql";
String query="PREFIX dbo:<http://dbpedia.org/ontology/>"
+ "PREFIX : <http://dbpedia.org/resource/>"
+ "PREFIX foaf:<http://xmlns.com/foaf/0.1/>"
+ "select ?person ?name where {?person dbo:birthPlace :Eindhoven."
+ "?person foaf:name ?name}";
QueryExecution qe=QueryExecutionFactory.sparqlService(service, query);
ResultSet rs=qe.execSelect();
while (rs.hasNext()){
QuerySolution s=rs.nextSolution();
Resource r=s.getResource("?person");
Literal name=s.getLiteral("?name");
System.out.println(s.getResource("?person").toString());
System.out.println(s.getLiteral("?name").getString());
}
03/28/11
Example query: people who were born in Berlin
before 1900
PREFIX dbo: http://dbpedia.org/ontology/
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX : http://dbpedia.org/resource/
SELECT ?name ?birth ?death ?person WHERE {
?person dbo:birthPlace :Berlin .
?person dbo:birthDate ?birth .
?person foaf:name ?name .
?person dbo:deathDate ?death .
FILTER (?birth < "1900-01-01"^^xsd:date) .
}
ORDER BY ?name
Other APIs
PHP:	
  RAP	
  –	
  RDF	
  
h+p://www.seasr.org/wp-­‐content/plugins/meandre/rdfapi-­‐php/doc/	
  
	
  

Python:	
  RDFLib	
  
h+p://www.rdflib.net/	
  
	
  

C:	
  Redland	
  
h+p://librdf.org/	
  
Installing PHP
Mac OS:
https://netbeans.org/kb/docs/php/configure-phpenvironment-mac-os.html
Windows:
https://netbeans.org/kb/docs/php/configure-phpenvironment-windows.html
Create new PHP project
Install RAP
•  Download at:
http://wifo5-03.informatik.uni-mannheim.de/bizer/rdfapi/
•  Unpack the zip file.
•  Include RDF API into your scripts:
•  define("RDFAPI_INCLUDE_DIR", "C:/Apache/htdocs/rdf_api/
api/");
•  include(RDFAPI_INCLUDE_DIR . "RDFAPI.php");

•  Change the constant RDFAPI_INCLUDE_DIR to the
directory in which you have unpacked the zip file.
PHP RAP: example
k.verbert@tue.nl
n.v.stash@tue.nl
g.l.fletcher@tue.nl

03/28/11
Sources
•  Konstantinos Tzonas. The Jena RDF Framework.

Contenu connexe

Tendances

HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2
HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2
HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2José Francisco Gómez Díez
 
Ch07 使用 JSTL
Ch07 使用 JSTLCh07 使用 JSTL
Ch07 使用 JSTLJustin Lin
 
Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -
Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -
Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -Dongbum Kim
 
Introduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and TerminologyIntroduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and TerminologySteven Miller
 
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
 
Laravel presentation
Laravel presentationLaravel presentation
Laravel presentationToufiq Mahmud
 
Laravel introduction
Laravel introductionLaravel introduction
Laravel introductionSimon Funk
 
SPARQL 사용법
SPARQL 사용법SPARQL 사용법
SPARQL 사용법홍수 허
 
CH1. 簡介 Web 應用程式
CH1. 簡介 Web 應用程式CH1. 簡介 Web 應用程式
CH1. 簡介 Web 應用程式Justin Lin
 
SPARQL introduction and training (130+ slides with exercices)
SPARQL introduction and training (130+ slides with exercices)SPARQL introduction and training (130+ slides with exercices)
SPARQL introduction and training (130+ slides with exercices)Thomas Francart
 
Introduction to laravel framework
Introduction to laravel frameworkIntroduction to laravel framework
Introduction to laravel frameworkAhmad Fatoni
 
Triplewave: a step towards RDF Stream Processing on the Web
Triplewave: a step towards RDF Stream Processing on the WebTriplewave: a step towards RDF Stream Processing on the Web
Triplewave: a step towards RDF Stream Processing on the WebDaniele Dell'Aglio
 
Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...
Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...
Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...Jean-Michel Franco
 
Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式
Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式
Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式Justin Lin
 

Tendances (20)

HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2
HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2
HOWTO - Upgrade de la Infraestuctura Grid de Oracle11gR2 a Oracle12cR2
 
Ch07 使用 JSTL
Ch07 使用 JSTLCh07 使用 JSTL
Ch07 使用 JSTL
 
Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -
Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -
Linked Data의 RDF 어휘 이해하고 체험하기 - FOAF, SIOC, SKOS를 중심으로 -
 
Introduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and TerminologyIntroduction to Ontology Concepts and Terminology
Introduction to Ontology Concepts and Terminology
 
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...
 
C# web api
C# web apiC# web api
C# web api
 
Laravel presentation
Laravel presentationLaravel presentation
Laravel presentation
 
Laravel introduction
Laravel introductionLaravel introduction
Laravel introduction
 
Spring framework
Spring frameworkSpring framework
Spring framework
 
DBpedia InsideOut
DBpedia InsideOutDBpedia InsideOut
DBpedia InsideOut
 
Laravel overview
Laravel overviewLaravel overview
Laravel overview
 
SPARQL 사용법
SPARQL 사용법SPARQL 사용법
SPARQL 사용법
 
CH1. 簡介 Web 應用程式
CH1. 簡介 Web 應用程式CH1. 簡介 Web 應用程式
CH1. 簡介 Web 應用程式
 
SPARQL introduction and training (130+ slides with exercices)
SPARQL introduction and training (130+ slides with exercices)SPARQL introduction and training (130+ slides with exercices)
SPARQL introduction and training (130+ slides with exercices)
 
Introduction to laravel framework
Introduction to laravel frameworkIntroduction to laravel framework
Introduction to laravel framework
 
Triplewave: a step towards RDF Stream Processing on the Web
Triplewave: a step towards RDF Stream Processing on the WebTriplewave: a step towards RDF Stream Processing on the Web
Triplewave: a step towards RDF Stream Processing on the Web
 
laravel.pptx
laravel.pptxlaravel.pptx
laravel.pptx
 
Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...
Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...
Réussir le projet MDM depuis son business case jusqu'à son exploitation au q...
 
RDF and OWL
RDF and OWLRDF and OWL
RDF and OWL
 
Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式
Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式
Servlet & JSP 教學手冊第二版 - 第 1 章:簡介Web應用程式
 

Similaire à Querying DBpedia with Jena and SPARQL

070517 Jena
070517 Jena070517 Jena
070517 Jenayuhana
 
03 form-data
03 form-data03 form-data
03 form-datasnopteck
 
SWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQLSWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQLMariano Rodriguez-Muro
 
Developing RESTful WebServices using Jersey
Developing RESTful WebServices using JerseyDeveloping RESTful WebServices using Jersey
Developing RESTful WebServices using Jerseyb_kathir
 
Rapid API Development ArangoDB Foxx
Rapid API Development ArangoDB FoxxRapid API Development ArangoDB Foxx
Rapid API Development ArangoDB FoxxMichael Hackstein
 
The Semantic Web #10 - SPARQL
The Semantic Web #10 - SPARQLThe Semantic Web #10 - SPARQL
The Semantic Web #10 - SPARQLMyungjin Lee
 
Tutorial on developing a Solr search component plugin
Tutorial on developing a Solr search component pluginTutorial on developing a Solr search component plugin
Tutorial on developing a Solr search component pluginsearchbox-com
 
Examiness hints and tips from the trenches
Examiness hints and tips from the trenchesExaminess hints and tips from the trenches
Examiness hints and tips from the trenchesIsmail Mayat
 
Querying Linked Data with SPARQL (2010)
Querying Linked Data with SPARQL (2010)Querying Linked Data with SPARQL (2010)
Querying Linked Data with SPARQL (2010)Olaf Hartig
 
Laurens Van Den Oever Xopus Presentation
Laurens Van Den Oever Xopus PresentationLaurens Van Den Oever Xopus Presentation
Laurens Van Den Oever Xopus PresentationAjax Experience 2009
 
More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)
More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)
More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)Jen Wong
 
nodejs_at_a_glance.ppt
nodejs_at_a_glance.pptnodejs_at_a_glance.ppt
nodejs_at_a_glance.pptWalaSidhom1
 
Scala45 spray test
Scala45 spray testScala45 spray test
Scala45 spray testkopiczko
 
Web data from R
Web data from RWeb data from R
Web data from Rschamber
 

Similaire à Querying DBpedia with Jena and SPARQL (20)

070517 Jena
070517 Jena070517 Jena
070517 Jena
 
03 form-data
03 form-data03 form-data
03 form-data
 
SPARQLing cocktails
SPARQLing cocktailsSPARQLing cocktails
SPARQLing cocktails
 
4 sw architectures and sparql
4 sw architectures and sparql4 sw architectures and sparql
4 sw architectures and sparql
 
SWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQLSWT Lecture Session 4 - SW architectures and SPARQL
SWT Lecture Session 4 - SW architectures and SPARQL
 
Developing RESTful WebServices using Jersey
Developing RESTful WebServices using JerseyDeveloping RESTful WebServices using Jersey
Developing RESTful WebServices using Jersey
 
Data shapes-test-suite
Data shapes-test-suiteData shapes-test-suite
Data shapes-test-suite
 
Rapid API Development ArangoDB Foxx
Rapid API Development ArangoDB FoxxRapid API Development ArangoDB Foxx
Rapid API Development ArangoDB Foxx
 
The Semantic Web #10 - SPARQL
The Semantic Web #10 - SPARQLThe Semantic Web #10 - SPARQL
The Semantic Web #10 - SPARQL
 
Tutorial on developing a Solr search component plugin
Tutorial on developing a Solr search component pluginTutorial on developing a Solr search component plugin
Tutorial on developing a Solr search component plugin
 
SFScon 2020 - Peter Hopfgartner - Open Data de luxe
SFScon 2020 - Peter Hopfgartner - Open Data de luxeSFScon 2020 - Peter Hopfgartner - Open Data de luxe
SFScon 2020 - Peter Hopfgartner - Open Data de luxe
 
Examiness hints and tips from the trenches
Examiness hints and tips from the trenchesExaminess hints and tips from the trenches
Examiness hints and tips from the trenches
 
Querying Linked Data with SPARQL (2010)
Querying Linked Data with SPARQL (2010)Querying Linked Data with SPARQL (2010)
Querying Linked Data with SPARQL (2010)
 
Laurens Van Den Oever Xopus Presentation
Laurens Van Den Oever Xopus PresentationLaurens Van Den Oever Xopus Presentation
Laurens Van Den Oever Xopus Presentation
 
More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)
More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)
More on Fitnesse and Continuous Integration (Silicon Valley code camp 2012)
 
nodejs_at_a_glance.ppt
nodejs_at_a_glance.pptnodejs_at_a_glance.ppt
nodejs_at_a_glance.ppt
 
Scala45 spray test
Scala45 spray testScala45 spray test
Scala45 spray test
 
Web data from R
Web data from RWeb data from R
Web data from R
 
How to use soap component
How to use soap componentHow to use soap component
How to use soap component
 
Practical OData
Practical ODataPractical OData
Practical OData
 

Plus de Katrien Verbert

Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?Katrien Verbert
 
Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?Katrien Verbert
 
Human-centered AI: how can we support lay users to understand AI?
Human-centered AI: how can we support lay users to understand AI?Human-centered AI: how can we support lay users to understand AI?
Human-centered AI: how can we support lay users to understand AI?Katrien Verbert
 
Explaining job recommendations: a human-centred perspective
Explaining job recommendations: a human-centred perspectiveExplaining job recommendations: a human-centred perspective
Explaining job recommendations: a human-centred perspectiveKatrien Verbert
 
Explaining recommendations: design implications and lessons learned
Explaining recommendations: design implications and lessons learnedExplaining recommendations: design implications and lessons learned
Explaining recommendations: design implications and lessons learnedKatrien Verbert
 
Designing Learning Analytics Dashboards: Lessons Learned
Designing Learning Analytics Dashboards: Lessons LearnedDesigning Learning Analytics Dashboards: Lessons Learned
Designing Learning Analytics Dashboards: Lessons LearnedKatrien Verbert
 
Human-centered AI: towards the next generation of interactive and adaptive ex...
Human-centered AI: towards the next generation of interactive and adaptive ex...Human-centered AI: towards the next generation of interactive and adaptive ex...
Human-centered AI: towards the next generation of interactive and adaptive ex...Katrien Verbert
 
Explainable AI for non-expert users
Explainable AI for non-expert usersExplainable AI for non-expert users
Explainable AI for non-expert usersKatrien Verbert
 
Towards the next generation of interactive and adaptive explanation methods
Towards the next generation of interactive and adaptive explanation methodsTowards the next generation of interactive and adaptive explanation methods
Towards the next generation of interactive and adaptive explanation methodsKatrien Verbert
 
Personalized food recommendations: combining recommendation, visualization an...
Personalized food recommendations: combining recommendation, visualization an...Personalized food recommendations: combining recommendation, visualization an...
Personalized food recommendations: combining recommendation, visualization an...Katrien Verbert
 
Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...
Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...
Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...Katrien Verbert
 
Learning analytics for feedback at scale
Learning analytics for feedback at scaleLearning analytics for feedback at scale
Learning analytics for feedback at scaleKatrien Verbert
 
Interactive recommender systems and dashboards for learning
Interactive recommender systems and dashboards for learningInteractive recommender systems and dashboards for learning
Interactive recommender systems and dashboards for learningKatrien Verbert
 
Interactive recommender systems: opening up the “black box”
Interactive recommender systems: opening up the “black box”Interactive recommender systems: opening up the “black box”
Interactive recommender systems: opening up the “black box”Katrien Verbert
 
Interactive Recommender Systems
Interactive Recommender SystemsInteractive Recommender Systems
Interactive Recommender SystemsKatrien Verbert
 
Web Information Systems Lecture 2: HTML
Web Information Systems Lecture 2: HTMLWeb Information Systems Lecture 2: HTML
Web Information Systems Lecture 2: HTMLKatrien Verbert
 
Information Visualisation: perception and principles
Information Visualisation: perception and principlesInformation Visualisation: perception and principles
Information Visualisation: perception and principlesKatrien Verbert
 
Web Information Systems Lecture 1: Introduction
Web Information Systems Lecture 1: IntroductionWeb Information Systems Lecture 1: Introduction
Web Information Systems Lecture 1: IntroductionKatrien Verbert
 
Information Visualisation: Introduction
Information Visualisation: IntroductionInformation Visualisation: Introduction
Information Visualisation: IntroductionKatrien Verbert
 

Plus de Katrien Verbert (20)

Explainability methods
Explainability methodsExplainability methods
Explainability methods
 
Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?
 
Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?Human-centered AI: how can we support end-users to interact with AI?
Human-centered AI: how can we support end-users to interact with AI?
 
Human-centered AI: how can we support lay users to understand AI?
Human-centered AI: how can we support lay users to understand AI?Human-centered AI: how can we support lay users to understand AI?
Human-centered AI: how can we support lay users to understand AI?
 
Explaining job recommendations: a human-centred perspective
Explaining job recommendations: a human-centred perspectiveExplaining job recommendations: a human-centred perspective
Explaining job recommendations: a human-centred perspective
 
Explaining recommendations: design implications and lessons learned
Explaining recommendations: design implications and lessons learnedExplaining recommendations: design implications and lessons learned
Explaining recommendations: design implications and lessons learned
 
Designing Learning Analytics Dashboards: Lessons Learned
Designing Learning Analytics Dashboards: Lessons LearnedDesigning Learning Analytics Dashboards: Lessons Learned
Designing Learning Analytics Dashboards: Lessons Learned
 
Human-centered AI: towards the next generation of interactive and adaptive ex...
Human-centered AI: towards the next generation of interactive and adaptive ex...Human-centered AI: towards the next generation of interactive and adaptive ex...
Human-centered AI: towards the next generation of interactive and adaptive ex...
 
Explainable AI for non-expert users
Explainable AI for non-expert usersExplainable AI for non-expert users
Explainable AI for non-expert users
 
Towards the next generation of interactive and adaptive explanation methods
Towards the next generation of interactive and adaptive explanation methodsTowards the next generation of interactive and adaptive explanation methods
Towards the next generation of interactive and adaptive explanation methods
 
Personalized food recommendations: combining recommendation, visualization an...
Personalized food recommendations: combining recommendation, visualization an...Personalized food recommendations: combining recommendation, visualization an...
Personalized food recommendations: combining recommendation, visualization an...
 
Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...
Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...
Explaining and Exploring Job Recommendations: a User-driven Approach for Inte...
 
Learning analytics for feedback at scale
Learning analytics for feedback at scaleLearning analytics for feedback at scale
Learning analytics for feedback at scale
 
Interactive recommender systems and dashboards for learning
Interactive recommender systems and dashboards for learningInteractive recommender systems and dashboards for learning
Interactive recommender systems and dashboards for learning
 
Interactive recommender systems: opening up the “black box”
Interactive recommender systems: opening up the “black box”Interactive recommender systems: opening up the “black box”
Interactive recommender systems: opening up the “black box”
 
Interactive Recommender Systems
Interactive Recommender SystemsInteractive Recommender Systems
Interactive Recommender Systems
 
Web Information Systems Lecture 2: HTML
Web Information Systems Lecture 2: HTMLWeb Information Systems Lecture 2: HTML
Web Information Systems Lecture 2: HTML
 
Information Visualisation: perception and principles
Information Visualisation: perception and principlesInformation Visualisation: perception and principles
Information Visualisation: perception and principles
 
Web Information Systems Lecture 1: Introduction
Web Information Systems Lecture 1: IntroductionWeb Information Systems Lecture 1: Introduction
Web Information Systems Lecture 1: Introduction
 
Information Visualisation: Introduction
Information Visualisation: IntroductionInformation Visualisation: Introduction
Information Visualisation: Introduction
 

Dernier

Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3JemimahLaneBuaron
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeThiyagu K
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityGeoBlogs
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactPECB
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Sapana Sha
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactdawncurless
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104misteraugie
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingTechSoup
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13Steve Thomason
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsTechSoup
 
social pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajansocial pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajanpragatimahajan3
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)eniolaolutunde
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introductionMaksud Ahmed
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformChameera Dedduwage
 
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesSeparation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesFatimaKhan178732
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfchloefrazer622
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxiammrhaywood
 

Dernier (20)

Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3Q4-W6-Restating Informational Text Grade 3
Q4-W6-Restating Informational Text Grade 3
 
Measures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and ModeMeasures of Central Tendency: Mean, Median and Mode
Measures of Central Tendency: Mean, Median and Mode
 
Paris 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activityParis 2024 Olympic Geographies - an activity
Paris 2024 Olympic Geographies - an activity
 
Beyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global ImpactBeyond the EU: DORA and NIS 2 Directive's Global Impact
Beyond the EU: DORA and NIS 2 Directive's Global Impact
 
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111Call Girls in Dwarka Mor Delhi Contact Us 9654467111
Call Girls in Dwarka Mor Delhi Contact Us 9654467111
 
Accessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impactAccessible design: Minimum effort, maximum impact
Accessible design: Minimum effort, maximum impact
 
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptxINDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
INDIA QUIZ 2024 RLAC DELHI UNIVERSITY.pptx
 
Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104Nutritional Needs Presentation - HLTH 104
Nutritional Needs Presentation - HLTH 104
 
Advance Mobile Application Development class 07
Advance Mobile Application Development class 07Advance Mobile Application Development class 07
Advance Mobile Application Development class 07
 
Grant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy ConsultingGrant Readiness 101 TechSoup and Remy Consulting
Grant Readiness 101 TechSoup and Remy Consulting
 
The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13The Most Excellent Way | 1 Corinthians 13
The Most Excellent Way | 1 Corinthians 13
 
Introduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The BasicsIntroduction to Nonprofit Accounting: The Basics
Introduction to Nonprofit Accounting: The Basics
 
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
Mattingly "AI & Prompt Design: Structured Data, Assistants, & RAG"
 
social pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajansocial pharmacy d-pharm 1st year by Pragati K. Mahajan
social pharmacy d-pharm 1st year by Pragati K. Mahajan
 
Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)Software Engineering Methodologies (overview)
Software Engineering Methodologies (overview)
 
microwave assisted reaction. General introduction
microwave assisted reaction. General introductionmicrowave assisted reaction. General introduction
microwave assisted reaction. General introduction
 
A Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy ReformA Critique of the Proposed National Education Policy Reform
A Critique of the Proposed National Education Policy Reform
 
Separation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and ActinidesSeparation of Lanthanides/ Lanthanides and Actinides
Separation of Lanthanides/ Lanthanides and Actinides
 
Arihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdfArihant handbook biology for class 11 .pdf
Arihant handbook biology for class 11 .pdf
 
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptxSOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
SOCIAL AND HISTORICAL CONTEXT - LFTVD.pptx
 

Querying DBpedia with Jena and SPARQL

  • 1. Tutorial: Querying DBpedia Web Technology 2ID60 14 November 2013 Dr. Katrien Verbert Dr. ir. Natasha Stash Dr. George Fletcher
  • 2. Overview •  •  •  •  •  Introduction to Jena Setting up the environment Querying DBpedia Other APIs PHP example
  • 3. Jena •  Jena is a Java framework for the creation of applications for the Semantic Web •  Provides interfaces and classes for the creation and manipulation of RDF repositories
  • 5. Capabilities of Jena •  •  •  •  RDF API Reading and writing in RDF/XML, N-Triples In-memory and persistent storage SPARQL query engine
  • 6. RDF concepts •  The Jena RDF API contains classes and interfaces for every important aspect of the RDF specification •  They can be used in order to construct RDF graphs from scratch, or edit existent graphs •  These classes/interfaces reside in the com.hp.hpl.jena.rdf.model package •  In Jena, the Model interface is used to represent RDF graphs •  Through Model, statements can be obtained/ created/ removed etc
  • 7. RDF concepts // Create an empty model Model model = ModelFactory.createDefaultModel(); String ns = new String("http://www.example.com/example#"); // Create two Resources Resource john = model.createResource(ns + "John"); Resource jane = model.createResource(ns + "Jane"); // Create the 'hasBrother' Property declaration Property hasBrother = model.createProperty(ns, "hasBrother"); // Associate jane to john through 'hasBrother' jane.addProperty(hasBrother, john); // Create the 'hasSister' Property declaration Property hasSister = model.createProperty(ns, "hasSister"); // Associate john and jane through 'hasSister' with a Statement Statement sisterStmt = model.createStatement(john, hasSister, jane); model.add(sisterStmt);
  • 8. SPARQL query processing •  Jena uses the ARQ engine for the processing of SPARQL queries •  The ARQ API classes are found in com.hp.hpl.jena.query •  Basic classes in ARQ: •  Query: Represents a single SPARQL query. •  Dataset: The knowledge base on which queries are executed (Equivalent to RDF Models) •  QueryFactory: Can be used to generate Query objects from SPARQL strings •  QueryExecution: Provides methods for the execution of queries •  ResultSet: Contains the results obtained from an executed query •  QuerySolution: Represents a row of query results. •  If there are many answers to a query, a ResultSet is returned after the query is executed. The ResultSet contains many QuerySolutions
  • 9. SPARQL query processing // Prepare query string String queryString = "PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>n" + "PREFIX : <http://www.example.com/onto1#>n" + "SELECT ?married ?spouse WHERE {" + "?married rdf:type :MarriedPerson.n" + "?married :hasSpouse ?spouse." + "}"; // Use the ontology model to create a Dataset object // Note: If no reasoner has been attached to the model, no results // will be returned (MarriedPerson has no asserted instances) Dataset dataset = DatasetFactory.create(ontModel); // Parse query string and create Query object Query q = QueryFactory.create(queryString); // Execute query and obtain result set QueryExecution qexec = QueryExecutionFactory.create(q, dataset); ResultSet resultSet = qexec.execSelect();
  • 10. SPARQL query processing // Print results while(resultSet.hasNext()) { // Each row contains two fields: ‘married’ and ‘spouse’, // as defined in the query string QuerySolution row = (QuerySolution)resultSet.next(); RDFNode nextMarried = row.get("married"); System.out.print(nextMarried.toString()); System.out.print(" is married to "); RDFNode nextSpouse = row.get("spouse"); System.out.println(nextSpouse.toString()); }
  • 12. Overview •  •  •  •  Introduction to Jena Setting up the environment Querying Dbpedia Other APIs
  • 13. Setting up the environment Download Netbeans Java EE version: https://netbeans.org/downloads/
  • 16. Getting started with Jena in Netbeans Create a new Java project
  • 17. Create a Java project
  • 18. Add Jena libraries to class path
  • 19. Add Jena libraries to class path
  • 20. Add all jars in lib folder of Jena distribution
  • 21. Add all jars in lib folder
  • 22. Using Jena with Eclipse •  http://www.iandickinson.me.uk/articles/jena-eclipsehelloworld/
  • 24. Overview •  •  •  •  Introduction to Jena Setting up the environment Querying Dbpedia Other APIs
  • 25. QueryFactory •  has various create() methods to read a textual query •  these create() methods •  return a Query object, •  which encapsulates a parsed query.
  • 26. QueryExecutionFactory Create a QueryExecution that will access a SPARQL service over HTTP QueryExecutionFactory.sparqlService(String service, Query query)
  • 28. Example String service = "http://dbpedia.org/sparql"; String query = "ASK { }"; QueryExecution qe = QueryExecutionFactory.sparqlService(service, query);
  • 29. Test connection import com.hp.hpl.jena.query.QueryExecution; import com.hp.hpl.jena.query.QueryExecutionFactory; import com.hp.hpl.jena.sparql.engine.http.QueryExceptionHTTP; public class QueryTest { public static void main(String[] args) { String service = "http://dbpedia.org/sparql"; String query = "ASK { }"; QueryExecution qe = QueryExecutionFactory.sparqlService(service, query); try { if (qe.execAsk()) { System.out.println(service + " is UP"); } // end if } catch (QueryExceptionHTTP e) { System.out.println(service + " is DOWN"); } finally { qe.close(); } } }
  • 31. Example query: people who were born in Eindhoven String service="http://dbpedia.org/sparql"; String query="PREFIX dbo:<http://dbpedia.org/ontology/>" + "PREFIX : <http://dbpedia.org/resource/>" + "select ?person where {?person dbo:birthPlace :Eindhoven.}"; QueryExecution qe=QueryExecutionFactory.sparqlService(service, query); ResultSet rs=qe.execSelect(); while (rs.hasNext()){ QuerySolution s=rs.nextSolution(); System.out.println(s.getResource("?person").toString()); } 03/28/11
  • 32. Processing results QuerySolution soln = results.nextSolution() ; RDFNode x = soln.get("varName") ; // Get a result variable by name. Resource r = soln.getResource("VarR") ; // Get a result variable - must be a resource Literal l = soln.getLiteral("VarL") ; // Get a result variable - must be a literal
  • 33. Example String service="http://dbpedia.org/sparql"; String query="PREFIX dbo:<http://dbpedia.org/ontology/>" + "PREFIX : <http://dbpedia.org/resource/>" + "PREFIX foaf:<http://xmlns.com/foaf/0.1/>" + "select ?person ?name where {?person dbo:birthPlace :Eindhoven." + "?person foaf:name ?name}"; QueryExecution qe=QueryExecutionFactory.sparqlService(service, query); ResultSet rs=qe.execSelect(); while (rs.hasNext()){ QuerySolution s=rs.nextSolution(); Resource r=s.getResource("?person"); Literal name=s.getLiteral("?name"); System.out.println(s.getResource("?person").toString()); System.out.println(s.getLiteral("?name").getString()); } 03/28/11
  • 34. Example query: people who were born in Berlin before 1900 PREFIX dbo: http://dbpedia.org/ontology/ PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX foaf: <http://xmlns.com/foaf/0.1/> PREFIX : http://dbpedia.org/resource/ SELECT ?name ?birth ?death ?person WHERE { ?person dbo:birthPlace :Berlin . ?person dbo:birthDate ?birth . ?person foaf:name ?name . ?person dbo:deathDate ?death . FILTER (?birth < "1900-01-01"^^xsd:date) . } ORDER BY ?name
  • 35. Other APIs PHP:  RAP  –  RDF   h+p://www.seasr.org/wp-­‐content/plugins/meandre/rdfapi-­‐php/doc/     Python:  RDFLib   h+p://www.rdflib.net/     C:  Redland   h+p://librdf.org/  
  • 37. Create new PHP project
  • 38. Install RAP •  Download at: http://wifo5-03.informatik.uni-mannheim.de/bizer/rdfapi/ •  Unpack the zip file. •  Include RDF API into your scripts: •  define("RDFAPI_INCLUDE_DIR", "C:/Apache/htdocs/rdf_api/ api/"); •  include(RDFAPI_INCLUDE_DIR . "RDFAPI.php"); •  Change the constant RDFAPI_INCLUDE_DIR to the directory in which you have unpacked the zip file.
  • 41. Sources •  Konstantinos Tzonas. The Jena RDF Framework.