Lab: SPARQL: Difference between revisions

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SPARQL reference:
SPARQL reference:
* [https://www.w3.org/TR/sparql11-query/ SPARQL Query Documentation]
* [https://www.w3.org/TR/sparql11-query/ SPARQL Query Documentation]
<!--
* [http://www.w3.org/TR/sparql11-update/ SPARQL Update Documentation]
* [http://www.w3.org/TR/sparql11-update/ SPARQL Update Documentation]
-->
* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]
* [https://en.wikibooks.org/wiki/SPARQL/Expressions_and_Functions SPARQL Expressions and Functions]


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* For each investigation with multiple indictments, list the number of indictments made, sorted with the most indictments first.
* For each investigation with multiple indictments, list the number of indictments made, sorted with the most indictments first.
* For each president, list the numbers of convictions and of pardons made after conviction.
* For each president, list the numbers of convictions and of pardons made after conviction.
 
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'''Task:'''
'''Task:'''
Write the following SPARQL updates:
Write the following SPARQL updates:
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'''Task:'''
'''Task:'''
Load the RDF graph you created in exercises 1 and 2. (Maybe you want to create a new namespace in Blazegraph first.) Use INSERT DATA updates to add these triples to your graph:
Load the RDF graph you created in exercises 1 and 2. (Maybe you want to create a new namespace in GraphDB first.) Use INSERT DATA updates to add these triples to your graph:
* George Papadopoulos was adviser to the Trump campaign.
* George Papadopoulos was adviser to the Trump campaign.
** He pleaded guilty to lying to the FBI.
** He pleaded guilty to lying to the FBI.
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* Use a DESCRIBE query to show the updated information about Roger Stone.
* Use a DESCRIBE query to show the updated information about Roger Stone.
* Use a CONSTRUCT query to create a new RDF group with triples only about Roger Stone (in other words, having Roger Stone as the subject.)
* Use a CONSTRUCT query to create a new RDF group with triples only about Roger Stone (in other words, having Roger Stone as the subject.)
 
-->
==If you have more time==
==If you have more time==
<!--
'''Task:'''
'''Task:'''
In the ''russia_investigation_kg.ttl'' dataset, the ''muellerkg:name'' property used as predicate is already covered by a standard term from an estalished vocabulary in the LOD cloud: ''foaf:name'', where ''foaf:'' is ''http://xmlns.com/foaf/0.1/''.  
In the ''russia_investigation_kg.ttl'' dataset, the ''muellerkg:name'' property used as predicate is already covered by a standard term from an estalished vocabulary in the LOD cloud: ''foaf:name'', where ''foaf:'' is ''http://xmlns.com/foaf/0.1/''.  
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'''Task:''' Write an INSERT statement to add at least one significant date to the Mueller investigation, with literal type xsd:date. Write a DELETE/INSERT statement to change the date to a string, and a new DELETE/INSERT statement to change it back to xsd:date.
'''Task:''' Write an INSERT statement to add at least one significant date to the Mueller investigation, with literal type xsd:date. Write a DELETE/INSERT statement to change the date to a string, and a new DELETE/INSERT statement to change it back to xsd:date.
 
-->
'''Task:''' Try to program some of the queries/updates in a Python program (this will be the topic of later labs). You have two options:
'''Task:''' Try to program some of the queries in a Python program (this will be the topic of later labs). You have two options:


''Using rdflib:''
''Using rdflib:''
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''Using SPARQLwrapper:''
''Using SPARQLwrapper:''
You can use SPARQLwrapper (another Python API) to connect to your running Blazegraph endpoint. See the Python example page for how to do this.
You can use SPARQLwrapper (another Python API) to connect to your running GraphDB endpoint. See the Python example page for how to do this.


'''Task:''' If you want to explore more, try out the Wikidata Query Service (WDQS):
'''Task:''' If you want to explore more, try out the Wikidata Query Service (WDQS):

Revision as of 07:02, 5 February 2024

Topics

  • Setting up GraphDB
  • SPARQL queries and updates

Useful materials

GraphDB documentation:

SPARQL reference:

Tasks

Registering for GraphDB Free

To retrieve a download link for Ontotext's GraphDB Free tool, you first need to register. Here is the registration link (or search for "ontotext graphdb registration").

If you do not like registering for proprietary software, it is still possible to do most of the exercises using Blazegraph, which you can download here (requires Java). Blazegraph is a powerful open-source tool, but GraphDB offers even more functionality and is what the lab leaders will prepare for this semester.

Installing and running GraphDB

When you have received the download link in an email from the GraphDB Team, you can proceed to install and run GraphDB according to this Quick Start Guide. Follow the guide up to and including the section Run GraphDB as a Standalone Server¶.

Setting up a repository

Jump forward in the Quick Start Guide to the section Create a Repository. Create a new GraphDB Repository called, for example, info216_lab3_NN, where NN are your initials. Choose No inference for now. Otherwise, the default parameters are fine.

Connect to the new repository and pin it as your default repository.

Load data

Download the Turtle file File:Russia investigation kg.txt, and save it with the correct extension, as russia_investigation_kg.ttl (not .txt). (You can also experiment with the Turtle file you saved after exercises 1 and 2.) Load the Russia_investigation data through the GraphDB Workbench as described in the QuickStart guide.

You can use http://example.org/ as Base IRI.

Graph visualisation

Go to Explore -> Visual graph and create an Easy graph around the resource http://example.org#Roger_Stone. Double-click on nodes to expand them. Have there been more investigations involving Russia?

SPARQL tasks

Go to the SPARQL Query & Update tab.

Task: Using the data in russia_investigation_kg.ttl, write the following SPARQL SELECT queries. ( This page explains the Russian investigation KG a bit more.)

  • List all triples in your graph.
  • List the first 100 triples in your graph.
  • Count the number of triples in your graph.
  • Count the number of indictments in your graph.
  • List everyone who pleaded guilty, along with the name of the investigation.
  • List everyone who were convicted, but who had their conviction overturned by which president.
  • For each investigation, list the number of indictments made.
  • For each investigation with multiple indictments, list the number of indictments made.
  • For each investigation with multiple indictments, list the number of indictments made, sorted with the most indictments first.
  • For each president, list the numbers of convictions and of pardons made after conviction.

If you have more time

Task: Try to program some of the queries in a Python program (this will be the topic of later labs). You have two options:

Using rdflib: Read the Turtle file into an rdflib Graph and use the query() method. 

g = Graph()
g.parse(..., format='ttl')
r = g.query(...your_query_string...)

The hard part is picking the results out of the object r...

Using SPARQLwrapper: You can use SPARQLwrapper (another Python API) to connect to your running GraphDB endpoint. See the Python example page for how to do this.

Task: If you want to explore more, try out the Wikidata Query Service (WDQS):

WDQS tutorials:

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