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==Topics==
==Topics==
SPARQL programming in python with SPARQLWrapper and Blazegraph, or alternatively RDFlib.
SPARQL programming in Python:
These tasks are about programming SPARQL queries and inserts in a python program.
* with ''rdflib'': to manage an rdflib Graph internally in a program
* with ''SPARQLWrapper'' and ''GraphDB'': to manage an RDF graph stored externally in GraphDB (on your own local machine, but in principle it could be anywhere on the internet)


Last week we added triples manually from the web interface.  
''Motivation:'' Last week we entered SPARQL queries and updates manually from the web interface. But in the majority of cases we want to ''program'' the management of triples in our graphs, for example to handle automatic or scheduled updates.


However, sometimes we would rather want to program the insertion or updates of triples for our graphs/databases.  
''Important:'' There were quite a lot of SPARQL tasks in the last exercise. There are a lot of tasks in this exercise too, but the important thing is that you get to try the different types of SPARQL programming. How many SPARK queries and updates you do is a little up to you, but you must try at least one query and one update both using rdflib and SPARQLWrapper. And it is best if you try several different types of SPARQL queries too: both a SELECT, a CONSTRUCT or DESCRIBE, and an ASK.
 
==Useful materials==
*[https://github.com/RDFLib/sparqlwrapper SPARQLWrapper]
*[https://rdflib.readthedocs.io/en/stable/intro_to_sparql.html RDFlib - Querying with SPARQL]


==Tasks==
==Tasks==
'''Make a new blazegraph namespace and add all the triples that are on the bottom of the page'''
===SPARQL programming in Python with rdflib===
'''Redo all the SPARQL queries and updates from [https://wiki.uib.no/info216/index.php/Lab:_SPARQL Lab 4], this time writing a Python program.'''
'''Getting ready:'''
No additional installation is needed. You are already running Python and rdflib.
 
Parse the file ''russia_investigation_kg.ttl'' into an rdflib Graph. (The original file is available here: [[File:russia_investigation_kg.txt]]. Rename it from ''.txt'' to ''.ttl'').
 
'''Task:'''
Write the following queries and updates with Python and rdflib. See boilerplate examples below.
* Print out a list of all the predicates used in your graph.  
* Print out a sorted list of all the presidents represented in your graph.  
* Create dictionary (Python ''dict'') with all the represented presidents as keys. For each key, the value is a list of names of people indicted under that president.
* Use an ASK query to investigate whether Donald Trump has pardoned more than 5 people.
* Use a DESCRIBE query to create a new graph with information about Donald Trump. Print out the graph in Turtle format.
 
Note that different types of queries return objects with different contents. You can use core completion in your IDE or Python's ''dir()'' function to explore this further (for example ''dir(results)'').
* SELECT: returns an object you can iterate over (among other things) to get the table rows (the result object also contains table headers)
* ASK: returns an object that contains a single logical value (''True'' or ''False'')
* DESCRIBE and CONSTRUCT: return an rdflib Graph


* SELECT all triples in your graph.
'''Contents of the file 'spouses.ttl':'''
* SELECT all the interests of Cade.
<syntaxhighlight>
* SELECT the city and country of where Emma lives.
@prefix ex: <http://example.org/> .
* SELECT only people who are older than 26.
@prefix schema: <https://schema.org/> .
* SELECT Everyone who graduated with a Bachelor Degree.
* Use SPARQL Update's DELETE DATA to delete that fact that Cade is interested in Photography. Run your SPARQL query again to check that the graph has changed.


* Use INSERT DATA to add information about Sergio Pastor, who lives in 4 Carrer del Serpis, 46021 Valencia, Spain. he has a M.Sc. in computer from the University of Valencia from 2008. His areas of expertise include big data, semantic technologies and machine learning.
ex:Donald_Trump schema:spouse ( ex:IvanaTrump ex:MarlaMaples ex:MelaniaTrump ) .
</syntaxhighlight>


* Write a SPARQL DELETE/INSERT update to change the name of "University of Valencia" to "Universidad de Valencia" whereever it occurs.
'''Boilerplate code for rdflib query:'''
<syntaxhighlight lang="python">
from rdflib import Graph


* Write a SPARQL DESCRIBE query to get basic information about Sergio.
g = Graph()
g.parse("spouses.ttl", format='ttl')
result = g.query("""
    PREFIX ex: <http://example.org/>
    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
    PREFIX schema: <https://schema.org/>


* Write a SPARQL CONSTRUCT query that returns that: any city in an address is a cityOf the country of the same address.
    SELECT ?spouse WHERE {
        ex:Donald_Trump schema:spouse / rdf:rest* / rdf:first ?spouse .
    }""")
for row in result:
    print("Donald has spouse %s" % row)
</syntaxhighlight>


'''Boilerplate code for rdflib update'''
(using the KG4News graph again):
<syntaxhighlight lang="python">
from rdflib import Graph


==With Blazegraph==
update_str = """
The most important part is that we need to import a SPARQLWrapper in order to connect to the SPARQL endpoint of Blazegraph. The default namespace for blazegraph is "kb". The different namespaces for blazegraph acts as seperate graphs/databases.
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX kg: <http://i2s.uib.no/kg4news/>
PREFIX ss: <http://semanticscholar.org/>


If you want to add other namespaces you can do it from the web-interface if Blazegraph. This is especially useful if you are using the UiB link to blazegraph: "i2s.uib.no:8888/bigdata/#splash", because with your own namespace, only you can select and update your data.
INSERT DATA {   
   
    kg:paper_123 rdf:type ss:Paper ;
When it comes to how to do some queries and updates I recommend scrolling down on this page for help: https://github.com/RDFLib/sparqlwrapper. There are also some examples on our example page.
              ss:title "Semantic Knowledge Graphs for the News: A Review"@en ;
            kg:year 2022 ;
            dct:contributor kg:auth_456, kg:auth_789 .  
}"""
 
g = Graph()
g.update(update_str)
print(g.serialize(format='ttl'))  # format=’turtle’ also works
</syntaxhighlight>
 
===SPARQL programming in Python with SPARQLWrapper and GraphDB===
'''Getting ready:'''
You need a running ''and activated'' GraphDB repository as in [[Lab: SPARQL | Exercise 3: SPARQL]]. You will run GraphDB locally to keep things simple.
 
Install SPARQLWrapper (in your virtual environment):
  pip install SPARQLWrapper
Some older versions also require you to install ''requests'' API. The [https://github.com/RDFLib/sparqlwrapper SPARQLWrapper page on GitHub] contains more information.
 
Continue with the ''russia_investigation_kg.ttl'' example.
 
'''Task:'''
Program the following queries and updates with SPARQLWrapper and GraphDB.
* Ask whether there was an ongoing investigation on the date 1990-01-01.
* List ongoing investigations on that date 1990-01-01.
* Describe investigation number 100 (''muellerkg:investigation_100'').
* Print out a list of all the types used in your graph.
* Update the graph to that every resource that is an object in a ''muellerkg:investigation'' triple has the ''rdf:type'' ''muellerkg:Investigation''.
* Update the graph to that every resource that is an object in a ''muellerkg:person'' triple has the ''rdf:type'' ''muellerkg:IndictedPerson''.
* Update the graph so all the investigation nodes (such as ''muellerkg:watergate'') become the subject in a ''dc:title'' triple with the corresponding string (''watergate'') as the literal.
* Print out a sorted list of all the indicted persons represented in your graph.
* Print out the minimum, average and maximum indictment days for all the indictments in the graph.
* Print out the minimum, average and maximum indictment days for all the indictments in the graph per investigation.


Remember, before you can program with Blazegraph you have to make sure its running like we did in [https://wiki.uib.no/info216/index.php/Lab:_SPARQL Lab 4]. Make sure that the URL you use with SPARQLWrapper has the same address and port as the one you get from running it.
Note that different types of queries return different data formats with different structures:
Now you will be able to program queries and updates.
* SELECT and ASK: return a SPARQL Results Document in either XML, JSON, or CSV/TSV format.
* DESCRIBE and CONSTRUCT: return an RDF graph serialised in TURTLE or RDF/XML syntax, for example.
* Use a DESCRIBE query to create an rdflib Graph about Oliver Stone. Print the graph out in Turtle format.


<syntaxhighlight>
'''Boilerplate code for SPARQLWrapper query:'''
# How to establish connection to Blazegraph endpoint. Also a quick select example.
<syntaxhighlight lang="python">
from SPARQLWrapper import SPARQLWrapper


from SPARQLWrapper import SPARQLWrapper, JSON, POST, DIGEST
SERVER = 'http://localhost:7200'  # you may want to change this
REPOSITORY = 'lab04'              # you most likely want to change this


namespace = "kb"
endpoint = f'{SERVER}/repositories/{REPOSITORY}'  # standard path for GraphDB queries
sparql = SPARQLWrapper("http://localhost:19999/blazegraph/namespace/"+ namespace + "/sparql")


sparql.setQuery("""
query = """
     PREFIX ex: <http://example.org/>
     PREFIX ex: <http://example.org/>
     SELECT * WHERE {
     PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
    ex:Cade ex:interest ?interest.
     PREFIX schema: <https://schema.org/>
     }
""")
sparql.setReturnFormat(JSON)


results = sparql.query().convert()
    SELECT ?spouse WHERE {
ex:Donald_Trump schema:spouse / rdf:rest* / rdf:first ?spouse .
    }"""
   
client = SPARQLWrapper(endpoint)
client.setReturnFormat('json')
client.setQuery(query)


print('Spouses:')
results = client.queryAndConvert()
for result in results["results"]["bindings"]:
for result in results["results"]["bindings"]:
     print(result["interest"]["value"])
     print(result["spouse"]["value"])
</syntaxhighlight>


'''Boilerplate code for SPARQLWrapper update:'''
<syntaxhighlight lang="python">
from SPARQLWrapper import SPARQLWrapper
SERVER = 'http://localhost:7200'  # you may want to change this
REPOSITORY = 'lab04'              # you most likely want to change this
endpoint = f'{SERVER}/repositories/{REPOSITORY}/statements'  # standard path for GraphDB updates
update_str = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX kg: <http://i2s.uib.no/kg4news/>
PREFIX ss: <http://semanticscholar.org/>
INSERT DATA {   
    kg:paper_123 rdf:type ss:Paper ;
              ss:title "Semantic Knowledge Graphs for the News: A Review"@en ;
            kg:year 2023 ;
            dct:contributor kg:auth_654, kg:auth_789 .
}"""
client = SPARQLWrapper(endpoint)
client.setMethod('POST')
client.setQuery(update_str)
res = client.queryAndConvert()
</syntaxhighlight>
</syntaxhighlight>


The different types of queries requires different return formats:  
==If you have more time==
* SELECT and ASK: a SPARQL Results Document in XML, JSON, or CSV/TSV format.
Continue with the ''russia_investigation_kg.ttl'' example. Use either rdflib or SPARQLWrapper as you prefer - or both :-)
* DESCRIBE and CONSTRUCT: an RDF graph serialized, for example, in the TURTLE or RDF/XML syntax, or an equivalent RDF graph serialization.
 
Remember to make sure that you can see the changes that take place after your inserts.
'''Task:''' Write a query that lists all the resources in your graph that have Wikidata prefixes (i.e., ''http://www.wikidata.org/entity/''). Use the result to generate a list of Wikidata entity identifiers (i.e., Q-codes like these ''['Q13', 'Q42', 'Q80']''.
 
'''Task:'''
Install the [https://pypi.org/project/Wikidata/ wikidata] API:
pip install wikidata
Check out the following code:
from wikidata.client import Client
client = Client()
q80 = client.get('Q80')
Use the API to extend your local graph, for example with ''descriptions'' of some of your resources.
 
'''Task:'''
The ''wikidata'' API is good for simple tasks, but SPARQL is must more powerful. To explore available Wikidata properties, you can go to the [http:query.wikidata.org web GUI] and try
DESCRIBE wd:Q80  # or Q7358961...
You want to use prefixes like these (predefined in Wikidata query interface):
PREFIX wd: <http://www.wikidata.org/entity/>        # for resources
PREFIX wdt: <http://www.wikidata.org/prop/direct/>  # for properties
Stay away from the ''p:'' and ''wds:'' prefixes for now.


'''Task:'''
Write an embedded query that extends your local graph further, for example with more resource types. Property ''P31'' in Wikidata corresponds to ''rdf:type'' in your local graph. ''Use LIMIT, and make sure the query runs in the web GUI before you embed it.''


'''Task:'''
For resources that are humans (entity ''Q5''), you can add further information, for example about ''party affiliation'' and about ''significant events'' the person has been involved in.


==Without Blazegraph==
'''Boilerplate for embedded Wikidata queries:'''
If you have not been able to run Blazegraph on your own computer yet, you can use the UiB blazegraph service: i2s.uib.no:8888/bigdata/#splash.
<syntaxhighlight lang="SPARQL">
Remember to create your own namespace like said above in the web-interface.  
PREFIX wd: <http://www.wikidata.org/entity/>        # for Wikidata resources
PREFIX wdt: <http://www.wikidata.org/prop/direct/>  # for Wikidata properties


Alternatively, you can
SELECT * WHERE {
instead program SPARQL queries directly with RDFlib.


For help, look at the link below:  
    # your local query heere, which binds the Wikidata identifier ?wdresource
    # ?wdresource must be a URI that starts with http://www.wikidata.org/entity/


[https://rdflib.readthedocs.io/en/4.2.0/intro_to_sparql.html Querying with Sparql]
    # test binding:
    BIND(wd:Q80 AS ?wdresource)


    SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
        # return the Wikidata types of ?wd resource
        SELECT * WHERE {
            ?wdresource wdt:P31 ?wdtype .
        }
        LIMIT 5  # always use limit in remote queries
    }


==Useful Readings==
    # possible to continue local query here
*[https://github.com/RDFLib/sparqlwrapper SPARQLWrapper]
}
*[https://rdflib.readthedocs.io/en/4.2.0/intro_to_sparql.html RDFlib - Querying with Sparql]
LIMIT 10
</syntaxhighlight>
 
'''Task:'''
You can also try to connect to the [http://info216.i2s.uib.no/ INFO216 Sandbox] (read/write) and [http://kg4news.newsangler.uib.no KG4News Server] (read-only).
 
Both SPARQL endpoints run [https://blazegraph.com/ Blazegraph], which uses "namespaces" instead of "repositories", so the URLs are a little different. In the web UI , the "NAMESPACES" tab lets you select an existing or create a new namespace to use.''
 
<syntaxhighlight>
SERVER = 'http://info216.i2s.uib.no/bigdata/'  # you may want to change this
NAMESPACE  = 'lab04'                          # you most likely want to change this
 
endpoint = f'{SERVER}namespace/{NAMESPACE}/sparql'  # standard path for Blazegraph queries (and updates)
</syntaxhighlight>

Latest revision as of 12:58, 19 February 2024

Topics

SPARQL programming in Python:

  • with rdflib: to manage an rdflib Graph internally in a program
  • with SPARQLWrapper and GraphDB: to manage an RDF graph stored externally in GraphDB (on your own local machine, but in principle it could be anywhere on the internet)

Motivation: Last week we entered SPARQL queries and updates manually from the web interface. But in the majority of cases we want to program the management of triples in our graphs, for example to handle automatic or scheduled updates.

Important: There were quite a lot of SPARQL tasks in the last exercise. There are a lot of tasks in this exercise too, but the important thing is that you get to try the different types of SPARQL programming. How many SPARK queries and updates you do is a little up to you, but you must try at least one query and one update both using rdflib and SPARQLWrapper. And it is best if you try several different types of SPARQL queries too: both a SELECT, a CONSTRUCT or DESCRIBE, and an ASK.

Useful materials

Tasks

SPARQL programming in Python with rdflib

Getting ready: No additional installation is needed. You are already running Python and rdflib.

Parse the file russia_investigation_kg.ttl into an rdflib Graph. (The original file is available here: File:Russia investigation kg.txt. Rename it from .txt to .ttl).

Task: Write the following queries and updates with Python and rdflib. See boilerplate examples below.

  • Print out a list of all the predicates used in your graph.
  • Print out a sorted list of all the presidents represented in your graph.
  • Create dictionary (Python dict) with all the represented presidents as keys. For each key, the value is a list of names of people indicted under that president.
  • Use an ASK query to investigate whether Donald Trump has pardoned more than 5 people.
  • Use a DESCRIBE query to create a new graph with information about Donald Trump. Print out the graph in Turtle format.

Note that different types of queries return objects with different contents. You can use core completion in your IDE or Python's dir() function to explore this further (for example dir(results)).

  • SELECT: returns an object you can iterate over (among other things) to get the table rows (the result object also contains table headers)
  • ASK: returns an object that contains a single logical value (True or False)
  • DESCRIBE and CONSTRUCT: return an rdflib Graph

Contents of the file 'spouses.ttl':

@prefix ex: <http://example.org/> .
@prefix schema: <https://schema.org/> .

ex:Donald_Trump schema:spouse ( ex:IvanaTrump ex:MarlaMaples ex:MelaniaTrump ) .

Boilerplate code for rdflib query:

from rdflib import Graph

g = Graph()
g.parse("spouses.ttl", format='ttl')
result = g.query("""
    PREFIX ex: <http://example.org/>
    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
    PREFIX schema: <https://schema.org/>

    SELECT ?spouse WHERE {
        ex:Donald_Trump schema:spouse / rdf:rest* / rdf:first ?spouse .
    }""")
for row in result:
    print("Donald has spouse %s" % row)

Boilerplate code for rdflib update (using the KG4News graph again):

from rdflib import Graph

update_str = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX kg: <http://i2s.uib.no/kg4news/>
PREFIX ss: <http://semanticscholar.org/>

INSERT DATA {    
    kg:paper_123 rdf:type ss:Paper ;
               ss:title "Semantic Knowledge Graphs for the News: A Review"@en ;
            kg:year 2022 ;
            dct:contributor kg:auth_456, kg:auth_789 . 
}"""

g = Graph()
g.update(update_str)
print(g.serialize(format='ttl'))  # format=’turtle’ also works

SPARQL programming in Python with SPARQLWrapper and GraphDB

Getting ready: You need a running and activated GraphDB repository as in Exercise 3: SPARQL. You will run GraphDB locally to keep things simple.

Install SPARQLWrapper (in your virtual environment):

pip install SPARQLWrapper

Some older versions also require you to install requests API. The SPARQLWrapper page on GitHub contains more information.

Continue with the russia_investigation_kg.ttl example.

Task: Program the following queries and updates with SPARQLWrapper and GraphDB.

  • Ask whether there was an ongoing investigation on the date 1990-01-01.
  • List ongoing investigations on that date 1990-01-01.
  • Describe investigation number 100 (muellerkg:investigation_100).
  • Print out a list of all the types used in your graph.
  • Update the graph to that every resource that is an object in a muellerkg:investigation triple has the rdf:type muellerkg:Investigation.
  • Update the graph to that every resource that is an object in a muellerkg:person triple has the rdf:type muellerkg:IndictedPerson.
  • Update the graph so all the investigation nodes (such as muellerkg:watergate) become the subject in a dc:title triple with the corresponding string (watergate) as the literal.
  • Print out a sorted list of all the indicted persons represented in your graph.
  • Print out the minimum, average and maximum indictment days for all the indictments in the graph.
  • Print out the minimum, average and maximum indictment days for all the indictments in the graph per investigation.

Note that different types of queries return different data formats with different structures:

  • SELECT and ASK: return a SPARQL Results Document in either XML, JSON, or CSV/TSV format.
  • DESCRIBE and CONSTRUCT: return an RDF graph serialised in TURTLE or RDF/XML syntax, for example.
  • Use a DESCRIBE query to create an rdflib Graph about Oliver Stone. Print the graph out in Turtle format.

Boilerplate code for SPARQLWrapper query:

from SPARQLWrapper import SPARQLWrapper

SERVER = 'http://localhost:7200'  # you may want to change this
REPOSITORY = 'lab04'               # you most likely want to change this

endpoint = f'{SERVER}/repositories/{REPOSITORY}'  # standard path for GraphDB queries

query = """
    PREFIX ex: <http://example.org/>
    PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
    PREFIX schema: <https://schema.org/>

    SELECT ?spouse WHERE {
 	 	ex:Donald_Trump schema:spouse / rdf:rest* / rdf:first ?spouse .
    }"""
    
client = SPARQLWrapper(endpoint)
client.setReturnFormat('json')
client.setQuery(query)

print('Spouses:')
results = client.queryAndConvert()
for result in results["results"]["bindings"]:
    print(result["spouse"]["value"])

Boilerplate code for SPARQLWrapper update:

from SPARQLWrapper import SPARQLWrapper

SERVER = 'http://localhost:7200'  # you may want to change this
REPOSITORY = 'lab04'               # you most likely want to change this

endpoint = f'{SERVER}/repositories/{REPOSITORY}/statements'  # standard path for GraphDB updates

update_str = """
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX dct: <http://purl.org/dc/terms/>
PREFIX kg: <http://i2s.uib.no/kg4news/>
PREFIX ss: <http://semanticscholar.org/>

INSERT DATA {    
    kg:paper_123 rdf:type ss:Paper ;
               ss:title "Semantic Knowledge Graphs for the News: A Review"@en ;
            kg:year 2023 ;
            dct:contributor kg:auth_654, kg:auth_789 . 
}"""

client = SPARQLWrapper(endpoint)
client.setMethod('POST')
client.setQuery(update_str)
res = client.queryAndConvert()

If you have more time

Continue with the russia_investigation_kg.ttl example. Use either rdflib or SPARQLWrapper as you prefer - or both :-)

Task: Write a query that lists all the resources in your graph that have Wikidata prefixes (i.e., http://www.wikidata.org/entity/). Use the result to generate a list of Wikidata entity identifiers (i.e., Q-codes like these ['Q13', 'Q42', 'Q80'].

Task: Install the wikidata API:

pip install wikidata

Check out the following code:

from wikidata.client import Client

client = Client()
q80 = client.get('Q80')

Use the API to extend your local graph, for example with descriptions of some of your resources.

Task: The wikidata API is good for simple tasks, but SPARQL is must more powerful. To explore available Wikidata properties, you can go to the [http:query.wikidata.org web GUI] and try

DESCRIBE wd:Q80  # or Q7358961...

You want to use prefixes like these (predefined in Wikidata query interface):

PREFIX wd: <http://www.wikidata.org/entity/>        # for resources
PREFIX wdt: <http://www.wikidata.org/prop/direct/>  # for properties

Stay away from the p: and wds: prefixes for now.

Task: Write an embedded query that extends your local graph further, for example with more resource types. Property P31 in Wikidata corresponds to rdf:type in your local graph. Use LIMIT, and make sure the query runs in the web GUI before you embed it.

Task: For resources that are humans (entity Q5), you can add further information, for example about party affiliation and about significant events the person has been involved in.

Boilerplate for embedded Wikidata queries:

PREFIX wd: <http://www.wikidata.org/entity/>        # for Wikidata resources
PREFIX wdt: <http://www.wikidata.org/prop/direct/>  # for Wikidata properties

SELECT * WHERE {

    # your local query heere, which binds the Wikidata identifier ?wdresource
    # ?wdresource must be a URI that starts with http://www.wikidata.org/entity/

    # test binding:
    BIND(wd:Q80 AS ?wdresource)

    SERVICE <https://query.wikidata.org/bigdata/namespace/wdq/sparql> {
        # return the Wikidata types of ?wd resource
        SELECT * WHERE {
             ?wdresource wdt:P31 ?wdtype .
        }
        LIMIT 5  # always use limit in remote queries
    }

    # possible to continue local query here 
}
LIMIT 10

Task: You can also try to connect to the INFO216 Sandbox (read/write) and KG4News Server (read-only).

Both SPARQL endpoints run Blazegraph, which uses "namespaces" instead of "repositories", so the URLs are a little different. In the web UI , the "NAMESPACES" tab lets you select an existing or create a new namespace to use.

SERVER = 'http://info216.i2s.uib.no/bigdata/'  # you may want to change this
NAMESPACE  = 'lab04'                           # you most likely want to change this

endpoint = f'{SERVER}namespace/{NAMESPACE}/sparql'  # standard path for Blazegraph queries (and updates)