Run Queries

Table of contents

1. Run Queries
2. Run queries in browser
   1. Comunica
   2. Virtuoso Stad Gent
3. Run queries locally
   1. Comunica query engine
   2. SparQL Wrapper
   3. pyLoDStorage

Run queries in browser

queries can be run directly in the browser using services such as Comunica and Virtuoso.

Comunica

Comunica

Virtuoso Stad Gent

Queries can be run in the virtuoso environment from Stad Gent. Results can than be exported in multiple formats.

Run queries locally

Comunica query engine

Installing the Comunica query engine, you can sparql locally using javascript and node.js. The default Comunica query engine that exposes most standard features is Comunica SPARQL, which uses the package name @comunica/query-sparql. Futher documentation can be found here. In addition, this youtube tutorial also show how to set up the comunica query engine.

SparQL Wrapper

To sparql query with python, the package SPARQLWrapper can be used. Documentation can be found here.

for example

from SPARQLWrapper import SPARQLWrapper, JSON

sparql = SPARQLWrapper("https://stad.gent/sparql")

sparql.setReturnFormat(JSON)
sparql.setQuery("""
    PREFIX cidoc: <http://www.cidoc-crm.org/cidoc-crm/>    
    SELECT ?title FROM <http://stad.gent/ldes/dmg>     
    WHERE { 
      ?object cidoc:P102_has_title ?title
    } LIMIT 100
    """)

try:
    ret = sparql.queryAndConvert()
for r in ret["results"]["bindings"]:
        print(r)
except Exception as e:
    print(e)

pyLoDStorage

Another method to sparql with python is the library pyLODStorage. This library makes it possible to store the results directly into a pandas dataframe.

for example

from lodstorage.sparql import SPARQL
from lodstorage.csv import CSV
import pandas as pd

sparqlQuery = ("""
    PREFIX cidoc: <http://www.cidoc-crm.org/cidoc-crm/>
    
    SELECT ?objectname 
    WHERE { 
      ?object cidoc:P41i_was_classified_by ?identifier.
      ?identifier cidoc:P42_assigned ?objectname.
    } 
    """)

sparql = SPARQL("https://stad.gent/sparql")
qlod = sparql.queryAsListOfDicts(sparqlQuery)
csv = CSV.toCSV(qlod)
df_result = pd.DataFrame([x.split(',') for x in csv.split('\n')])
print(df_result)