davidsbatista

DBpedia SPARQL Explorer: http://dbpedia.org/snorql/

DBpedia Virtuoso SPARQL Query Editor: https://dbpedia.org/sparql

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People and death-causes

PREFIX dbo: <http://dbpedia.org/ontology/> 

davidsbatista

1. Follow the tutorial to install Tensorflow implementation of Syntaxet

2. Download the trained Portuguese model from universal dependencies and unzipped it.

3. Run the following command on the shell

MODEL_DIRECTORY=/Users/dbatista/Downloads/Portuguese
cd /Users/dbatista/models/syntaxnet
echo "Quase 900 funcionários do Departamento de Estado assinaram memorando que critica Trump" | syntaxnet/models/parsey_universal/parse.sh $MODEL_DIRECTORY

davidsbatista

import numpy as np
import matplotlib.pyplot as plt

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
from sklearn.utils.multiclass import unique_labels

def plot_confusion_matrix(y_true, y_pred, classes, normalize=False, title=None, cmap=plt.cm.Blues):
    """

davidsbatista

SELECT ?iata_code ?places_served_label(LANG(?places_served_label) AS ?places_served_label_lang) ?country_code
WHERE {
  ?item wdt:P238 ?iata_code.
  ?item wdt:P931 ?places_served.
  ?places_served rdfs:label ?places_served_label.
  ?places_served wdt:P17 ?country.
  ?country wdt:P297 ?country_code.
  FILTER( LANG(?places_served_label) = "de" || LANG(?places_served_label) = "en").       
}

davidsbatista

SELECT DISTINCT ?station_label ?ibnr_stmt ?x ?ibnr_type_stmnt ?label WHERE
    {
      wd:Q376589 rdfs:label ?station_label.      
      wd:Q376589 p:P954 ?ibnr_stmt.                 
      ?ibnr_stmt ps:P954 ?x.
      ?ibnr_stmt pq:P518 ?ibnr_type_stmnt.
      ?ibnr_type_stmnt rdfs:label ?label.
      FILTER(lang(?station_label) = 'de')
      FILTER(lang(?label) = 'de')
    }

davidsbatista

SELECT DISTINCT ?capital ?capital_label ?pop ?code WHERE {
        ?county wdt:P31/wdt:P279* wd:Q47168 .      
        ?county wdt:P36 ?capital .
        ?capital rdfs:label ?capital_label .
        ?capital wdt:P1082 ?pop .
        ?county wdt:P131 ?state .
        ?state wdt:P31 wd:Q35657 . 
        ?state wdt:P300 ?code .
      FILTER(LANG(?capital_label) = "en")
      FILTER(?pop >= 50000).

davidsbatista

SELECT ?iata_code ?airport_name (COUNT(?connectsairport) AS ?nr_connections) 
WHERE
{
  VALUES ?airport { wd:Q17480 wd:Q9694 wd:Q160556 wd:Q403671}
  ?airport wdt:P238 ?iata_code.
  ?airport rdfs:label ?airport_name.
  OPTIONAL {
      ?airport wdt:P81 ?connectsairport.
  }  
  FILTER(LANG(?airport_name) = "en")

davidsbatista

#defaultView:Map
SELECT DISTINCT ?dam ?coords ?lat ?long WHERE {
        ?dam wdt:P31/wdt:P279* wd:Q12323 .      
        ?dam wdt:P17 wd:Q45 .
        ?dam p:P625 ?coordinataes .
        ?coordinataes ps:P625 ?coords;
                      psv:P625 [
                        wikibase:geoLatitude ?lat;
                        wikibase:geoLongitude ?long
                      ] .

davidsbatista

#defaultView:Map
SELECT DISTINCT ?dam ?name ?elevation ?coords WHERE {
        # ?dam wdt:P31/wdt:P279* wd:Q12323 .
        ?dam wdt:P31/wdt:P279* wd:Q15911738 .  # only hidroelectric cpower
        ?dam wdt:P17 wd:Q45 .  # only located in Portugal
        ?dam rdfs:label ?name  FILTER(LANG(?name) = "pt") .
        ?dam p:P2044 ?elevation_sea_level .
        ?elevation_sea_level ps:P2044 ?elevation .
        ?dam p:P625 ?coordinataes .
        ?coordinataes ps:P625 ?coords;

davidsbatista

#defaultView:Map
SELECT DISTINCT ?companyLabel ?company ?headquarters ?headquartersLabel ?coordinates WHERE {  
  VALUES ?stock_markets {wd:Q155718 wd:Q378967 wd:Q595622 wd:Q874430} .
  ?company wdt:P361 ?stock_markets .
  ?company rdfs:label ?companyLabel . FILTER(LANG(?companyLabel) = "en")
  ?company wdt:P159 ?headquarters.
  ?headquarters rdfs:label ?headquartersLabel . FILTER(LANG(?headquartersLabel) = "en") 
  ?headquarters wdt:P625 ?coordinates
}
ORDER BY ASC(?companyLabel)