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Visualizing Open Data with Plone
a practical guide on how to query and visualize Linked Open Data
                      with eea.daviz product




                                       Antonio De Marinis
                                         Web Technology Management
                                         European Environment Agency
                                                   www.eea.europa.eu
Linked Data evolution




     2007




                                          as per 2011
     keeps growing...
     > 1 million datasets
     Watch video STRATA conference 2013
Open Data - what is it?

    Open data is a philosophy and practice requiring
    that certain data be freely available to everyone,
    without restrictions from copyright, patents or
    other mechanisms of control.


    Linked Open Data (LOD) or simply Linked
    Data is a technique to interlink all open datasets
    into a web of data, aka semantic web, using
    technologies like RDF and SPARQL.
Linked Data vs classic ODBC
SPARQL query structure

 A SPARQL query comprises, in order:
 ● Prefix declarations, for abbreviating URIs
 ● Dataset definition, stating what RDF graph(s) are being queried
 ● A result clause, identifying what information to return from the query
 ● The query pattern, specifying what to query for in the underlying dataset
 ● Query modifiers, slicing, ordering, and otherwise rearranging query results
 # prefix declarations
 PREFIX foo: <http://example.com/resources/>
 ...
 # dataset definition
 FROM ...
 # result
              clause
 SELECT ...
 # query pattern
 WHERE {
     ...
 }
 # query modifiers
 ORDER BY ...
Real example querying DBpedia


SELECT * WHERE {
?subject rdf:type <http://dbpedia.org/ontology/City>.
?subject rdfs:label ?label.
?subject rdfs:comment ?abstract.
?subject <http://dbpedia.org/ontology/populationTotal> ?populationTotal.
FILTER (lang(?label) = "en" && lang(?abstract) = "en")
} LIMIT 5
Let's dive into a real example


 SELECT * WHERE {
 ?subject rdf:type <http://dbpedia.org/ontology/City>.
 ?subject rdfs:label ?label.
 ?subject rdfs:comment ?abstract.
 ?subject <http://dbpedia.org/ontology/populationTotal> ?populationTotal.
 FILTER (lang(?label) = "en" && lang(?abstract) = "en" && (?populationTotal >= "5000000"^^xsd:
 integer))
 } LIMIT 5
Let's dive into a real example

 PREFIX o: <http://dbpedia.org/ontology/>
 PREFIX p: <http://dbpedia.org/property/>
 PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
 PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>

 SELECT DISTINCT * WHERE {
 ?subject a o:City.
 ?subject rdfs:label ?label.
 OPTIONAL {?subject rdfs:comment ?abstract.}
 ?subject p:populationTotal ?populationTotal.
 OPTIONAL {?subject geo:lat ?latitude.}
 OPTIONAL {?subject geo:long ?longitude.}
 FILTER (lang(?label) = "en" && lang(?abstract) = "en" && (?populationTotal >= "5000000"^^xsd:integer
 && ?populationTotal < "60000000"^^xsd:integer))
 }
 ORDER BY DESC(?populationTotal)

      find all properties by exploring dbpedia e.g.
      dbpedia http://dbpedia.org/page/Tokyo

      Example without duplicates http://daviz.eionet.
      europa.eu/data/local-sparql-queries/most-
      populated-cities
Corresponding data visualisation with
Daviz




 We have been able to create a data visualisation of
 open linked data with filters/facets entirely through
 the web in about 10 minutes!

 live demo http://www.eea.europa.eu/sandbox/plog2013/most-populated-cities-
 with-coordinates-plus
Removing redundancies
TIP: In order to get rid of some rednundancy you can use "SAMPLE" or "SELECT DISTINCT"

PREFIX   o: <http://dbpedia.org/ontology/>
PREFIX   p: <http://dbpedia.org/property/>
PREFIX   rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX   geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>

SELECT ?subject (sql:SAMPLE(?subject) as ?city)
(sql:SAMPLE(?label) as ?label)
(sql:SAMPLE(?latitude) as ?latitude)
                                                        live example http://daviz.eionet.europa.
(sql:SAMPLE(?longitude) as ?longitude)
 max(?populationTotal) as ?maxPopulation
                                                        eu/visualisations/most-populated-cities
 max(?rainyDays) as ?rainyDays

WHERE {
?subject a o:City.
?subject rdfs:label ?label.
OPTIONAL {?subject rdfs:comment ?abstract.}
?subject p:populationTotal ?populationTotal.
OPTIONAL {?subject geo:lat ?latitude.}
OPTIONAL {?subject geo:long ?longitude.}
OPTIONAL {?subject p:yearPrecipitationDays ?rainyDays.}
FILTER (lang(?label) = "en" && lang(?abstract) = "en" && (?populationTotal >= "5000000"^^xsd:integer))
}
GROUP BY ?subject
ORDER BY DESC(?maxPopulation)
More examples at Daviz show room
      daviz.eionet.europa.eu
Example 2: Large companies (DBpedia)

 PREFIX o: <http://dbpedia.org/ontology/>
 PREFIX p: <http://dbpedia.org/property/>
 PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
 PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>

 SELECT * WHERE {
 ?subject rdf:type <http://dbpedia.org/ontology/Company>.
 ?subject rdfs:label ?label.
 ?subject rdfs:comment ?abstract.
 ?subject p:numEmployees ?employees.
 ?subject o:location ?location.
 ?location geo:lat ?latitude.
 ?location geo:long ?longitude.
 FILTER (lang(?label) = "en" && lang(?abstract) = "en" && ?employees > 10000)
 }
 ORDER BY DESC(?employees)
 LIMIT 20
Example 3: Energy plants (Enipedia)

 SPARQL Endpoint:http://enipedia.tudelft.nl/sparql


 BASE <http://enipedia.tudelft.nl/wiki/>
 PREFIX a: <http://enipedia.tudelft.nl/wiki/>
 PREFIX prop: <http://enipedia.tudelft.nl/wiki/Property:>
 PREFIX cat: <http://enipedia.tudelft.nl/wiki/Category:>
 PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
 PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
 select ?Name ?Point ?Generation_capacity where {
 ?powerPlant prop:Country a:Italy .
 ?powerPlant rdfs:label ?Name .
 ?powerPlant prop:Point ?Point .
 ?powerPlant prop:Generation_capacity_electrical_MW ?Generation_capacity .
 }
More resources



 ●   SPARQL endpoints and their status: http://labs.mondeca.
     com/sparqlEndpointsStatus/index.html
 ●   SPARQL tutorial by example: http://www.cambridgesemantics.com/semantic-
     university/sparql-by-example
 ●   eea.sparql package gives you a sparql client and data holder for plone
     available on pypi
 ●   eea.daviz bundle includes eea.sparql and the visualisations tools
Data table manipulation via drag and drop
Modular framework
EEA Daviz




       And much more...
EEA Daviz




            Live Demo
EEA Daviz - Resources


More live examples

   ○   Eionet
       http://daviz.eionet.europa.eu


   ○   EEA
       http://www.eea.europa.eu/data-and-maps/daviz

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Visualize open data with Plone - eea.daviz PLOG 2013

  • 1. Visualizing Open Data with Plone a practical guide on how to query and visualize Linked Open Data with eea.daviz product Antonio De Marinis Web Technology Management European Environment Agency www.eea.europa.eu
  • 2. Linked Data evolution 2007 as per 2011 keeps growing... > 1 million datasets Watch video STRATA conference 2013
  • 3. Open Data - what is it? Open data is a philosophy and practice requiring that certain data be freely available to everyone, without restrictions from copyright, patents or other mechanisms of control. Linked Open Data (LOD) or simply Linked Data is a technique to interlink all open datasets into a web of data, aka semantic web, using technologies like RDF and SPARQL.
  • 4. Linked Data vs classic ODBC
  • 5. SPARQL query structure A SPARQL query comprises, in order: ● Prefix declarations, for abbreviating URIs ● Dataset definition, stating what RDF graph(s) are being queried ● A result clause, identifying what information to return from the query ● The query pattern, specifying what to query for in the underlying dataset ● Query modifiers, slicing, ordering, and otherwise rearranging query results # prefix declarations PREFIX foo: <http://example.com/resources/> ... # dataset definition FROM ... # result clause SELECT ... # query pattern WHERE { ... } # query modifiers ORDER BY ...
  • 6. Real example querying DBpedia SELECT * WHERE { ?subject rdf:type <http://dbpedia.org/ontology/City>. ?subject rdfs:label ?label. ?subject rdfs:comment ?abstract. ?subject <http://dbpedia.org/ontology/populationTotal> ?populationTotal. FILTER (lang(?label) = "en" && lang(?abstract) = "en") } LIMIT 5
  • 7. Let's dive into a real example SELECT * WHERE { ?subject rdf:type <http://dbpedia.org/ontology/City>. ?subject rdfs:label ?label. ?subject rdfs:comment ?abstract. ?subject <http://dbpedia.org/ontology/populationTotal> ?populationTotal. FILTER (lang(?label) = "en" && lang(?abstract) = "en" && (?populationTotal >= "5000000"^^xsd: integer)) } LIMIT 5
  • 8. Let's dive into a real example PREFIX o: <http://dbpedia.org/ontology/> PREFIX p: <http://dbpedia.org/property/> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> SELECT DISTINCT * WHERE { ?subject a o:City. ?subject rdfs:label ?label. OPTIONAL {?subject rdfs:comment ?abstract.} ?subject p:populationTotal ?populationTotal. OPTIONAL {?subject geo:lat ?latitude.} OPTIONAL {?subject geo:long ?longitude.} FILTER (lang(?label) = "en" && lang(?abstract) = "en" && (?populationTotal >= "5000000"^^xsd:integer && ?populationTotal < "60000000"^^xsd:integer)) } ORDER BY DESC(?populationTotal) find all properties by exploring dbpedia e.g. dbpedia http://dbpedia.org/page/Tokyo Example without duplicates http://daviz.eionet. europa.eu/data/local-sparql-queries/most- populated-cities
  • 9. Corresponding data visualisation with Daviz We have been able to create a data visualisation of open linked data with filters/facets entirely through the web in about 10 minutes! live demo http://www.eea.europa.eu/sandbox/plog2013/most-populated-cities- with-coordinates-plus
  • 10. Removing redundancies TIP: In order to get rid of some rednundancy you can use "SAMPLE" or "SELECT DISTINCT" PREFIX o: <http://dbpedia.org/ontology/> PREFIX p: <http://dbpedia.org/property/> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> SELECT ?subject (sql:SAMPLE(?subject) as ?city) (sql:SAMPLE(?label) as ?label) (sql:SAMPLE(?latitude) as ?latitude) live example http://daviz.eionet.europa. (sql:SAMPLE(?longitude) as ?longitude) max(?populationTotal) as ?maxPopulation eu/visualisations/most-populated-cities max(?rainyDays) as ?rainyDays WHERE { ?subject a o:City. ?subject rdfs:label ?label. OPTIONAL {?subject rdfs:comment ?abstract.} ?subject p:populationTotal ?populationTotal. OPTIONAL {?subject geo:lat ?latitude.} OPTIONAL {?subject geo:long ?longitude.} OPTIONAL {?subject p:yearPrecipitationDays ?rainyDays.} FILTER (lang(?label) = "en" && lang(?abstract) = "en" && (?populationTotal >= "5000000"^^xsd:integer)) } GROUP BY ?subject ORDER BY DESC(?maxPopulation)
  • 11. More examples at Daviz show room daviz.eionet.europa.eu
  • 12. Example 2: Large companies (DBpedia) PREFIX o: <http://dbpedia.org/ontology/> PREFIX p: <http://dbpedia.org/property/> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#> SELECT * WHERE { ?subject rdf:type <http://dbpedia.org/ontology/Company>. ?subject rdfs:label ?label. ?subject rdfs:comment ?abstract. ?subject p:numEmployees ?employees. ?subject o:location ?location. ?location geo:lat ?latitude. ?location geo:long ?longitude. FILTER (lang(?label) = "en" && lang(?abstract) = "en" && ?employees > 10000) } ORDER BY DESC(?employees) LIMIT 20
  • 13. Example 3: Energy plants (Enipedia) SPARQL Endpoint:http://enipedia.tudelft.nl/sparql BASE <http://enipedia.tudelft.nl/wiki/> PREFIX a: <http://enipedia.tudelft.nl/wiki/> PREFIX prop: <http://enipedia.tudelft.nl/wiki/Property:> PREFIX cat: <http://enipedia.tudelft.nl/wiki/Category:> PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#> PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> select ?Name ?Point ?Generation_capacity where { ?powerPlant prop:Country a:Italy . ?powerPlant rdfs:label ?Name . ?powerPlant prop:Point ?Point . ?powerPlant prop:Generation_capacity_electrical_MW ?Generation_capacity . }
  • 14. More resources ● SPARQL endpoints and their status: http://labs.mondeca. com/sparqlEndpointsStatus/index.html ● SPARQL tutorial by example: http://www.cambridgesemantics.com/semantic- university/sparql-by-example ● eea.sparql package gives you a sparql client and data holder for plone available on pypi ● eea.daviz bundle includes eea.sparql and the visualisations tools
  • 15. Data table manipulation via drag and drop
  • 17. EEA Daviz And much more...
  • 18. EEA Daviz Live Demo
  • 19. EEA Daviz - Resources More live examples ○ Eionet http://daviz.eionet.europa.eu ○ EEA http://www.eea.europa.eu/data-and-maps/daviz