It is well understood that data is the foundation of our contemporary business world. But what happens if this foundation is not strong enough? Taking the example of the Open Data Hub, we will introduce the different dimensions of data quality, use the Virtual Knowledge Graph as a powerful tool for assessing the data quality and reason on why the data quality requires an ongoing commitment of all involved parties.

1. Data Quality
Data Quality at the Open Data Hub and
why it is everybody's business
Peter Hopfgartner - ODH Day

2. What is Data Quality

3. Possible definition
Data is [...] of high quality if it is
"fit for [its] intended uses in
operations, decision making and
planning".
[https://en.wikipedia.org/wiki/D
ata_quality]

4. Dimensions of Data
Quality
This can be a very complex topic.
No general agreement on the
attributes.
Some examples:
➔ accuracy
➔ completeness
➔ consistency

5. Importance of Data
Quality

6. Hypothetical example:
Google Maps
Google:
➔ has the geographic data
➔ builds services on data
➔ can evaluate the quality of
the data from usage
◆ did you arrive at the correct
place
◆ how happy were you with the
navigation
◆ did you use the route that GM
proposed?

7. Data of the ODH
Right now, data is used in many
web pages:
https://sparql.opendatahub.bz.it
/predefined/accommodation?Id=
4E44BB8764234E7B937652859C
B7BBAD

9. (Good) data generates
value
ODH data can be used for many
purposes:
➔ Tourism
➔ Research
➔ Mobility

10. Inspecting data

11. Incorrect Data 1
Finding wrong data
# on the top of the world?
PREFIX schema: <http://schema.org/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX : <http://noi.example.org/ontology/odh#>
SELECT ?h ?pos ?posLabel ?altitude
WHERE {
?h a schema:LodgingBusiness ;
schema:name ?posLabel ;
geo:defaultGeometry/geo:asWKT ?pos ;
schema:geo/schema:elevation ?altitude ;
FILTER (lang(?posLabel) = 'de')
FILTER (?altitude > 3500)
} ORDER BY DESC(?altitude)

12. Restaurants in the
stratosphere?
posLabel altitude
Garni Wieterer 26430
Alberhof 10060
BNB Kreithof 4002
Ferienwohnungen
Steinegg
4000

13. Incorrect Data 1
Finding wrong data
# Tropic South Tyrol?
PREFIX schema: <http://schema.org/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
SELECT ?h ?pos ?posLabel ?box ?boxColor ?posColor {
?h a schema:LodgingBusiness ;
schema:name ?posLabel ;
schema:geo [ schema:latitude ?lat ;
schema:longitude ?long ] ;
geo:defaultGeometry/geo:asWKT ?pos .
FILTER (?lat = 0.0 || ?long = 0.0) .
FILTER (lang(?posLabel) = 'de') .
}

14. Incorrect Data 3
Wrong position
PREFIX schema: <http://schema.org/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
SELECT *
WHERE {
?h a schema:LodgingBusiness ;
geo:defaultGeometry/geo:asWKT ?hPos ;
schema:name ?hPosLabel ;
schema:address ?a .
?a schema:postalCode "39100" .
}

15. Incorrect Data 2
Eno-Camping?
14 Hotels are on the “Null Island”

16. Incomplete Data 1
Hotels without rooms
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX schema: <http://schema.org/>
SELECT ?pos ?posColor ?bName (?bName AS ?posLabel)
WHERE {
?b a schema:LodgingBusiness ;
schema:name ?bName ;
geo:defaultGeometry/geo:asWKT ?pos .
MINUS {
?r schema:containedInPlace ?b .
?r a schema:Accommodation
}
FILTER (lang(?bName) = 'en')
}

17. Incomplete Data 2
Is this Betten-Stop?
449 Hotels have no rooms

18. Incomplete Data 3
Missing names
PREFIX schema: <http://schema.org/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX : <http://noi.example.org/ontology/odh#>
SELECT * WHERE {
?h a ?c .
OPTIONAL {
?h schema:name ?hName .
FILTER (langMatches(lang(?hName), "de"))
}
VALUES (?c ?p) {
#(schema:FoodEstablishment schema:name)
(schema:LodgingBusiness schema:name)
(schema:FoodEstablishment schema:name)
}
VALUES (?l) {
("it") ("de") ("en")
}
MINUS {
VALUES (?l) {
("it") ("de") ("en")
}
?h ?p ?n .
FILTER(lang(?n) = ?l)
FILTER(str(?n) != "")
}
}
LIMIT 1000

19. Inconsistent Data 1
Misplaced Hotels
PREFIX uom: <http://www.opengis.net/def/uom/OGC/1.0/>
PREFIX geof: <http://www.opengis.net/def/function/geosparql/>
PREFIX geo: <http://www.opengis.net/ont/geosparql#>
PREFIX schema: <http://schema.org/>
PREFIX rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#>
PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>
PREFIX : <http://noi.example.org/ontology/odh#>
SELECT ?hPos (CONCAT(?hName, " (", ?munName, ") ",
str(?distance), " km away") AS ?hPosLabel) ?munName ?distance
WHERE {
?h a schema:LodgingBusiness ;
schema:name ?hName ;
geo:defaultGeometry/geo:asWKT ?hPos ;
schema:containedInPlace [
a :Municipality ;
schema:name ?munName ;
geo:defaultGeometry/geo:asWKT ?munPos ] .
FILTER(langMatches(lang(?hName), "de"))
FILTER(langMatches(lang(?munName), "de"))
FILTER(!geof:sfContains(?munPos,?hPos))
BIND(geof:distance(?hPos, ?munPos, uom:metre)/1000 AS
?distance)
FILTER(?distance > 1)
}

20. Inconsistent Data 1
Misplaced Hotels
20 Hotels are more than 10 km from their
municipality

21. Inspection tools
The Open Data Hub has
➔ Well integrated data
➔ Powerful inspection tools to
filter and compare data
➔ Full data querying language

22. Who should care?

23. Data providers
Can easily fix data upstream, if
they are aware of the issues.
ODH/Data integrators
Can do many routinary checks.
Data users
Notify ODH or the data provider
on any issue found

24. We make data usable