The slides presented in the F2F meeting of the 4V project on 15th of June, 2015 at Zaragoza.

1. 4V: Volumen, Velocidad, Variedad y Validez
en la gestión innovadora de datos
(TIN2013-46238)
Progress Report – WP3
Zaragoza, 15 de Junio 2016
Ontology Engineering Group (OEG)
Escuela Técnica Superior de Ingenieros Informáticos
Universidad Politécnica de Madrid
Campus de Montegancedo,
Boadilla del Monte, 28660, Spain

2. Outline
• Loupe
• On-going work
• Quality Assessment and Repair
• Conciseness
• Consistency
• Collaborations
• A two-fold quality assurance approach for dynamic KBs: The
3cixty use case
2Nandana Mihindukulasooriya, OEG

3. Loupe - An Online Tool for Inspecting Datasets in
the Linked Data Cloud
Demo @ ISWC2015
3Nandana Mihindukulasooriya, OEG

4. Loupe - Overview
4Nandana Mihindukulasooriya, OEG
Explore the vocabularies used and the abstract triple patterns in 2+
billion triples including all Dbpedia datasets, Wikidata, Linked Brainz,
Bio2RDF.
Loupe helps to understand data, uncover patterns, formulate queries, and detect
quality issues
Loupe - An Online Tool for Inspecting Datasets in the Linked Data Cloud
Demo @ ISWC2015.

5. Loupe – Google Analytics
5Ontology Engineering Group, Universidad Politécnica de Madrid

6. Loupe – Google Analytics (II)
• Users from 84 countries
• Spain(23.76%), US (16.69%), Germany (10.64%), UK
(9.14%), Italy (4.51%)
6Ontology Engineering Group

7. Loupe
On-going work
7Nandana Mihindukulasooriya, OEG

8. Loupe – Use Case Analysis
• Dataset Descriptions
• Dataset statistics
• Dataset profiling
• Dataset exploration
• Class/property browsing
• Triple pattern browsing
• Dataset discovery and
recommendation
• keywords, vocabularies
• SPARQL queries
• RDF shapes
8Ontology Engineering Group
• Quality assessment
• Consistency
• Misused vocabularies
• Guided SPARQL query
generation
• auto-complete based on
abstract triple patterns
• Vocabulary reuse and
recommendation
• Recommendation of
vocabularies based on
popularity
• Ontology development
feedback
• Common properties

9. Loupe – LOD Laundromat integration
9Nandana Mihindukulasooriya, OEG
• Current status of Loupe
• 2 billion triples from 32 datasets
• LOD Laundromat
• 32 billion triples from 650K documents
• cleaned for syntax errors and duplicates
• coverage of smaller documents
• Collaboration with VU University Amsterdam
• Steps
• Fully automatic dataset download, SPARQL endpoint
creation, indexing, clean up
• UI changes to handle large number of datasets
• Vocabulary usage datasets

10. Loupe Ontology – Vocabulary Usage Statistics of LOD
• Analysis of existing metrics
• VoID
• DCAT
• RDFStats
• LODStats
• VoID-Ext
• Analysis of use case requirements
• Statistics
• Profiling
• Discovery
• Recommendation
10Nandana Mihindukulasooriya, OEG

11. Loupe Ontology
11Nandana Mihindukulasooriya, OEG

12. An Analysis of the Quality Issues of the Properties
Available in the Spanish Dbpedia
CAEPIA 2015, Albacete
12Nandana Mihindukulasooriya, OEG

13. Analyzed Quality Dimensions
13Nandana Mihindukulasooriya, OEG
An Analysis of the Quality Issues of the Properties Available in the Spanish Dbpedia
CAEPIA2015.
A. Conciseness. A dataset does not contain
redundant concepts with different identifiers.
B. Consistency. A dataset does not contain
conflicting or contradictory data.
C. Syntactic Validity. Values belong to the
legal value range for the represented domain
and do not violate the syntactic rules.
D. Semantic Accuracy. Values correctly
represent real world facts

14. Conciseness
• Many redundant properties in esDBpedia
• 97.93% are auto-generated
• Causes
• Capitalization (857): partidosEnPrimera,partidosenprimera
• Synonyms: causaDeMuerte, causaDeFallecimiento
• Prepositions: causaDeFallecimiento, causaFallecimiento
• Spelling (7,495): apeliido, apelldio, apellid
• Singular/plural: apellido, apellidos
• Gender: administrador, administradora
• Accent usage (1,252): administracion, administración
• Parsing (107): altitudMin/máx, residencia/trabajo, idioma/s
14Ontology Engineering Group, Universidad Politécnica de Madrid

15. Consistency
• Diverse and incorrect domain and range types
• esdbpedia:edad has range of type dbo:Place
• esdbpedia:lugarmuerte has range of type dbo:Person
• esdbpedia:pais has range of type dbo:Actor
• OWL properties with IRI and literal values
• 3,380 properties
• Use of strings and URL interchangeably
• esdbpedia:lugarDeEntierro
• "Madrid"@es
• http://es.dbpedia.org/resource/Madrid
15Ontology Engineering Group, Universidad Politécnica de Madrid

16. Conciseness
16Nandana Mihindukulasooriya, OEG

17. How to query for the birth place of a person in DBpedia?
17Nandana Mihindukulasooriya, OEG
DBpedia
(lang)
Syntactically Similar Semantically
Similar
English birthplace, birthplace, placeofbirth, birthplace,
birthdplace, birthPalce, birthplace, PlaceOfBirth,
laceOfBirth, oplaceOfBirth, birthplace, birthplace,
birthPalce, birthPlae, birthPace, birthPlaxe,
birtPlace, birthPlcace, bithPlace, brithPlace,
nbirthPlace, birthplace, birghPlace, birthdplace,
biRthPlace, birth, placebirth, placeOfBirth,
placOfBirth, birthPlaceOf, birthPlae
cityofbirth,
cityofbirthPlace,
cityOfBirth,
birthLocation
Spanish birthPlace, placeOfBirth, birthPlace, birthplace
lugarDeNacimiento, lugarNacimiento,
lugarNacimiento, lugarnacimiento,
lugardenacimiento, lugarNacimento, lugarNaciento
ciudaddenacimiento,
ciudadDenacimiento,
paisdenacimiento,
paisNacimiento
German geburtsort, birthplace, birthPlace, placeOfBirth
placeofbirth
geburtsland,
countryofbirth

18. Conciseness
• Less-concise datasets
• Multiple identifiers with same semantics
• Issues
• Harder to understand data and vocabularies used
• Harder to write queries
• Harder to reuse
• Causes
• Less concise mappings
• Diverse distributed mappings created by multiple teams
• No policies or guidance of consistent vocabulary usage
• No tools for recommending class / properties
• Crowd-sourced ontologies
• No or minimum labels / descriptions
18Nandana Mihindukulasooriya, OEG

19. RDF generation process
19Nandana Mihindukulasooriya, OEG
Bulk RDF Transformation
(e.g., LOD Refine, DBpedia extraction
framework, Ad-hoc programs)
structured data
unstructured
Query Rewriting
RDF Mappings
(e.g., R2RML,
Mappings Wiki, D2R
mappings, LOD
Refine RDF
skeletons)
SPARQL Endpoint
(e.g., Virtuoso, Fuseki)
RDF
Dumps
Linked Data
Resources
(e.g,, Pubby, ELDA)
Triple Store Web Server
SPARQL Clients Linked Data Clients
Data sources
Transformation
Storage
Access

20. DBpedia extraction process
20Nandana Mihindukulasooriya, OEG
RDF
Triple
store
Rendering

21. Issues in DBpedia mappings
• 16 DBpeida chapters
• Crowd-sourced mappings using mapings wiki
• 5553 template mappings
• Mostly using DBpedia ontology
• 739 classes, 3049 properties
• In-concise usage of similar properties
• elevation & height, formationYear & foundingYear, team &
club, occupation & profession, foundedBy & founder
• Plan for repair
• Detection of inconsistent property usage
• Feedback to the ontology team
• Feedback and guidance to the mapping teams
• Automatic cleaning of the mappings (in RML)
21Nandana Mihindukulasooriya, OEG

22. Repairing conciseness issues in mappings
22Nandana Mihindukulasooriya, OEG
Bulk RDF Transformation
(e.g., LOD Refine, DBpedia extraction
framework, Ad-hoc programs)
structured data
unstructured
Query Rewriting
RDF Mappings
(e.g., R2RML,
Mappings Wiki, D2R
mappings, LOD
Refine RDF
skeletons)
SPARQL Endpoint
(e.g., Virtuoso, Fuseki)
RDF
Dumps
Linked Data
Resources
(e.g,, Pubby, ELDA)
Triple Store Web Server
SPARQL Clients Linked Data Clients
Data sources
Transformation
Storage
Access

23. Detecting in-concise mapping based on data
dbr:Adobe_Systems dbo:formationYear “1982” ^^xsd:gYear
23Ontology Engineering Group
dbr:Adobe_Systems dbo:foundingYear “1982” ^^xsd:gYear
DBpedia EN
DBpedia ES

24. Detection of in-concise mappings
24Nandana Mihindukulasooriya, OEG
SC P1 ?o
Graph 1 (e.g., Dbpedia EN) Graph 2 (e.g., Dbpedia ES)
SC P2 ?oM1(C,P1,P2)
M2(C,P1,P2) SC P1 O SC P2 O
M3(C,P1,P2) SC P1 O1 SC P2 O2
M4(G1,C,P1,P2)
M5(G2,C,P1,P2) SC
P1 ?o
P2 ?o
SC
P1 ?o
P2 ?o
C P1 P1 M1 M2/
M1
M3/
M1
M4/
M1
M5/
M1
Company foundingYear formationYear 170 0.72 0.24 0 0.05
Person activeYearsEndYear year 150 0.84 0.16 0 0
Person birthPlace deathPlace 2845 0.59 0.43 0.53 0.31
in-concise mappings
1
2
3
4
5

25. RDF generation process
25Nandana Mihindukulasooriya, OEG
Bulk RDF Transformation
(e.g., LOD Refine, DBpedia extraction
framework, Ad-hoc programs)
structured data
unstructured
Query Rewriting
RDF Mappings
(e.g., R2RML,
Mappings Wiki, D2R
mappings, LOD
Refine RDF
skeletons)
SPARQL Endpoint
(e.g., Virtuoso, Fuseki)
RDF
Dumps
Linked Data
Resources
(e.g,, Pubby, ELDA)
Triple Store Web Server
SPARQL Clients Linked Data Clients
Data sources
Transformation
Storage
Access

26. Property Maps
Property Map
Generation
• Step 1: group properties
into clusters according to
their domain and range
• Step 2: Multilingual NL
preprocessing
• Step 3: aggregate
properties by similarity
(syntactic and semantic)
26Ontology Engineering Group

27. Enhance SPARQL queries with property mappings
27Ontology Engineering Group

28. Consistency
28Nandana Mihindukulasooriya, OEG

29. Consistency
• Consistent data does not contain conflicting or
contradictory data.
29Nandana Mihindukulasooriya, OEG
@prefix dbr: <http://dbpedia.org/resource/> .
@prefix dbo: <http://dbpedia.org/ontology/> .
dbo:City a owl:Class ;
rdfs:subClassOf
[ a owl:Restriction ;
owl:onProperty dbo:populationTotal ;
owl:maxCardinality "1"^^xsd:nonNegativeInteger ],
[ a owl:Restriction ;
owl:onProperty dbo:mayor;
owl:maxCardinality "1"^^xsd:nonNegativeInteger ] .
dbo:country a owl:ObjectProperty ;
rdfs:domain dbo:City;
rdfs:range dbo:Country .

30. Consistency (II)
• Consistency issues
• Data does not comply with the formal definitions or schema
30Nandana Mihindukulasooriya, OEG
@prefix dbr: <http://dbpedia.org/resource/> .
@prefix dbo: <http://dbpedia.org/ontology/> .
dbr:Zaragoza a dbo:City;
dbo:populationTotal 666058;
dbo:populationTotal 684953;
dbo:country dbr:Aragón;
dbo:mayor dbr:Juan_Alberto_Belloch;
dbo:mayor dbr:Pedro_Santisteve_Roche .
dbr:Aragón a dbo:AutonomousCommunity .
1
2
3

31. populationTotal - Cardinality Violation
31Nandana Mihindukulasooriya, OEG
1

32. Consistency – (Incorrect) inferences
32Nandana Mihindukulasooriya, OEG
dbr:Juan_Alberto_Belloch owl:sameAs dbr:Pedro_Santisteve_Roche .
dbr:Aragón a dbo:Country .
• Open World Assumption and Non-Unique Name Assumption
• Works better for inferencing than validation
2
3

33. Consistency – Rich Semantics
• Checking consistency with OWL.
33Nandana Mihindukulasooriya, OEG
@prefix dbr: <http://dbpedia.org/resource/> .
@prefix dbo: <http://dbpedia.org/ontology/> .
@prefix dbo: <http://www.w3.org/2002/07/owl#>.
dbo:City a owl:Class ;
rdfs:subClassOf [ a owl:Restriction ; owl:onProperty dbo:populationTotal ;
owl:maxCardinality "1"^^xsd:nonNegativeInteger ],
[ a owl:Restriction ; owl:onProperty dbo:mayor;
owl:maxCardinality "1"^^xsd:nonNegativeInteger ] .
dbo:country a owl:ObjectProperty; rdfs:domain dbo:Place; rdfs:range dbo:Country .
dbo:AutonomousCommunity owl:disjointWith dbo:Country .
dbr:Juan_Alberto_Belloch owl:differentFrom dbr:Pedro_Santisteve_Roche .
2
3

34. Consistency – SHACAL constraints
• Checking consistency with W3C SHACL.
34Nandana Mihindukulasooriya, OEG
@prefix sh: <http://www.w3.org/ns/shacl#>
@prefix dbo: <http://dbpedia.org/ontology/> .
_:cityShape a sh:Shape;
sh:scopeClass dbo:City;
sh:property [
sh:predicate dbo:mayor;
sh:maxCount 1;
sh:nodeKind sh:IRI;
sh:classIn (dbo:Person schema:Person foaf:Person)
] ;
sh:property [
sh:predicate dbo:country;
sh:maxCount 1;
sh:minCount 1;
sh:nodeKind sh:IRI;
sh:classIn (dbo:Country);
sh:stem “http://dbpedia.org/” ] .

35. Data validation with semi-automatically generated RDF Shapes
35Nandana Mihindukulasooriya, OEG
Pattern
Extraction
Domain
Expert
Review
RDF Shape
Generation
Data
Validation
Data
Repair
SHACL Shapes

36. Cardinality constraints example
36Nandana Mihindukulasooriya, OEG
schema:Place Min Max P1 P99 Mean 0 1 2 3 4 5
rdf:type 1 2 1 1 1.0002 0 99.9793 0.0207 0 0 0
rdfs:label 1 6 1 6 4.2508 0 4.4048 36.6743 1.7445 0.4831 0
rdfs:seeAlso 0 4 1 2 1.5717 0.0340 42.7702 57.1905 0.0041 0.0011 0
owl:sameAs 0 6 0 0 0.0058 99.4455 0.5339 0.0146 0.0041 0.0015 0
schema.org:review 0 2 0 2 0.0329 98.3175 0.0717 1.6108 0 0 0
schema.org:url 0 40 0 10 0.5085 89.8340 1.8947 3.7013 0.3008 1.2155 0.3434
events:poster 0 23 0 1 0.0155 98.9609 0.5900 0.4237 0.0097 0.0120 0.0007
dc:publisher 0 2 0 2 1.0677 39.1777 14.8776 45.9447 0 0 0
events:businessType 0 4 0 2 1.5273 4.1889 38.9255 56.8673 0.0041 0.0142 0
schema:description 0 28 1 12 3.0573 0.0886 30.5193 32.8359 1.9605 19.1139 0.1226
geo:location 0 24 0 4 0.2040 92.7525 0.6819 3.2436 0.2634 2.9831 0.0060
Property cardinalities of schema:Place class
(extracted from data)
Pat. Min Max Description
A 0 N No restrictions
B 0 1 Maximum 1
C 1 N Minimum 1
D 1 1 Exactly 1
Common cardinalities
Cardinality
Classifier
schema:Place Class
rdf:type D (Exactly 1)
rdfs:label C (Minimum 1)
rdfs:seeAlso C (Minimum 1)
owl:sameAs A (No restrictions)
schema.org:review A (No restrictions)
Expert Review
schema:Place Class
rdf:type C (Minimum 1)
rdfs:label C (Minimum 1)
rdfs:seeAlso C (Minimum 1)
owl:sameAs A (No restrictions)
schema.org:review A (No restrictions)
_:placeShape a sh:Shape;
sh:scopeClass schema:Place;
sh:property [
sh:predicate rdf:type;
sh:minCount 1
] ;
sh:property [
sh:predicate rdfs:label;
sh:minCount 1
] ;
sh:property [
sh:predicate rdfs:seeAlso;
sh:minCount 1
] ;
Approved PatternsExtracted Patterns
Restrictions in SHACL

37. W3C SHACL restrictions
• Value type constraints
• sh:class, sh:classIn, sh:datatype, sh:datatypeIn,
sh:nodeKind
• Cardinality constraints
• sh:minCount, sh:maxCount
• Value range constraints
• sh:minInclusive, sh:minExclusive, sh:maxInclusive,
sh:maxExclusive
• String based constraints
• sh:minLength, sh:maxLength, sh:pattern, sh:stem,
sh:uniqueLang
• Property pair constraints
• sh:equals, sh:disjoint, sh:lessThan, sh:lessThanOrEquals
37Ontology Engineering Group

38. A Two-Fold Quality Assurance Approach
for Dynamic Knowledge Bases: The 3cixty Use Case
38Nandana Mihindukulasooriya, OEG

39. Continuous Integration is essential
39Ontology Engineering Group, Universidad Politécnica de Madrid

40. Exploratory testing with Loupe
40Ontology Engineering Group, Universidad Politécnica de Madrid

41. Automated testing with SPARQL Interceptor
41Ontology Engineering Group, Universidad Politécnica de Madrid
• a set of user-defined SPARQL queries (as unit tests)
• Knowledge-based specific
Test
SPARQL
Queries
System
Requirements
Schema
Constraints
Conventions
and other
restrictions
Inputs from
Exploratory
Testing

42. SPARQL Interceptor
42Ontology Engineering Group, Universidad Politécnica de Madrid
Designed and implemented by Localidata.