Using Semantic Web Resources for Data Quality Management

Using Semantic Web
Resources for Data Quality
Management
Christian Fürber and Martin Hepp
christian@fuerber.com, mhepp@computer.org

Presentation at the 17th International Conference on
Knowledge Engineering and Knowledge Management,
October 10-15, 2010, Lisbon, Portugal

Purpose of Data
Measurement Information &
Knowledge

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C. Fürber, M. Hepp: 2
Using SemWeb Resources for DQM

Data Quality in Practice

Reference: http://www.heise.de/newsticker/meldung/Comdirect-Bank-macht-Kunden-zu-Billiardaeren-996088.html


The Web of Messy Data?
Retrieved from http://dbpedia.org/sparql on July 20th

Which one is
the correct
population?


The Web of Messy Data?
Retrieved from http://dbpedia.org/sparql on July 20th

Places with
negative
population?!?


Risk of Failure
Measurement Information &
Knowledge

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Data Quality Problem Types
Inconsistent duplicates
Invalid characters Missing classification

Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/
Incorrect reference Approximate duplicates

Reference: Linking Open Data cloud diagram, by
Character alignment violation

Word transpositions
Invalid substrings
Mistyping / Misspelling errors
Cardinality violation
Missing values Referential integrity violation
Misfielded values
Unique value violation False values Functional Dependency
Out of range values
Violation Imprecise values
Existence of Homonyms Meaningless values
Incorrect classification
Existence of Synonyms Contradictory relationships
Outdated conceptual elements Untyped literals Outdated values


Goals

• Use Semantic Web data to identify data
quality problems on instance level

• Support Data Quality Management (DQM)
process


Total Data Quality Management
for and based on the Semantic Web
Develop and
Define what‘s
apply SPARQL
good and / or
queries based
what‘s poor Define Measure
on DQ-
data quality
Definition

DQ
Improve Analyze

Reference: Richard Wang (1998)


How can the Semantic Web support
Data Quality Management?

Availability of FREE Data Quality Knowledge,
e.g. for the identification of…

• Legal value violations
• Functional dependency violations


Using Trusted References
Las Vegas France DQ-Constraints

local:Location tref:Location

Las Vegas

Las Vegas
France
USA

Tested Knowledgebase Trusted Reference


Basic Architecture


Basic Characteristics of SPIN
• Allows definition of generalized
SPARQL query templates
http://spinrdf.org/
• Constraint checking based on
SPARQL
• Definition of inferencing rules via
SPARQL


Generic Data Quality Constraints
Library for Easy DQ-Defintion
• Mandatory properties &
literals
• Legal values*
• Legal value ranges
• Functional dependencies*
• Legal syntaxes
• Uniqueness

* Designed to use trusted references

available @ http://semwebquality.org/ontologies/dq-constraints#

Definition of Data Quality
Constraints based on SPIN


Constraint checking in Practice


Legal Value Constraints
Return all instances of class vcard:Address that do not have a
matching value for property vcard:country-name in property
tref:country
SELECT ?s
WHERE {
?s a vcard:Address .
?s vcard:country-name ?value .
OPTIONAL {
?s2 a tref:Location .
?s2 tref:country ?value1 .
FILTER(str(?value1)= str(?value))
} .
FILTER(!bound(?value1))
}

Functional Dependency Constraints
Return all instances of vcard:ADR with city-country-combinations
that do not have a matching pair in instances of gn:Location.

SELECT ?s
WHERE {
?s a gr:LocationOfSalesOrServiceProvisioning .
?s vcard:ADR ?node
?node vcard:city ?value1 .
?node vcard:country ?value2 .
NOT EXISTS {
?s2 a gn:Location .
?s2 gn:asciiname ?value1 .
?s2 gn:country ?value2 .
}}


Acquisition of Semantic Web
Sources for DQM
(1) Replication of relevant knowledge-bases
(2) On the fly via federated SPARQL queries:
PREFIX dbo:<http://dbpedia.org/ontology/>
SELECT *
WHERE {
?s1 :location_CITY ?city .
OPTIONAL{
SERVICE <http://dbpedia.org/sparql>{
?s2 a dbo:City .
?s2 rdfs:label ?city .
FILTER (lang(?city) = "en") .
}
}
FILTER(!bound(?s2))
}


Limitations
• High degree of uncertainty about quality of Semantic
Web resources
• Risk for data quality problem proliferation
• Lack of Semantic Web resources for certain domains
• Flexible design of RDF and structural heterogeneity
complicate definition of generic DQ constraints
• Scalability on large data sets
• DQ constraints close the world


Contributions
• Data quality control for Semantic Web data
• Identification of potential inconsistencies
between Semantic Web Resources
• Reduction of effort for the definition of functional
dependency rules and legal value rules
• Reuse of shared data quality rules on a Web
scale


Future Work
• Semantic Web information quality assessment
framework (SWIQA) with computation of KPI‘s
• Analysis and identification of useful „trusted
references“ based on SWIQA
• Application on multi-source master data of
information systems
• Evaluation on large data sets


Data Quality Constraints Library for SPIN @
http://semwebquality.org/ontologies/dq-constraints#

Christian Fürber
Researcher
E-Business & Web Science Research Group

Werner-Heisenberg-Weg 39
85577 Neubiberg
Germany

skype c.fuerber
email christian@fuerber.com
web http://www.unibw.de/ebusiness
homepage http://www.fuerber.com
twitter http://www.twitter.com/cfuerber

Paper available at http://bit.ly/c5v6TM
23

Using Semantic Web Resources for Data Quality Management

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