FAME.Q – A Formal approach to Master Quality in Enterprise Linked Data

Chemnitz University of Technology Prof. Dr.-Ing. Martin Gaedke & Team 29.11.2016

FAME.Q A Formal Approach to Master Quality in Enterprise Linked Data

André Langer and Martin Gaedke

Semantic Web and XML @ ICWI2016

VSR://IntelligentInformationManagement/LEDS

What we do

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VSR://IntelligentInformationManagement/LEDS/Background 3

Linked Open Data Corporate Data Social Networks

Data Lake

Knowledge Graphs

Management of Background Knowledge

Data Quality and Coherence

Knowledge Extraction

Search in Linked Data

E-Commerce Applications

VSR://IntelligentInformationManagement/LEDS/Background 4

Linked Open Data Corporate Data Social Networks

Data Lake

Knowledge Graphs

Management of Background Knowledge

Data Quality and Coherence

Knowledge Extraction

Search in Linked Data

E-Commerce Applications

Main Focus

Initial

Question

5

What is Data Quality?

Which common definitions exist?

How can DQ be measured ?

VSR://IntelligentInformationManagement/LEDS/Motivation 6

Data Quality

• Is a multi-dimensional concept

• data that is „fit for use“ by data consumers (Wang & Strong, 1996; Strong, Lee & Wang, 1997b)

• data that is „free of defects and posesses desired features“ (Redman, 2001)


2.1 Simple Example

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How can existing

definitions be formalized? ?


Approach

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Data Quality characterizes

data to which degree it corresponds

to specific requirements

VSR://IntelligentInformationManagement/LEDS/Approach 14





Context





Context

metrics





Context

metrics a percentage


Context


Simplified Version


Common Quality dimensions and

appropriate metrics have already been

extensively classified by other authors

• Wang & Strong, 1996

• Zaveri et al, 2014


Common Quality dimensions and

appropriate metrics have already been

extensively classified by other authors

• Wang & Strong, 1996

• Zaveri et al, 2014

Zaveri, A. et al., 2014. Quality Assessment for Linked Open Data: A Survey. Semantic Web Journal, 1, p. 22


FAME.Q Quality Assessment Levels

Data Quality

Instance Level Schema Level Service Level


Example calculation 1


Example calculation 2


Summary: What is Data Quality?

„fatal“ „perfect“

Conclusion

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VSR://IntelligentInformationManagement/LEDS/Conclusion 27

Data Quality can be interpreted as the degree to which data fits to current requirements • Build upon and reuse existing definitions • Apply it to the field of the Semantic Web • Set it in a formalized schema

4.1 Future Steps

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Several related Quality Measurement frameworks already exist(ed) with different result output capabilities • SWIQA (Fürber & Hepp, 2011a) • Luzzu (Debattista et al., 2015) • Roomba OpenData Checker (Assaf et al., 2015)


We output the results of our Quality Assessment tool with the means of the data quality vocabulary (dqv)

VSR

Chemnitz University of Technology Prof. Dr.-Ing. Martin Gaedke & Team 29.11.2016

Inspired and Interested?

[email protected]

VSR.Informatik.TU-Chemnitz.de

@andrelanger @myVSR /myVSR

FAME.Q – A Formal approach to Master Quality in Enterprise Linked Data

Data & Analytics

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