sieve - data quality and fusion - lwdm2012

Sieve Linked Data

Quality Assessment

and Fusion

Pablo N. Mendes

Hannes Mühleisen

Christian Bizer

With contributions from:

Andreas Schultz, Andrea Matteini, Christian Becker, Robert Isele

“A sieve, or sifter, separates wanted elements

from unwanted material using a woven screen

such as a mesh or net.” Source: http://en.wikipedia.org/wiki/Sieve

“sieve”

• Raw data (RDF)

• Accessible on the Web

• Data can link to other data sources

• Benefits: Ease of access and re-use; enables discovery

What is Linked Data?

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A B C

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D E

data link data link data link data link

Linking Open Data Cloud

http://lod-cloud.net

Linked Data Challenges

• Data providers have different intentions, experience/knowledge

• data may be inaccurate, outdated, spam etc.

• Data sources that overlap in content may use…

• ... different RDF schemata

• ... different identifiers for the same real-world entity

• …conflicting values for properties

• Integrating public datasets with internal databases poses the

same problems

An Architecture for Linked Data Applications

LDIF – Linked Data Integration Framework

• Open source (Apache License, Version 2.0)

• Collaboration between Freie Universität Berlin and mes|semantics

Collect data: Managed download and update

Translate data into a single target vocabulary

Resolve identifier aliases into local target URIs

Output

1

2

3

5

Assess quality, filter bad results, resolve conflicts 4

Supported data sources:

• RDF dumps (various formats)

• SPARQL Endpoints

• Crawling Linked Data

LDIF Pipeline

Collect data

Translate data

Resolve identities

Filter and fuse

1

2

3

4

Output 5

dbpedia-owl: City

LDIF Pipeline

Collect data

Translate data

Resolve identities

1

2

3 R2R

• Mappings expressed in RDF (Turtle)

• Simple mappings using OWL / RDFs statements (x rdfs:subClassOf y)

• Complex mappings with SPARQL expressivity

• Transformation functions

Data sources use a wide range of different RDF

vocabularies

schema:Place

fb:location.citytown

local:City

Filter and fuse 4

Output 5

LDIF Pipeline

Collect data

Translate data

Resolve identities

1

2

3

Silk

Berlin, Germany

Berlin, CT

Berlin, MD

Berlin, NJ

Berlin, MA

Berlin

• Profiles expressed in XML

• Supports various comparators and transformations

Data sources use different identifiers for the same entity

Berlin

=

Berlin,

Germany

Filter and fuse 4

Output 5

LDIF Pipeline

Collect data

Translate data

Resolve identities

1

2

Sieve

891.85 km2

891.82 km2

891.82 km2

891.85 km2

Quality

• Profiles expressed in XML

• Supports various scoring and fusion functions

Sources provide different values for the same property

Filter and fuse

Output 5

4

3

Total Area

Total Area

891.85 km2

• Output options:N-Quads

• N-Triples

• SPARQL Update Stream

• Provenance tracking using Named

Graphs

LDIF Pipeline

Collect data

Translate data

Resolve identities

1

2

3

Filter and fuse 4

Output 5

An Architecture for Linked Data Applications

Data Quality and

Fusion Module

Data Fusion

“fusing multiple records representing the same

real-world object into a single, consistent, and

clean representation”

(Bleiholder & Naumann, 2008)

Conflict resolution strategies

• Independent of quality assessment metrics

• Pick most frequent (democratic voting)

• Average, max, min, concatenation

• Within interval

• Based on task-specific quality assessment

• Keep highest scored

• Keep all that pass a threshold

• Trust some sources over others

• Weighted voting

Data Fusion

• Input:

• (Potentially) conflicting data

• Quality metadata describing input

• Execution:

• Use existing or custom FusionFunctions

• Output:

• Clean data, according to user’s definition of clean

Configuration: Data Fusion

Sieve: Quality Assessment

• Quality as “fitness for use”:

• Subjective:

• good for me might not be enough for you

• Task dependent:

• temperature: planning a weekend vs biology experiment

• Multidimensional:

• even correct data may be outdated or not available

• Requires task-specific quality assessment.

Data Quality - Conceptual Framework Dimension

Accuracy

Consistency

Objectivity

Timeliness

Validity

Believability

Completeness

Understandability

Relevancy

Reputation

Verifiability

Amount of Data

Interpretability

Rep. Conciseness

Rep. Consistency

Availability

Response Time

Security

Configuration: Quality Assessment

• Quality Assessment Metrics composed by:

• ScoringFunction (generically applicable to given data types)

• Quality Indicator as input (adaptable to use case)

• Output: [0;1]

Describes input within a quality dimension,

according to a user’s definition of quality

Configuration: Quality Assessment

More about Sieve

• Software: Open Source, Apache V2

• Scoring Functions and Fusion Functions can be extended

• Scala/Java interface, methods score/fuse and fromXML

• Quality scores can be stored and shared with other

applications

• Website: http://sieve.wbsg.de

• Documentation, examples, downloads, support

Use Case

Conflicting values

Quality indicators

User config Voilá!

(Multidimensional)

(Task-dependent)

Multiple data sources

(Complementary)

(Conflict

Resolution

Strategies)

(Heterogeneous)

Evaluating Quality of Data Integration

• Completeness

• How many cities did we find?

• How many of the properties did we fill with values?

• Conciseness

• How much redundancy is there in the object identifiers?

• How much redundancy is there in the property values?

• Consistency

• How many conflicting values are there?

Results

Generated data that is more complete, concise

and consistent than in the original sources

Linked Data application Architecture

My view on this data space can also be

shared, and reused.

We can “pay as we go”

• Twitter: @pablomendes

• E-mail: pablo.mendes@fu-berlin.de

• Website: http://sieve.wbsg.de

• Google Group: http://bit.ly/ldifgroup

THANK YOU!

Supported in part by: Vulcan Inc. as part of its Project Halo

EU FP7 projects:

-LOD2 - Creating Knowledge out of Interlinked Data

-PlanetData - A European Network of Excellence on Large-Scale Data Management