The Linked Data Lifecycle

The Linked Data Life-Cycle
Jens Lehmann

Quan Nguyen
Sebastian Hellmann
Claus Stadler

Lorenz Bühmann

contributors:

Sören Auer
Anja Jentzsch
Christina Unger

Richard Cyganiak
Dimitris Kontokostas

Daniel Gerber
Axel Ngonga

2013-08-23
Lehmann, Bühmann (Univ. Leipzig)


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Outline
1

Introduction to Linked Data

2

Linked Dataset Example: DBpedia

3

Linked Data Life-Cycle Overview

4

Knowledge Extraction

5

Data Integration / Linking

Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis

Knowledge Base Exploration / Querying


Search/
Browsing/
Exploration


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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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The Linked Data Principles

The term Linked Data refers to a set of best practices for publishing and
interlinking structured data on the Web.

Linked Data principles:
1

Use URIs as names for things.

2

Use HTTP URIs, so that people can look up those names.

3

When someone looks up a URI, provide useful information, using the
standards (RDF, SPARQL).

4

Include links to other URIs, so that they can discover more things.



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LOD Cloud



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Linked Data Principles Detailed: 1 + 2

1

URI references to identify not just Web documents and digital
content, but also real world objects and abstract concepts
tangible things: people, places
abstract things: relationship type of knowing somebody

2

HTTP URIs enable re-use of Web architecture Linked Data gives
emphasis to the Web in Semantic Web
Resource dereferencing
Re-use of standard tools for security, load-balancing etc.



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Principles Detailed: 3 Content Negotiation
Humans and machines should be able to retrieve appropirate
representations of resources:

machines


HTML for humans, RDF for


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machines


Achievable using an HTTP mechanism called

content negotiation

Basic idea: HTTP client sends HTTP headers with each request to
indicate what kinds of documents they prefer
Servers can inspect headers and select appropriate response



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machines


Achievable using an HTTP mechanism called

content negotiation

Basic idea: HTTP client sends HTTP headers with each request to
indicate what kinds of documents they prefer
Servers can inspect headers and select appropriate response
Two strategies:

303 URIs
Hash URIs

Both ensure that objects and the documents that describe them are
not confused + humans and machines can retrieve appropriate
representations



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303 URIs
303 Redirect:

instead of sending the object itself over the network,

the server responds to the client with the HTTP response code

303

See Other and the URI of a Web document which describes the
real-world object

Second step: client dereferences new URI and gets a Web document
describing the real-world object



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Hash URIs

Hash URI strategy builds on characteristic that URIs may contain a
special part (

fragment identier) separated from their base part by a

hash symbol (#)
HTTP protocol requires the fragment part to be stripped o before
requesting the URI from the server

→

a URI that includes a hash cannot be retrieved directly and

therefore does not necessarily identify a Web document



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Hash versus 303

Hash Uris

(+) Reduced number of necessary HTTP round-trips → reduces access
latency

(-) Descriptions of all resources sharing the same non-fragment URI
part are always returned to the client together

→

can lead to large

amounts of data being unnecessarily transmitted to the client

303 Uris

(+) Flexible because the redirection target can be congured
separately for each resource (usually points to a single document for
each resource, but could also summarise several resources)

(-) Requires two HTTP requests to retrieve a single description of a
real-world object



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Principles Detailed: 4 Links

If an RDF triple connects URIs in dierent namespaces/datasets, is is
called a

link (no unique syntactical denition of link

exists)

Basic idea of Linked Data: apply the general hyperlink-based
architecture of the World Wide Web to the task of sharing structured
data on global scale
Research challenge: ecient creation of links with high precision and
recall



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Why Linked Data?
Problem:

Try to search for these things on the current Web:

Apartments near German-Russian bilingual childcare in Leipzig.
ERP service providers with oces in Vienna and London.
Researchers working on multimedia topics in Eastern Europe.

Information is available on the Web, but opaque to current Web
search.



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Why Linked Data?
Problem:

Try to search for these things on the current Web:

Apartments near German-Russian bilingual childcare in Leipzig.
ERP service providers with oces in Vienna and London.
Researchers working on multimedia topics in Eastern Europe.

Information is available on the Web, but opaque to current Web
search.
Solution: complement text on Web pages with structured linked open data
intelligently combine/integrate such structured information from
dierent sources:



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How to get there?



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Tim Berners-Lee's 5-star plan

Tim Berners-Lee's 5-star plan for an open web of data
Make data available on the Web under an open license
Make it available as structured data
Use a non-proprietary format
Use URIs to identify things
Link your data to other people's data to provide context



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The 0th star
Data catalog with good metadata
Make your data ndable



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Data on the Web, Open License

��



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Open vs. Closed:
Data used to be closed by default
In the future, it may be open by default.



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Publishers: sharing data to make it more visible



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E-Commerce: Data sharing for increasing trac



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Community: Collaboratively created databases



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Good reasons against opening data

Privacy
Competitive advantage
Producing data and charging for it as business model
Can't get license from upstream



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Structured Data

Enabling re-use:
Delivering data to end users in dierent forms
Combining data with other data
3rd party analysis of data



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Structured Data

Formats:
Good for re-use / Structured: MS Excel, CSV, XML, JSON, Microdata
Not so good for re-use: Pure websites, MS Word
Bad for re-use: PDF
Really bad for re-use: Only charts/maps without numbers



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Non-Proprietary Formats

Specialist tools often have specialist formats
Few people have the tools
Expensive
Dicult to re-use
(Geospatial tools, statistics packages, etc.)

Non-proprietary:
CSV (dead simple)
XML
JSON
RDF (good for 4+5 stars)



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URIs as Identiers
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URIs as Identiers
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URIs as Identiers

URI-Design: prefer stable, implementation independent URIs



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URIs as Identiers

Turning local identiers into URIsWhy?
Make them globally unique
Clarify auhority
Make them resolvable
Make them linkable



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Links to Other Data
Hyperlinks are the soul of the Web. The Web of Data is no dierent.



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Links to Other Data
Hyperlinks are the soul of the Web. The Web of Data is no dierent.

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Summary
Linked Data Principles:
1 Use URIs to name things (not only documents, but also people,
locations, concepts, etc.)
2

To enable agents (human users and machine agents alike) to look up
those names,

3

use HTTP URIs

When someone looks up a URI,

provide useful information

(structured data in RDF, SPARQL).
4

Include

links to other URIs allowing agents to discover more things



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Summary
Linked Data Principles:
1 Use URIs to name things (not only documents, but also people,
locations, concepts, etc.)
2

To enable agents (human users and machine agents alike) to look up
those names,

3

use HTTP URIs

When someone looks up a URI,

provide useful information

(structured data in RDF, SPARQL).

links to other URIs allowing agents to discover more things
5-Star-Data:
4

Include

Five-star plan for realising an emerging web of data, dataset by
dataset
2 stars: re-usable data
3 stars: open standards
4+5 stars: connect data silos


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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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DBpedia

Community eort to extract structured information from Wikipedia
and to make this information available on the Web
Allows to ask sophisticated queries against Wikipedia, and to link
other data sets on the Web to Wikipedia data

Semi-structured Wiki markup


→

structured information


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Wikipedia Limitations

Simple Questions hard to answer with Wikipedia:
What have Innsbruck and Leipzig in common?
Who are mayors of central European towns elevated more than
1000m?
Which movies are starring both Brad Pitt and Angelina Jolie?
All soccer players, who played as goalkeeper for a club that has a
stadium with more than 40.000 seats and who are born in a country
with more than 10 million inhabitants



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Structure in Wikipedia
Title
Abstract
Infoboxes
Geo-coordinates
Categories
Images
Links
other language versions
other Wikipedia pages
To the Web
Redirects
Disambiguation

...



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DBpedia Information Extraction Framework

DBpedia Information Extraction Framework (DIEF)
Started in 2007
Hosted on Sourceforge and Github
Initially written in PHP but fully re-written Written in Scala and Java
Around 40 Contributors
See

https://www.ohloh.net/p/dbpedia

for detailed overview

Can potentially be adapted to other MediaWikis
Currently Wiktionary


http://wiktionary.dbpedia.org


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DIEF - Overview



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DIEF - Raw Infobox Extractor
WikiText syntax
{{Infobox Korean settlement
|title = Busan Metropolitan City
...
|area_km2 = 763.46
|pop = 3635389
|region = [[Yeongnam]]
}}

RDF serialization
dbp:Busan dbp:title Busan Metropolitan City
dbp:Busan dbp:area_km2 763.46^xsd:oat
dbp:Busan dbp:pop 3635389^xsd:int
dbp:Busan dbp:region dbp:Yeongnam



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DIEF - Raw Infobox Extractor/Diversity



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DIEF - Raw Infobox extractor/Diversity



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DIEF - Mapping-Based Infobox Extractor

Cleaner data:
Combine what belongs together (birth_place, birthplace)
Separate what is dierent (bornIn, birthplace)
Correct handling of datatypes
Mappings Wiki:

http://mappings.dbpedia.org
Everybody can contribute to new mappings or improve existing ones

≈

170 editors



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DIEF - Mapping-Based Infobox Extractor



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URI/IRI schemes
http://{lang.}dbpedia.org is the main domain
For every article there exists a DBpedia resource in the form:
http://lang.dbpedia.org/resource/{ArticleName}
Properties from the raw infobox extractor use the
http://{lang.}dbpedia.org/property/namespace
Ontology is global for all languages and under
http://dbpedia.org/ontology/namespace
Note: that for English language no language code is used
http://dbpedia.org as main domain
http://dbpedia.org/resource/{title} for articles
http://dbpedia.org/property/{title} for properties



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Linked Data Publication via 303 Redirects
http://dbpedia.org/resource/Dresden

- URI of the city of

Dresden

http://dbpedia.org/page/Dresden

- information resource

describing the city of Dresden in HTML format

http://dbpedia.org/data/Dresden

- information resource

describing the city of Dresden in RDF/XML format
further formats supported,
e.g.

http://dbpedia.org/data/Dresden.n3



for N3

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DBpedia Links
Data set

Predicate

Amsterdam Museum

owl:sameAs

BBC Wildlife Finder

owl:sameAs

Book Mashup

rdf:type

Count

Tool

627

S

444

S

9 100

owl:sameAs
Bricklink

dc:publisher

10 100

CORDIS

owl:sameAs

314

S

Dailymed

owl:sameAs

894

S

DBLP Bibliography

owl:sameAs

196

S

DBTune

owl:sameAs

838

S

Diseasome

owl:sameAs

2 300

S

Drugbank

owl:sameAs

4 800

S

EUNIS

owl:sameAs

3 100

S

Eurostat (Linked Stats)

owl:sameAs

253

S

Eurostat (WBSG)

owl:sameAs

137

CIA World Factbook

owl:sameAs

545



S
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DBpedia Links
Data set

Predicate

ickr wrappr

dbp:hasPhoto-

Count

Tool

3 800 000

C

3 600 000

C

Collection
Freebase

owl:sameAs

GADM

owl:sameAs

1 900

GeoNames

owl:sameAs

86 500

S

GeoSpecies

owl:sameAs

16 000

S

GHO

owl:sameAs

196

L

Project Gutenberg

owl:sameAs

2 500

S

Italian Public Schools

owl:sameAs

5 800

S

LinkedGeoData

owl:sameAs

103 600

S

LinkedMDB

owl:sameAs

13 800

S

MusicBrainz

owl:sameAs

23 000

New York Times

owl:sameAs

9 700

OpenCyc

owl:sameAs

27 100

C

OpenEI (Open Energy)

owl:sameAs

678

S



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DBpedia Links
Data set

Predicate

Revyu

owl:sameAs

6

Sider

owl:sameAs

2 000

TCMGeneDIT

owl:sameAs

904

UMBEL

rdf:type

US Census

owl:sameAs

WikiCompany

owl:sameAs

WordNet

dbp:wordnet_type

YAGO2

rdf:type

Sum

Count

Tool

S

896 400
12 600
8 300
467 100
18 100 000
27 211 732

(S: Silk, L: LIMES, C: custom script, missing: no regeneration)



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DBpedia Links - Query Example
Compare funding per year (from FTS) and country with the gross domestic
product of a country (from DBpedia)
SELECT

∗

{

{

SELECT ? f t s y e a r

? dbpcountry

? com

rdf : type

? com

fts

? year

fts

−o : y e a r

? year

rdfs : label

(SUM( ? amount )

−o : Commitment
.

fts

? ftscountry

o w l : sameAs

SELECT ? d b p c o u n t r y

? dbpcountry

? gdpyear

.

}

? gdpnominal

.
.

{

? dbpcountry

rdf : type

? dbpcountry

dbp : g d p N o m i n a l

? dbpcountry
}

{

.

? ftsyear

−o : d e t a i l A m o u n t ? amount .
? b e n e f i t f t s −o : b e n e f i c i a r y ? b e n e f i c i a r y
? b e n e f i c i a r y f t s −o : c o u n t r y ? f t s c o u n t r y
? benefit

AS ? f u n d i n g )

.

d bo : C o u n t r y

dbp : g d p N o m i n a l Y e a r

}

{

.

? gdpnominal
? gdpyear

.
.

}

FILTER

((? ftsyear


=

s t r (? gdpyear ) )

}


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Infrastructure
DBpedia has two extraction modes:
Wikipedia-database-dump-based extraction
DBpedia Live synchronisation (more later)
DBpedia Dumps:
The DBpedia Dump archive is located in:

http://downloads.dbpedia.org/
Latest downloads is described in: http://dbpedia.org/Downloads
Ocial Endpoint (by OpenLink): http://dbpedia.org/sparql



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Query Answering

Back to our Wikipedia questions:
What have Innsbruck and Leipzig in common?
Who are mayors of central European towns elevated more than
1000m?
Which movies are starring both Brad Pitt and Angelina Jolie?
All soccer players, who played as goalkeeper for a club that has a
stadium with more than 40.000 seats and who are born in a country
with more than 10 million inhabitants
Using the data extracted from Wikipedia and the public SPARQL endpoint
DBpedia can answer these questions.



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DBpedia Live

DBpedia dumps are generated on a bi-annual basis
Wikipedia has around 100,000 150,000 page edits per day
DBpedia Live pulls page updates in real-time and extraction results
update the triple store
In practice, a 5 minute update delay increases performance by 15%
Links

http://live.dbpedia.org/sparql
Documentation: http://wiki.dbpedia.org/DBpediaLive
Statistics: http://live.dbpedia.org/LiveStats/
SPARQL Endpoint:



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DBpedia Live - Overview



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DBpedia Internationalization (I18n)

DBpedia Internationalization Committee founded:

http://wiki.dbpedia.org/Internationalization
Available DBpedia language editions in:
Korean, Greek, German, Polish, Russian, Dutch, Portuguese, Spanish,
Italian, Japanese, French
Use the corresponding Wikipedia language edition for input

Mappings available for 23 languages



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DBpedia I18n - Overview



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Applications: Disambiguation

Named entity recognition and disambiguation Tools such as:

DBpedia

Spotlight, AlchemyAPI, Semantic API, Open Calais, Zemanta and Apache
Stanbol



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Applications: Question Answering

DBpedia is the primary target for several QA systems in the Question
Answering over Linked Data (QALD) workshop series
IBM Watson relied also on DBpedia



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Applications: Faceted Browsing

Neofonie Browser
gFacet
OpenLink faceted browser (fct)



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Applications: Search and Querying

Query Builder
RelFinder
SemLens



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Applications: Digital Libraries Archives

Virtual International Authority Files (VIAF) project as Linked Data
VIAF added a total of 250,000 reciprocal authority links to Wikipedia.

DBpedia can also provide:
Context information for bibliographic and archive records (e.g. an
author's demographics, a lm's homepage, an image etc.)
Stable and curated identiers for linking.
The broad range of Wikipedia topics can form the basis for a thesaurus
for subject indexing.



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Applications: DBpedia Mobile

DBpedia Mobile is a location-centric DBpedia client application for mobile
devices consisting of a map view, the Marbles Linked Data Browser and a
GPS-enabled launcher application.



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Applications: DBpedia Wiktionary

Wiktionary is a Wikimedia project: http://wiktionary.org
171 languages, 3M words for English.

Extracted Using the DBpedia Information Extraction Framework
Easily congurable for every Wiktionary language edition
Pre-congured for German, Greek, English, Russian and French.
http://Wiktionary.dbpedia.org
100 milion triples
Lemon model



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Other Applications

See

http://wiki.dbpedia.org/Applications



for a more complete list

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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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Linked Data - Achievements and Challenges
Achievements:
1

2
3

data
commons (50B facts)
vibrant, global RTD community
Industrial uptake begins (e.g.
Extension of the Web with a

BBC, Thomson Reuters, Eli Lilly,

Challenges:
1 Coherence:
2

4
5

Governmental adoption in sight
Establishing Linked Data as a
deployment path for the Semantic
Web.

Quality:

partly low quality data

and inconsistencies
3

NY Times, Facebook, Google,
Yahoo)

Relatively few,

expensively maintained links

Performance:

Still substantial

penalties compared to relational
4

Data consumption:

large-scale

processing, schema mapping and
data fusion still in its infancy
5

Usability: Missing direct end-user
tools and network eect.

These issues are closely related and
should ultimately lead to an
ecosystem of interlinked knowledge!


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Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Quality
Analysis

Evolution /
Repair

Extraction
Search/
Browsing/
Exploration



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Extraction


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Extraction

From

unstructured sources

Formats: plain text
Methods: NLP, text mining, ontology learning

From

semi-structured sources

Formats: wiki markup, tags
Tools: DBpedia framework (Wikipedia, Wictionary)

From

structured sources

Formats: databases, spreadsheets, XML
RDB2RDF tools: Sparqlify, D2R, Triplify
CSV converters: RDF extension of Google Rene



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Extraction Challenges
From

unstructured sources

Improve F-Measure of existing NLP approaches (OpenCalais, Ontos
API)
Develop standardized, LOD enabled interfaces between NLP tools
(NLP2RDF)
From

semi-structured sources

Ecient bi-directional synchronization
From

structured sources

Declarative syntax and semantics of data model transformations (W3C
WG RDB2RDF)
Orthogonal challenges
Using LOD as background knowledge
Provenance


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1234567859A8BC74DE96


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RDF Data Management
From unstructured sources
SPARQL RDF access still by a factor 2-10 slower than relational data
management
Performance increases steadily
Comprehensive, well-supported open-soure and commercial
implementations are available:
OpenLink's Virtuoso (os+commercial)
OWLIM-Lite (free), OWLIM-SE, OWLIM-Enterprise
Talis (hosted)
Bigdata (distributed)
Allegrograph (commercial)
Mulgara (os)



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Storage and Querying Challenges

Reduce the performance gap between relational and RDF data
management
SPARQL Query extensions: Spatial/semantic/temporal data
management
View maintenance / adaptive reorganization based on common access
patterns
More realistic benchmarks



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Authoring



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Authoring

Integrated in Existing Environments: Tiki
Data oriented: RDFauthor, rdfEditor
Schema oriented: Protégé, TopBraid Composer, NeOn Toolkit,
Swoop, Neologism, Knoodl



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Authoring: Semantic Wikis

1

Semantic (Text) Wikis
Authoring of semantically annotated
texts
Semantic MediaWiki, KiWi,
(Wikipedia+DBpedia)



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Authoring: Semantic Wikis

1

Semantic (Text) Wikis
Authoring of semantically annotated
texts
Semantic MediaWiki, KiWi,
(Wikipedia+DBpedia)

2

Semantic Data Wikis
Direct authoring of structured
information (i.e. RDF, RDF-Schema,
OWL)
OntoWiki



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1234235

123345647347829A2B8CDDB2EFCC22F



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Interlinking

Data Web is an uncontrolled environment proliferation of equivalent
or similar entities need for links / merging
Currently only few RDF triples are links
Manual Link Discovery:
Sindice Integration, LODStats, Semantic Pingback

Tool supported / Semi-Automatic:
SILK, LIMES, COMA, RDF-AI
Usually via mapping specications / heuristics

Machine Learning / Automatic:
RAVEN, EAGLE, SILK GP



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Interlinking Challenges

Apply work in the de-duplication/record linkage literature
Consider the open world nature of Linked Data
Use LOD background knowledge
Zero-conguration linking
Explore active learning approaches, which integrate users in a feedback
loop
Maintain a 24/7 linking service: Linked Open Data Around-The-Clock
project (http://latc-project.eu/)



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1234567829



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Enrichment

Currently, lack of knowledge bases with sophisticated schema
information and instance data adhering to this schema
Goal: powerful reasoning, consistency checking and querying
Manual:
Via ontology editors, DBpedia mappings

(Semi-)Automatic:
DL-Learner, Statistical Schema Induction



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Enrichment: Example
Given: knowledge base with property birthPlace (i.e. triples using that
property) but no information on the semantics of birthPlace
Possibly enrichment:

ObjectProperty: birthPlace
Characteristics: Functional
Domain: Person
Range: Place
SubPropertyOf: hasBeenAt
Benets:
axioms serve as documentation for purpose and correct usage of
schema elements
additional implicit information can be inferred
improve the applicability of schema debugging techniques


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Repair

Ontology Debugging:

OWL reasoning to detect inconsistencies and

satisable classes + detect the most likely sources for the problems
basic task: provide feedback to user for resolving undesired entailments
justication J

⊆O

of an entailment is a minimal set of axioms from

which the entailment can be drawn



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1234567
89347A5A


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Linked Data Quality Analysis

Quality on the Data Web is varying a lot
Hand crafted or expensively curated knowledge base (e.g. DBLP,
UMLS) vs. extracted from text or Web 2.0 sources (DBpedia)

Quality = Fitness for use
Often not necessary to x all problems, but to know about them
30+ quality dimensions dened in recent survey
Research Challenge
Establish measures for assessing the authority, provenance, reliability of
Data Web resources



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Evolution


© CC-BY-SA by alasis on flickr)

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KB Evolution
Tasks:
Performing knowledge base changes / refactoring
Ensuring consistency of related knowledge
Managing changes, e.g. undo operations
Update materialized inferred data upon changes
Update materialised links to other data upon changes
Tools:
Protégé - PROMPT and change management plugins
EvoPat - easily re-usable and sharable evolution patterns dened via
SPARQL
PatOMat - ontology transformation framework



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1234567895A



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Exploration

RDF data can be complex (as discussed by Pascal Hitzler)
Exploration phase aims to make data accessible to non-experts
Options:
Faceted Browsing
Question Answering
Query Builders
Visualisation of statistical or geospatial data
...



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Catalogus Professorum Lipsiensis



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Visual Query Builder



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Relationship Finder in CPL



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Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Quality
Analysis

Evolution /
Repair

Extraction
Search/
Browsing/
Exploration



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Make the Web a Linked Data Washing Machine



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Tool Support for Life-Cycle?
Many SW tools support one or more life-cycle stages
Linked Data Stack (http://stack.linkeddata.org) provides a
consolidated repository of such tools
Each tool is a Debian package
Lightweight integration between tools via common vocabularies and
SPARQL
Demonstrator interfaces for showing tools in combination
Developed by LOD2 and GeoKnow EU projects

Geo

Know


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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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Knowledge Extraction is the creation of knowledge from structured
(relational databases, XML) and unstructured (text, documents, images)
sources.
Resulting knowledge needs to be in a machine-readable and
machine-interpretable format and facilitate inferencing
Similar to Information Extraction (NLP) and ETL (Data Warehouse),
but main dierence: extraction result goes beyond the creation of
structured information or the transformation into a relational schema
Requires re-use of existing formal knowledge (reusing ontologies) or
the generation of a schema based on the source data



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Categorisation of Approaches
Source - Examples: plain text, relational databases, XML, CSV
Exposition - How is the extracted knowledge made explicit? How can
you query and perform inference?

Synchronization - Is the knowledge extraction process executed once
to produce a dump or is the result synchronized with the source? Are
changes to the result written back (Bi-directional)?

Reuse of Vocabularies - Can popular ontologies (Good Relations,
FOAF, . . . ) be re-used to simplify global data integration?

Automatisation - manual, semi-automatic, automatic
Domain Ontology Required - Does the approach require a
pre-dened ontology or can it create a schema from the source
(e.g. ontology learning)?



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Extraction from Structured Sources to RDF
Simple mappings from RDB tables/views to RDF
Direct mapping of the model of relational databases to RDF

→ OWL class
→ Instance s of

Table
Row

this class

→ Triple (s ,p ,o )
http://www.w3.org/TR/rdb-direct-mapping/

Cell with value o in column p
Details:

Complex mappings of relational databases to RDF

Additional renements can be employed to 1:1 mapping to improve the
usefulness of RDF output

Extract or learn an OWL schema from the given database schema
Map the schema and its contents to a pre-existing domain ontology
Powerful mapping languages: R2RML, SML

XML
XML tree structure can be directly converted to RDF graph structure
Complex mappings possible, e.g. via XSLT processors



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Extraction from Natural Language Sources
80% of the information in business documents is in unstructured
natural language

1

(-) Increased complexity and decreased quality of extraction
(+) Potential for a massive acquisition of extracted knowledge
Traditional Information Extraction (IE)
Recognize and categorise elements in text
Techniques: Named Entity Recognition (NER), Coreference Resolution
(CO), . . .

Ontology Learning (OL) from Text
Learn whole ontologies from natural language text
Usually (semi-)automatic extracted

1

Wimalasuriya, Dou. Ontology-based information extraction: [. . . ] Journal of Information Science



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LinkedGeoData + Sparqlify

Example: LinkedGeoData Knowledge Extraction Project using Sparqlify

Structure
Motivation
OpenStreetMap
LGD Architecture
Mapping
Access (How LinkedGeoData is published)
Use Cases



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Motivation

Ease

information integration tasks that require spatial knowledge,

such as
Oerings of bakeries next door
Map of distributed branches of a company
Historical sights along a bicycle track

LOD cloud contains data sets with spatial features
e.g. Geonames, DBpedia, US census, EuroStat

But:

they are

restricted to popular or large entities like countries,

famous places etc. or specic regions

Therefore

they lack


buildings, roads, mailboxes, etc.


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OpenStreetMap - Datamodel
Basic entities are:

Nodes Latitude, Longitude.
Ways Sequence of nodes.
Relations Associations between any number of nodes, ways and
relations. Every member in a relation plays a certain role.
Each entity may be described with tags (= key-value pairs)

A way is

closed if the ID of the last referenced node equals that of the

rst one.
Whether a closed way denotes a linear ring or a polygon (i.e. whether
the enclosed area is part of the respective OSM entity) depends on the
tags.



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Example: Leipzig's Zoo



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Comparison: Leipzig's Zoo (OpenStreetMap)



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Comparison: Leipzig's Zoo (GoogleMaps)



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LGD Architecture



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Tag Mappings

Key-value pairs will be assigned to
RDF ressources
Each pair

(k , v )

can be annotated with

datatypes, language tags, classes
Mappings are themselves tables
Example table:

k

lgd_map_literal

name
name:en
alt_label
note
...


property

rdfs:label
rdfs:label
skos:altLabel
rdfs:comment
...


lang
en
...

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View Denition

RDF mapping of the data from a
PostgreSQL database

Create View lgd_nodes As
Construct {
?n a lgdm:Node .
?n geom:geometry ?g .
?g ogc:asWKT ?o .
}
With
?n = uri(lgd:node, ?id)
?g = uri(lgd-geom:node, ?id)
?o = typedLiteral(?geom, ogc:wktLiteral)
From
nodes



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Sparqlify

SPARQL-SQL Rewriter
Rewrites SPARQL Queries according
to the view denition
Platform module oers SPARQL
Endpoint and Linked Data interface

https:
//github.com/AKSW/Sparqlify



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Rest-API

Oers REST methods for frequent
queries
Based on SPARQL (Virtuoso) endpoint



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Downloads

RDF dataset for download
Generated using

Construct { ?s ?p ?o }

http:
//downloads.linkedgeodata.org



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Ontology

Enriched

classes and properties with multilingual labels from

TranslateWiki

http://translatewiki.net
Imported

icons for 90 classes from the freely available icon

collection from the SJJB Management

http://www.sjjb.co.uk/mapicons/



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SML Mapping Examples

The following slides demonstrate how to map relational data to RDF
with the Sparqlication Mapping Language (SML).
Thereby, these prexes are used:

prex

rdfs
ogc
geom
lgd
lgd-geom

IRI

Prexes

http://www.w3.org/2000/01/rdf-schema#
http://www.opengis.net/ont/geosparql#
http://geovocab.org/geometry#
http://linkedgeodata.org/triplify/
http://linkedgeodata.org/geometry/



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SML - Mapping Example I: The Goal (1/4)
Input Table

id
1
2

How to map tables to RDF?

nodes

How to introduce the

geom

commonly used

POINT(0 0)
POINT(1 1)

distinction in GIS between
feature and geometry?

Aimed for RDF Output

@prefix rdfs: http://www.w3.org/2000/01/rdf-schema# .
...
lgd:node1 geom:geometry lgd-geom:node1 .
lgd-geom:node1 ogc:asWKT POINT(0 0)^^ogc:wktLiteral .



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SML - Mapping Example I: SML Syntax Outline (2/4)
Input Table

id
1
2

nodes

geom

POINT(0 0)
POINT(1 1)

Create View myNodesView As
Construct {
...
}
With
...
From
...


...



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SML - Mapping Example I: Construct and From (3/4)
Input Table

id
1
2

nodes

geom

POINT(0 0)
POINT(1 1)

Construct {
?g ogc:asWKT ?o
}
With
...
From
nodes


...


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SML - Mapping Example I: Complete! (4/4)
Input Table

id
1
2

nodes

geom

POINT(0 0)
POINT(1 1)

Construct {
?g ogc:asWKT ?o
}
With
?n = uri(lgd:node, ?id)
?g = uri(lgd-geom:node, ?id)
?o = typedLiteral(?geom,
ogc:wktLiteral)
From
nodes


...


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SML Mapping Examples

A more complex example, which demonstrates the use of an SQL
mapping table and an SQL helper view.



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SML - Mapping Example II: The Goal (1/8)
Input Table

id
1
1
1
1
1

node_tags

k

name
name:en
amenity
addr:street
addr:city

v

Universitaet Leipzig
University of Leipzig
university
Augustusplatz
Leipzig


@prefix lgd: http://linkedgeodata.org/triplify/ .
lgd:node1 rdfs:label Universitaet Leipzig .
lgd:node1 rdfs:label University of Leipzig@en .



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SML - Mapping Example II: Source Data (2/8)

OSM Table

id
1
1
1
1
1

node_tags

k

name
name:en
amenity
addr:street
addr:city

v

university
Augustusplatz
Leipzig



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SML - Mapping Example II: Mapping Table (3/8)

OSM Table

id
1
1
1
1
1

node_tags

k

name
name:en
amenity
addr:street
addr:city

RDF Mapping Table

v

university
Augustusplatz
Leipzig


k

lgd_map_literal

name
name:en
alt_label
note
...


property

rdfs:label
rdfs:label
skos:altLabel
rdfs:comment
...

lang
en
...

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SML - Mapping Example II: Helper View (4/8)
OSM Table

id
1
1
1
1
1

node_tags

k

name
name:en
amenity
addr:street
addr:city

RDF Mapping Table

v

university
Augustusplatz
Leipzig

k

lgd_map_literal

name
name:en
alt_label
note
...

property

rdfs:label
rdfs:label
skos:altLabel
rdfs:comment
...

lang
en
...

Helper View

lgd_node_tags_literal

id

property

v

lang

1
rdfs:label
1
rdfs:label
University of Leipzig en
...
...
...
...
SELECT id, property, v, lang FROM node_tags, lgd_map_literal
WHERE node_tags.k = lgd_map_literal.k


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SML - Mapping Example II: SML View (5/8)

Logical Table

id

1
1
...

SML View


property

rdfs:label
rdfs:label
...

v

Univ. L.
Univ. of L.
...


lang
en
...

Create View lgd_node_tags_text As
Construct {


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Logical Table

id

1
1
...

SML View


property

rdfs:label
rdfs:label
...

v

Univ. L.
Univ. of L.
...


lang
en
...

Construct {
?s ?p ?o .
}
With
...
From


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Logical Table

id

1
1
...

SML View


property

rdfs:label
rdfs:label
...

v

Univ. L.
Univ. of L.
...


lang
en
...

Construct {
?s ?p ?o .
}
With
?s = uri(lgd:node, ?id)
?p = uri(?property)
?o = plainLiteral(?v, ?lang)
From


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Logical Table

SML View

+

Construct {
?s ?p ?o .
}
With
?s = uri(lgd:node, ?id)
?p = uri(?property)
?o = plainLiteral(?v, ?lang)
From

id

1
1
...


property

rdfs:label
rdfs:label
...

v

Univ. L.
Univ. of L.
...

lang
en
...

Resulting RDF

@prefix lgd: http://linkedgeodata.org/triplify/ .
lgd:node1 rdfs:label Universitaet Leipzig .
lgd:node1 rdfs:label University of Leipzig@en .



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Further Tag Mappings
lgd_map_dataype

k

seats
unisex

k

datatype
integer
boolean

lgd_map_property

website

property

foaf:homepage

lgd_map_resource_k

k

highway

property
rdf:type

lgd_map_resource_kv

k

waterway

v

river

object

lgdo:HighwayThing

property
rdf:type


object

lgdo:River


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LGD Edit Tool
Multi User Tag Mapping WebApp



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Resources

Sparqlify
http://sparqlify.org

LinkedGeoData
http://linkedgeodata.org

Tag Mappings
https://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/
org/aksw/linkedgeodata/sql/Mappings.sql

SML View Denitions
https://github.com/GeoKnow/LinkedGeoData/blob/master/linkedgeodata-core/src/main/resources/
org/aksw/linkedgeodata/sml/LinkedGeoData-Triplify-IndividualViews.sml



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Statistics (15 August 2013)

Complete OSM planet le corresponds to
Virtual access via Sparqlify

∼

20.000.000.000 triples

Downloads limited to selected classes.
292.780.188 Triples

153.613.243 triples of Nodes
139.166.945 triples of Ways
Relations not yet available for download
Among them

532.812 PlaceOfWorship
82.788 RailwayStation
72.091 Toilets
71.613 Town
19.937 City



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Access
Materialized Sparql Endpoint (based on Virtuoso DB, download
datasets loaded)

http://linkedgeodata.org/sparql
http://linkedgeodata.org/snorql

Virtual Sparql Endpoint (based on Sparqlify, access to 20B triples,
limited SPARQL 1.0 support)

http://linkedgeodata.org/vsparql
http://linkedgeodata.org/vsnorql

Rest Interface (based on the Virtual Sparql Endpoint)
Supports limited queries (e.g. circular/rectangular area, ltering by
labels)

Downloads

http://downloads.linkedgeodata.org
Monthly updates on the above datasets envisioned



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Use Cases Augmented Reality



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Use Cases Generic Browsing



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Use Cases Generic Browsing



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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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Why Link Discovery?
1

Fourth Linked Data
principle

2

Links are central for
Cross-ontology QA
Data Integration
Reasoning
Federated Queries
...

3

2011 topology of the
LOD Cloud:
31+ billion triples

≈ 0.5 billion links
owl:sameAs in most
cases



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Why is it dicult?
1

Time complexity
Large number of triples
Quadratic a-priori runtime
69 days for mapping cities from
DBpedia to Geonames (1ms per
comparison)
decades for linking DBpedia and LGD
...

Denition (Link Discovery)
Given sets S and T of resources and relation
Task: Find M

= {(s , t ) ∈ S × T : R(s , t )}

R

Common approaches:
Find M
Find M

= {(s , t ) ∈ S × T : σ(s , t ) ≥ θ}
= {(s , t ) ∈ S × T : δ(s , t ) ≤ θ}



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Why is it dicult?
2

Complexity of specications
Combination of several attributes required for high precision
Tedious discovery of most adequate mapping
Dataset-dependent similarity functions



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LIMES Framework



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Runtime Optimization
Reduce the number of comparisons C (A)
all

σ /θ

values for links)

≥ |M |

(assuming we need

Maximize reduction ratio:
RR (A)


=1−

C (A)

|S ||T |


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Runtime Optimization
Reduce the number of comparisons C (A)
all

σ /θ

values for links)

≥ |M |

(assuming we need

Maximize reduction ratio:
RR (A)

=1−

C (A)

|S ||T |

Question
Can we devise lossless approaches with guaranteed RR?
Advantages
Space management
Runtime prediction
Resource scheduling



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RR Guarantee

Best achievable reduction ratio: RRmax


=1−


|M |
|S ||T |

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RR Guarantee

Approach

H(α)

=1−

|M |
|S ||T |

fullls RR guarantee criterion, i:

∀r RRmax , ∃α : RR (H(α)) ≥ r



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RR Guarantee

Approach

H(α)

=1−

|M |
|S ||T |

fullls RR guarantee criterion, i:

∀r RRmax , ∃α : RR (H(α)) ≥ r
Here, we use relative reduction ratio (RRR ):

RRR (A)


=

RRmax
RR (A)


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Goal

Formal Goal
Devise

H(α) : ∀r 1, ∃α : RRR (H(α)) ≤ r



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Restrictions
Minkowski Distance
δ(s , t ) = p

n

1

i=


|si − ti |p , p ≥ 2


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Space Tiling
HYPPO
δ(s , t ) ≤ θ

describes a hypersphere

Approximate hypersphere by using a hypercube
Easy to compute
No loss of recall (blocking)



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Space Tiling
Set width of single hypercube to


∆ = θ/α


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Space Tiling
Set width of single hypercube to
Tile

Ω=S ∪T

(c1 , . . . , c ) ∈ N
points ω ∈ Ω : ∀i ∈ {1 . . . n}, c ∆ ≤ ω (c + 1)∆

Coordinates:
Contains

∆ = θ/α

into the adjacent cubes C


n

n

i


i

i

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HYPPO
Combine

(2α + 1)n

hypercubes around C (ω) to approximate

hypersphere

RRR (HYPPO (α))

n
2
= (αα+(1))
nS n

lim RRR (HYPPO (α))

α→∞


n
= S2 n)
(


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HYPPO
RRR(HYPPO) for p


= 2,

n

= 2, 3, 4

and 2

≤ α ≤ 50


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HYPPO
RRR(HYPPO) for p

= 2,


α→∞


α→∞


α→∞


n

= 2, 3, 4

and 2

≤ α ≤ 50

4
= π ≈ 1.27 (n = 2)
6
= π ≈ 1.91 (n = 3)

32
= π2 ≈ 3.24 (n = 4)


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HR3 : Idea
index (C , ω)

=


0

if

n



i=


∃i : |ci − c (ω)i | ≤ 1, 1 ≤ i ≤ n,

(|ci − c (ω)i | − 1)p

1


else,

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HR3 : Idea

Compare C (ω) with C i index (C , ω)

α = 4, p = 2


≤ αp


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HR3 : Idea

Lemma
∀s ∈ S : index (C , s ) αp

implies that all t

∈C

are non-matches

Claims
No loss of recall

3 (α))

lim RRR (HR

α→∞


=1


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HR3 : Lemma 3
Lemma
∀α 1
p

3 (2α))

RRR (HR

RRR (HR3 (α))

= 2, α = 4



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HR3 : Proof
Lemma
∀α 1
p

RRR (HR

3 (2α))

RRR (HR 3 (α))

= 2, α = 8



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HR3 : Proof
Lemma
∀α 1
p

RRR (HR

3 (2α))

RRR (HR 3 (α))

= 2, α = 25



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HR3 : Proof
Lemma
∀α 1
p

RRR (HR

3 (2α))

RRR (HR 3 (α))

= 2, α = 50



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HR3 : Idea

Theorem
3 (α))

lim RRR (HR

α→∞

=1

Claims
No loss of recall

3 (α))

lim RRR (HR

α→∞


=1


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HR3 : Experiments

Compare

HR3

with LIMES 0.5's HYPPO and SILK 2.5.1

Experimental Setup:
Deduplicating DBpedia places by minimum elevation, elevation and
maximum elevation (θ

= 49m, 99m).

Geonames and LinkedGeoData by longitude and latitude (θ

= 1◦ , 9◦ )

64-bit computer with a 2.8GHz i7 processor with 8GB RAM.



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HR3 : Experiments (Comparisons)
Experiment 2: Deduplicating DBpedia places,

6
0.64 × 10

θ = 99m

less comparisons



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HR3 : Experiments (Comparisons)
Experiment 4: Linking Geonames and LinkedGeoData,
4.3

× 106

θ = 9◦

less comparisons



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HR3 : Experiments (Runtime)
θ = 49, 99m
◦
Geonames and LGD, θ = 1, 9

Experiment 1, 2: DBpedia,
Experiment 3, 4:
10

Runtime (s)

10

10

10

10

4

3

HR3
HYPPO
SILK

2

1

0

Exp. 1


Exp. 2

Exp. 3


Exp. 4

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HR3 : Summary

Mission
New category of algorithms for link discovery



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HR3 : Summary

Mission
New category of algorithms for link discovery

Presented

HR3

Link discovery in ane spaces with Minkowski measures
Outperforms the state of the art (runtime, comparisons)
Optimal reduction ratio
Integrated in LIMES



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Learning Complex Specications

Supervised (mostly active, e.g., RAVEN, EAGLE, SILK)
Unsupervised (e.g., KnoFuss, EUCLID, EAGLE)



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Supervised (mostly active, e.g., RAVEN, EAGLE, SILK)
Unsupervised (e.g., KnoFuss, EUCLID, EAGLE)



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Insight
Choice of right example is key for learning
So far, only use of informativeness



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Insight
Question
Can we do better by using more information?



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Insight
Question
Can we do better by using more information?

Higher F-measure
Often slower



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Basic Idea
Use similarity of link candidates when selecting most informative
examples (intra + inter class similarity)



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Similarity of Candidates

= (s , t ) can
(σ1 (x ), . . . , σn (x )) ∈ [0, 1]n .
Link candidate x

be regarded as vector

Similarity of link candidates x and y :
sim (x , y )

1

=
n

1

+
i=

.

(1)

(σi (x ) − σi (y ))2

1

Allows exploiting both intra- and inter-class similarity



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Graph Clustering
Rationale:
Approach

Use intra-class similarity

Cluster elements of S

+

and S

−

independently

Choose one element per cluster as representative
Present oracle with most informative representatives

e

S+
0.9

a

0.25

0.8

c

0.8
0.8

b

h
0.8

f

d

0.9

0.25

l

0.8

i

0.9
0.8

0.8

g

k
0.25


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BorderFlow
G

= (V , E , ω)

with V

= S+

or V

= S−

ω(x , y ) = sim(x , y )
Keep best ec edges for each x


∈V


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BorderFlow
Seed-based algorithm
Goal: Maximize borderow ratio bf (X )


=

Ω(b (X ),X )
Ω(b (X ),n(X ))


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BorderFlow

=

Ω(b (X ),X )
Ω(b (X ),n(X ))

http://sourceforge.net/projects/cugar-framework/


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BorderFlow


=

Ω(b (X ),X )
Ω(b (X ),n(X ))


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BorderFlow

=

Ω(b (X ),X )
Ω(b (X ),n(X ))

http://sourceforge.net/projects/cugar-framework/


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Conclusion

Can be combined with arbitrary active learning ML algorithms
Was experimentally combined with EAGLE (genetic programming) and
RAVEN (linear classier) and shown to outperform the plain
informativeness function in terms of F-measure
Choice of example important to minimise user eort
Contact me for detailed experimental results
Longer runtimes (up to 2×)



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Summary
Linking crucial task in the web of data
Tow key problems
1

Ecient execution of link specications

2

Creation of link specication

Presented HR3 to handle the rst problem
Presented COALA as building block for the second problem



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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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Motivation

rise in the availability and usage of knowledge bases
still a lack of knowledge bases that consist of sophisticated schema
information and instance data adhering to this schema
e.g. in the life sciences several knowledge bases
only consist of schema information
to a large extent, a collection of facts without a clear structure
(e.g. information extracted from databases)

combination of sophisticated schema and instance data would allow
powerful reasoning, consistency checking, and improved querying

→

create schemata based on existing data



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Example
dbr : Brad_Pitt

: birthPlace
a

dbr : Angela_Merkel

: birthPlace
a

: birthPlace
a

d b r : Shawnee , _Oklahoma
a

Suggestions:

a

a

: Place .

: Place .

birthPlace

ObjectProperty :

birthPlace

Characteristics :
Range :

d b r : Ulm ;

: Person .

: Place .

d b r : Hamburg

Domain :

d b r : Hamburg ;

: Person .

dbr : A l b e r t _ E i n s t e i n

d b r : Ulm

d b r : Shawnee , _Oklahoma ;

: Person .

Functional

Person
Place

SubPropertyOf :

hasBeenAt



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Benets of an expressive schema

Axioms serve as documentation for the purpose and correct usage of
schema elements
Additional implicit information can be inferred
Improve querying optimisations
Improve/allow the application of schema debugging techniques



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Each person was only born at one place?!



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=
birthPlace

birthPlace

birthPlace is functional

=
birthPlace

birthPlace

SELECT ? s WHERE {
? s dbo : b i r t h P l a c e ? o1 .
? s dbo : b i r t h P l a c e ? o2 .
FILTER ( ? o1 != ? o2 ) }
}
birthPlace is functional



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Where was Julia Nannie Wallace born?



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Julia Nannie Wallace was born in Lacrosse?



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No, Julia Nannie Wallace was born in La Crosse!



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rdf:type

birthPlace

birthPlace range Place

Sport

rdf:type

Sport

birthPlace

rdf:type


Place

rdf:type

Sport

=

birthPlace

rdf:type


Place disjointWith Sport

Place

rdf:type

City

birthPlace

rdf:type



Place

rdf:type

City

birthPlace

rdf:type

Place

SELECT ? s ? p l a c e WHERE {
? s dbo : b i r t h P l a c e ? p l a c e .
? place r d f : type / r d f s : subClassOf ∗ ? type1 .
? t y p e 2 r d f s : s u b C l a birthPlace :range Place
s s O f ∗ dbo P l a c e .
? t y p e 1 owl : d i s j o i n t W i t h ? t y p e 2 .
}



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3 Steps to get a schema

3-Phase Enrichment
Learning Approach:

SPARQL
Endpoint

Input: Entity URI,
Axiom Type,
Knowledge Base
(SPARQL Endpoint)



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3-Phase Enrichment
Learning Approach:
(only executed once
per knowledge base)

SPARQL
Endpoint

Input: Entity URI,
1. obtain schema
Axiom Type,
information
Knowledge Base
(SPARQL Endpoint)


Background
Knowledge


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3-Phase Enrichment
Learning Approach:

Input: Entity URI,
1. obtain schema
Axiom Type,
information
Knowledge Base
(SPARQL Endpoint)


(sample data
if necessary)

Reasoner
(optional
invocation)

(only executed once
per knowledge base)

SPARQL
Endpoint

Background
Knowledge

2. obtain axiom type
and entity specific data

Background
Knowledge
+ Relevant
Instance Data


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3-Phase Enrichment
Learning Approach:

Input: Entity URI,
1. obtain schema
Axiom Type,
information
Knowledge Base
(SPARQL Endpoint)


(sample data
if necessary)

Reasoner

Learner

DL-Learner

Enrichment
Ontology

(optional
invocation)

(only executed once
per knowledge base)

SPARQL
Endpoint

Background
Knowledge


Background
3. run machine learning
Knowledge
algorithm
+ Relevant
Instance Data


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List of Axiom
Suggestions
+ Metadata

185 / 252


3-Phase Enrichment
Learning Approach:

Input: Entity URI,
1. obtain schema
Axiom Type,
information
Knowledge Base
(SPARQL Endpoint)


(sample data
if necessary)

Reasoner

Learner

DL-Learner

Enrichment
Ontology

(optional
invocation)

(only executed once
per knowledge base)

iterate over all axiom types
and schema entities for full
enrichment

SPARQL
Endpoint

Background
Knowledge


Background
3. run machine learning
Knowledge
algorithm
+ Relevant
Instance Data


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List of Axiom
Suggestions
+ Metadata

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Starting Point

http://dbpedia.org/sparql
http://dbpedia.org/ontology/author

SPARQL endpoint:
Entity URI:

Axiom Type: Object Property Domain



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Step 1 - Obtaining Schema Information



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CONSTRUCT WHERE {
? sub r d f s : s u b C l a s s O f ? sup .
}
ORDER BY DESC( ? sub ) LIMIT 1000 OFFSET 1000



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CONSTRUCT WHERE {
? sub r d f s : s u b C l a s s O f ? sup .
}
ORDER BY DESC( ? sub ) LIMIT 1000 OFFSET 1000

dbo : D i s e a s e
dbo : Book
dbo : WrittenWork
dbo : Work
dbo : P h i l o s o p h e r
dbo : P e r s o n
dbo : Agent
dbo : S p o r t
dbo : A c t i v i t y
dbo : F i s h

rdfs
rdfs
rdfs
rdfs
rdfs
rdfs
rdfs
rdfs
rdfs
rdfs


: subClassOf
: subClassOf
: subClassOf
: subClassOf
: subClassOf
: subClassOf
: subClassOf
: subClassOf
: subClassOf
: subClassOf

owl : Thing .
dbo : WrittenWork .
dbo : Work .
owl : Thing .
dbo : P e r s o n .
dbo : Agent .
owl : Thing .
dbo : A c t i v i t y .
owl : Thing .
dbo : Animal .


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Step 2 - Obtain axiom type and entity specic data



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SELECT ? t y p e (COUNT( DISTINCT ? s ) AS ? c n t ) WHERE {
? s dbo : a u t h o r ? o .
? s a ? type .
} GROUP BY ? t y p e ORDER BY DESC( ? c n t )



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SELECT ? t y p e (COUNT( DISTINCT ? s ) AS ? c n t ) WHERE {
? s dbo : a u t h o r ? o .
? s a ? type .
} GROUP BY ? t y p e ORDER BY DESC( ? c n t )
type

cnt

owl:Thing

30284

dbo:Work

30284

schema:CreativeWork

30284

dbo:WrittenWork

25730

dbo:Book

24673

schema:Book

24673

dbo:TelevisionShow

2567

dbo:Play

1057

.
.
.


.
.
.


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CONSTRUCT WHERE {
? i n d dbo : a u t h o r ? o .
? ind a ? type .
}
ORDER BY DESC( ? i n d ) LIMIT 1000 OFFSET 2000



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CONSTRUCT WHERE {
? i n d dbo : a u t h o r ? o .
? ind a ? type .
}
ORDER BY DESC( ? i n d ) LIMIT 1000 OFFSET 2000

.
.
.
d b p e d i a : The_Adventures_of_Tom_Sawyer
dbo : a u t h o r
d b p e d i a : Mark_Twain ;
rdf : type
dbo : Book .
d b p e d i a : The_Zombie_Survival_Guide
dbo : a u t h o r
d b p e d i a : Max_Brooks ;
rdf : type
dbo : WrittenWork .
d b p e d i a : Web_Therapy
dbo : a u t h o r
d b p e d i a : Lisa_Kudrow ;
rdf : type
dbo : Book .
.
.
.



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Step 3 - Scoring
dbo : a u t h o r
rdf : type
dbo : Book .
dbo : a u t h o r
rdf : type
dbo : WrittenWork .
dbo : a u t h o r
rdf : type
dbo : Book .



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Step 3 - Scoring
dbo : a u t h o r
rdf : type
dbo : Book .
dbo : a u t h o r
rdf : type
dbo : WrittenWork .
dbo : a u t h o r
rdf : type
dbo : Book .
Score(Domain(dbo:author, dbo:Book))=

2
3

≈ 66.7%

Score(Domain(dbo:author, dbo:WrittenWork))=



1
3

≈ 33.3%

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Step 3 - Scoring
dbo : a u t h o r
rdf : type
dbo : Book .
dbo : a u t h o r
rdf : type
dbo : WrittenWork .
dbo : a u t h o r
rdf : type
dbo : Book .

2
3

≈ 66.7%


dbo : Book

1
3

≈ 33.3%

r d f s : s u b C l a s s O f dbo : WrittenWork .



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Step 3 - Scoring
dbo : a u t h o r
rdf : type
dbo : Book .
dbo : a u t h o r
rdf : type
dbo : WrittenWork .
dbo : a u t h o r
rdf : type
dbo : Book .

2
3

≈ 66.7%


dbo : Book

1
3

≈ 33.3%

r d f s : s u b C l a s s O f dbo : WrittenWork .




3
3

= 100%
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Step 3 - Scoring(2)
Problem:
support for axiom in KB not taken into account

→

no dierence between 3 out of 3 and 100 out of 100



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Step 3 - Scoring(2)
Problem:

→


Solution:
Average of 95% condence interval (Wald method)

s
p = m+2
+4
min(1, p + 1.96 ·

p ·(1−p ) ) max(0, p − 1.96 ·
m +4

− #success
m − #total
s

p ·(1−p ) )
m +4

In 95% of the intervals the true value is between ... and ...



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Step 3 - Scoring(2)
Problem:

→


Solution:
Average of 95% condence interval (Wald method)

s
p = m+2
+4
min(1, p + 1.96 ·

p ·(1−p ) ) max(0, p − 1.96 ·
m +4

− #success
m − #total
s

p ·(1−p ) )
m +4

In 95% of the intervals the true value is between ... and ...
Score(Domain(dbo:author, dbo:Book))≈ 57.3%

Score(Domain(dbo:author, dbo:WrittenWork))≈ 69.1%



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More Complex Axioms

Pattern Based Knowledge Base Enrichment, ISWC 2013


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Outlook and Summary

Schema in the Linked Data Web often shallow
support knowledge engineers

→

tools needed to

Showed some techniques for learning OWL axioms on large knowledge
bases available as SPARQL endpoints
More complex aioms require:
OWL-SPARQL rewriting or
Fragment extraction

Small- and medium sized knowledge bases can be handled via
techniques from Inductive Logic Programming
All algorithms implemented in DL-Learner framework
(http://dl-learner.org)



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Outline
1


2


3


4


5


Interlinking
/ Fusing
Manual
revision/
Authoring

Classification/
Enrichment

Linked Data
Lifecycle

Storage/
Querying

Evolution /
Repair

Extraction

6

Enrichment

7

Repair

8

Quality
Analysis



Search/
Browsing/
Exploration


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Motivation

increasing number of knowledge bases in the
Semantic Web (see e.g. LOD cloud)
maintenance of knowledge bases with
expressive semantics is challenging



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(Automatically) Detectable Ontology Problems

Common problems:
Syntactic Problems
Structural Problems
Semantic Problems (focus of talk)
Task Based Problems:
Reasoning Related Problems
Linked Data Related Problems



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Syntactic Problems
Syntactic errors are mainly violations of conventions of the language in
which the ontology is modelled.

Example (Validity of XML)
? x m l

v e r s i o n = 1 . 0 ?

r d f : R D F

x m l n s : r d f = h t t p : / /www . w3 . o r g /1999/02/22 − r d f −

s y n t a x −n s#

x m l n s : d c= h t t p : / / p u r l . o r g / d c / e l e m e n t s

/ 1 . 1 /

r d f : D e s c r i p t i o n

r d f : a b o u t = h t t p : / /www . w3 . o r g /

d c : t i t l e W o r l d

Wide Web

C o n s o r t i u m/ d c : t i t l e

/ r d f : R D F

FatalError: The element type rdf:Description must be terminated by the
matching end-tag /rdf:Description.[Line = 7, Column = 3]



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Structural Problems

Problems in the taxonomy

Example (Circularities)
A


B, B

C, C

A


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Reasoning Related Problems
Problems which negatively aect the performance of reasoning over
expressive knowledge bases

Example (A named concept is equivalent to an AllValues restriction)
A

≡ ∀r .C

Reasoning complexity:
Universal restriction does not require to have a property value but only
restricts the values for existing property values
Any concept B for which instances cannot have r -llers satises the
restriction, i.e. B

∀r .C ,

and becomes a subclass of A

Typically leads to unintended inferences and additional inferences may
eventually slow down reasoning performance
Can be checked via Pellint (part of Pellet)



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Linked Data Related Problems

Problems which are the specic to publishing RDF using the Linked Data
principles
Incorrect implementation of content negotiation
Mixing up information and non-information resources



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Semantic Problems

Logical contradictions in the underlying knowledge base

Example (Unsatisable classes)
O = {A

B

C, C

¬B } |= A

⊥

Example (Inconsistent ontology)
O = {A

B

C, C

¬B , A(x )} |=

⊥

Usually handled by Ontology Debugging



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Ontology Debugging
Problem: We have undesirable entailments



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Ontology Debugging
Solution:

Repair (Delete/Modify) responsible axioms



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Ontology Debugging
Solution:

Repair (Delete/Modify) responsible axioms

Question: Which axioms?



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Justication
Justication
For an ontology

O and an entailment η where O |= η , a set of axioms J
η in O if J ⊆ O, J |= η and if J ⊂ J then J |= η .

is

a justication for

Minimal subsets of an ontology that are sucient for a given
entailment to hold
Synonyms: MUPS (Minimal Unsatisability Preserving Sub-TBoxes),
MinAs (Minimal Axiom sets), Kernels
Observations:
there can be multiple justications for a single entailment
an axiom can be part of multiple justications



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Justication - Example

O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)
(4)

E

A

C

(2)

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)
(4)

E

A

C

(2)

|= A

⊥

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)
(4)

E

A

C

J1 = {1, 2, 3}

(2)

|= A

⊥

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)
(4)

E

A

C

J1 = {1, 2, 3}

(2)

|= A

⊥

J2 = {5, 6}

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)
(4)

E

A

C

(5)
F

J1 = {1, 2, 3}

(2)

(6)

|= A

⊥

J2 = {5, 6}
J3 = {3, 4}

}



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Justication Based Repair

For a repair, at least one axiom from every justication needs to be
removed.
For a repair plan, all justications are needed.



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Justication Algorithms

Single justication:
Glass Box: Modifying underlying reasoning algorithm (tableau tracing)
Black-Box: Using reasoner as oracle
All justications:
Reiter's Hitting Set Tree Algorithm (HST)



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Black-Box

Expansion-Contraction Strategy
Expansion: Add axioms to empty set until entailment holds
Contraction: Remove axioms from set such that set becomes minimal
CHAPTER 3. COMPUTING JUSTIFICATIONS
54
and entailment still can be derived.

Expansion

Contraction

Key:
Axiom
Axiom in justiﬁcation
Selected axiom

Figure 3.1: A Depiction of a Black-Box Expand-Contract Strategy
Source: M. Horridge:Justication

3.2

Based Explanation
Black-Box Algorithms for Computing Sin- in Ontologies(PhD
Thesis)



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Hitting Set Tree Algorithm

from eld of Model Based Diagnosis
given a faulty system (ontology), it constructs nite tree whose
nodes are labelled with conict sets (justications), and whose
edges are labelled with components (axioms)

nds all minimal hitting sets, which represent diagnoses for the
conict sets in the system
diagnosis = repair



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CHAPTER 3. COMPUTING JUSTIFICATIONS

63

Hitting Set Tree Algorithm - Example
O = {A

B

Figure 3.2: An Example of a Hitting Set Tree

B

D

A

∃R .C

∃R .

J2 = {A

D}

J1 = {A

|= A

∃R.C, ∃R.
A

∃R.C

{}

B, B

D}

D

A

B

B

D

J2 = {A

D}

∃R.

{}

D

A

∃R.C

{}

∃R.

∃R.C, ∃R.

D}

D

{}
Source: M. Horridge:Justication
Based Explanation in Ontologies(PhD

bottom right hand successor to the node labelled with J2 and whose successor
Thesis)
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edge is labelled with ∃R. The Linked Data Life-Cycle by considering O S where
D was generated

Justication Scenarios

A user can be faced with the following situations:
Small number of small justications
Easy and pleasant to inspect

Small number of large justications
Better than alternatives

Large number of justications
Pretty hopeless with current mechanisms
Idea: Find source of unsatisability



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Root Unsatisability - Denitions

A root UC is a class whose unsatisability does not depend on another
class, otherwise it is a derived UC.
A derived UC for which there is some justication that is not a strict
superset of a justication for another UC is a partial derived UC.

Root Unsatisable Class
A class A is a root unsatisable class if there is no justication
such that

J

J |= A

is a strict superset of a justication for some other

⊥

unsatisable class.



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Root Unsatisability - Approaches

Approaches:
1: compute all justications for each unsatisable class and apply the
denition

→

computationally often too expensive

2: heuristics for structural analysis of axioms

Debugging Unsatisable Classes in OWL Ontologies, Kalyanpur, Parsia, Sirin, Hendler,
J. Web Sem, 2005.



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Root Unsatisability - Example

O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)
(4)

E

A

C

(2)

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

|= A

⊥

(3)
(4)

E

A

C

(2)

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

(3)

|= A

⊥

J2 = {5, 6}
J3 = {3, 4}

(4)

E

A

C

(2)

J1 = {1, 2, 3}

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

|= A

⊥

|= B

J2 = {5, 6}

⊥

(3)

J3 = {3, 4}

(4)

E

A

C

(2)

J1 = {1, 2, 3}

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

|= A

⊥

|= B

⊥

(3)

J2 = {5, 6}
J3 = {3, 4}

(4)

E

A

C

(2)

J1 = {1, 2, 3}

(5)
F

(6)

J4 = {1, 2}

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

|= A

⊥

|= B

⊥

(3)

J2 = {5, 6}
J3 = {3, 4}

(4)

E

A

C

(2)

J1 = {1, 2, 3}

(5)
F

(6)

J4 = {1, 2}

root

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

|= A

⊥

|= B

⊥

(3)

J2 = {5, 6}

partial

J4 = {1, 2}

root

J3 = {3, 4}

(4)

E

A

C

(2)

J1 = {1, 2, 3}

(5)
F

(6)

}



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O={
B
B

∃r .D

(1)

∀r .¬D

A

B

B

¬C

A

¬E

|= A

⊥

|= B

⊥

(3)

J2 = {5, 6}
J3 = {3, 4}

partial
(J4

⊂ J1 )

(4)

E

A

C

(2)

J1 = {1, 2, 3}

(5)
F

(6)

J4 = {1, 2}

root

}



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Axiom Relevance

resolving justication requires to delete or edit axioms
ranking methods highlight the most probable causes for problems
methods:
frequency
syntactic relevance
semantic relevance



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Repair Consequences

after repairing process, axioms have been deleted or modied

→

desired entailments may be lost or new entailments obtained

→

user can decide to preserve them

(including inconsistencies!)



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SPARQL Endpoint Support

Previously mentioned approaches are implemented in the ORE tool
(http://ore-tool.net)
ORE supports using SPARQL endpoints
implements an incremental load procedure
knowledge base is loaded in small chunks:
count number of axioms by type
priority based loading procedure

e.g. disjointness axioms have higher priority than class assertion axioms

uses Pellet incremental reasoning

Learning of OWL Class Descriptions on Very Large Knowledge Bases,
Hellmann, Lehmann, Auer, Int. Journal Semantic Web Inf. Syst, 2009



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SPARQL Endpoint Support II

algorithm performs sanity checks, e.g. SPARQL queries which probe
for typical inconsistent axiom sets
can fetch additional Linked Data
dierent termination criteria



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SPARQL Endpoint Support II

algorithm performs sanity checks, e.g. SPARQL queries which probe
for typical inconsistent axiom sets
can fetch additional Linked Data
dierent termination criteria
overall:
ORE allows to apply state-of-the-art ontology debugging methods on a
larger scale than was possible previously

aims at stronger support for the web aspect of the Semantic Web
and the high popularity of Web of Data initiative



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DBpedia Live Demo
Inconsistency in DBpedia Live:

Individual: dbr:Purify_(album)
Facts: dbo:artist dbr:Axis_of_Advance
Individual: dbr:Axis_of_Advance
Types: dbo:Organisation
Class: dbo:Organisation
DisjointWith dbo:Person
ObjectProperty: dbo:artist
Range: dbo:Person



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The Linked Data Lifecycle

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