Interlinking Data and Knowledge in Enterprises, Research and Society with Linked Data

Introduction Motivation Evolution Knowledge URIs Lifecycle Interfaces Discovery Conclusion
Interlinking Data and Knowledge
in Enterprises, Research and Society
with Linked Data
Baltic DB & IS 2014
http://eis.iai.uni-bonn.de
Christoph Lange1,2 and Sören Auer1,2
1Enterprise Information Systems, University of Bonn, Germany
2Organized Knowledge, Fraunhofer IAIS, Sankt Augustin, Germany
2014-06-09
Lange/Auer (Bonn) Interlinking Data and Knowledge in Enterprises, Research and Society with Linked Data 2014-06-09 1

„Tere, maailm!”
2011 PhD at Jacobs Univ. Bremen, Germany: Enabling
Collaboration on Semiformal Mathematical
Knowledge by Semantic Web Integration [Lan11]
2011/12 Univ. Bremen, Germany: Ontology Integration
and Interoperability (OntoIOp) ↝ Distributed
Ontology Language (DOL)
2012/13 Univ. Birmingham, UK: Formal Mathematical
Reasoning in Economics (ForMaRE) [KLR]
2013– Univ. Bonn, Germany: Enterprise Information
Systems; Fraunhofer IAIS: Organized Knowledge

EIS/OK Group in Bonn
Prof. Sören Auer
previously at the University of Leipzig, AKSW group
(DBpedia etc.)
Christoph Lange: 1 of 3 postdocs
∼ 15 members of scientific staff
6 PhD students
Business areas:
Enterprise Information Integration
Digital Libraries (cultural heritage and other
applications)
Personalised medicine

Data in Today’s Society
Example (Demographics in Bonn)
Statistics, e.g. from municipal office for integration
Housing (accessibility, availability, . . . ):
municipal, commercial, self-organised
Transport (e.g. accessibility): bus/tram
Infrastructure: e.g. accessible public toilets
Complex questions:
Apartments that meet my requirements w.r.t.
public transport, accessibility, care, co-residents
What bus takes me from A to B, with sufficient
changing time near an accessible toilet?

Example: Accessible Facilities
Collected by the Bonn Disableds’ Union. Now combine
with public transport, housing offers, . . . !

Data in Science
Example (Quality of Scientific Workshops)
Indicators for the quality of a workshop:
part of a high-profile conference
long history
many editions
continuity in chairs
number of papers not shrinking
contributions from many institutions and countries

Data in Science: Datasets
Sources of complementary information:
DBLP computer science publications (basics), author
name disambiguation
CEUR-WS.org computer science workshops
→ ESWC 2014 Semantic Publishing Challenge
Springer computer science conferences
WikiCFP calls for papers

Data Sources of Interest
(Open) Government Data
general Open Data: Wikipedia, OpenStreetMap, . . .
private data
personal data
How can they be . . .
1
published (licenses, privacy),
2
described (for machines to understand),
3
discovered,
4
integrated,
5
analysed?

Linked (Open) Data: Principles
http://5stardata.info
☀ make your stuff available on the Web
(whatever format) under an open li-
cense
☀☀ make it available as structured data
(e.g., Excel instead of image scan of a
table)
☀☀☀ use non-proprietary formats (e.g., CSV
instead of Excel)
☀☀☀☀ use URIs to denote things, so that
people can point at your stuff
☀☀☀☀☀ linkyourdatatootherdatatoprovide
context [12]
☀☀☀☀☀☀ further stars proposed for: quality
[DLA14], explicit schema [Hyv+]

Linked (Open) Data: Datasets
As of September 2011
Music
Brainz
(zitgist)
P20
Turismo
de
Zaragoza
yovisto
Yahoo!
Geo
Planet
YAGO
World
Fact-
book
El
Viajero
Tourism
WordNet
(W3C)
WordNet
(VUA)
VIVO UF
VIVO
Indiana
VIVO
Cornell
VIAF
URI
Burner
Sussex
Reading
Lists
Plymouth
Reading
Lists
UniRef
UniProt
UMBEL
UK Post-
codes
legislation
data.gov.uk
Uberblic
UB
Mann-
heim
TWC LOGD
Twarql
transport
data.gov.
uk
Traffic
Scotland
theses.
fr
Thesau-
rus W
totl.net
Tele-
graphis
TCM
Gene
DIT
Taxon
Concept
Open
Library
(Talis)
tags2con
delicious
t4gm
info
Swedish
Open
Cultural
Heritage
Surge
Radio
Sudoc
STW
RAMEAU
SH
statistics
data.gov.
uk
St.
Andrews
Resource
Lists
ECS
South-
ampton
EPrints
SSW
Thesaur
us
Smart
Link
Slideshare
2RDF
semantic
web.org
Semantic
Tweet
Semantic
XBRL
SW
Dog
Food
Source Code
Ecosystem
Linked Data
US SEC
(rdfabout)
Sears
Scotland
Geo-
graphy
Scotland
Pupils &
Exams
Scholaro-
meter
WordNet
(RKB
Explorer)
Wiki
UN/
LOCODE
Ulm
ECS
(RKB
Explorer)
Roma
RISKS
RESEX
RAE2001
Pisa
OS
OAI
NSF
New-
castle
LAAS
KISTI
JISC
IRIT
IEEE
IBM
Eurécom
ERA
ePrints dotAC
DEPLOY
DBLP
(RKB
Explorer)
Crime
Reports
UK
Course-
ware
CORDIS
(RKB
Explorer)
CiteSeer
Budapest
ACM
riese
Revyu
research
data.gov.
ukRen.
Energy
Genera-
tors
reference
data.gov.
uk
Recht-
spraak.
nl
RDF
ohloh
Last.FM
(rdfize)
RDF
Book
Mashup
Rådata
nå!
PSH
Product
Types
Ontology
Product
DB
PBAC
Poké-
pédia
patents
data.go
v.uk
Ox
Points
Ord-
nance
Survey
Openly
Local
Open
Library
Open
Cyc
Open
Corpo-
rates
Open
Calais
OpenEI
Open
Election
Data
Project
Open
Data
Thesau-
rus
Ontos
News
Portal
OGOLOD
Janus
AMP
Ocean
Drilling
Codices
New
York
Times
NVD
ntnusc
NTU
Resource
Lists
Norwe-
gian
MeSH
NDL
subjects
ndlna
my
Experi-
ment
Italian
Museums
medu-
cator
MARC
Codes
List
Man-
chester
Reading
Lists
Lotico
Weather
Stations
London
Gazette
LOIUS
Linked
Open
Colors
lobid
Resources
lobid
Organi-
sations
LEM
Linked
MDB
LinkedL
CCN
Linked
GeoData
LinkedCT
Linked
User
Feedback
LOV
Linked
Open
Numbers
LODE
Eurostat
(Ontology
Central)
Linked
EDGAR
(Ontology
Central)
Linked
Crunch-
base
lingvoj
Lichfield
Spen-
ding
LIBRIS
Lexvo
LCSH
DBLP
(L3S)
Linked
Sensor Data
(Kno.e.sis)
Klapp-
stuhl-
club
Good-
win
Family
National
Radio-
activity
JP
Jamendo
(DBtune)
Italian
public
schools
ISTAT
Immi-
gration
iServe
IdRef
Sudoc
NSZL
Catalog
Hellenic
PD
Hellenic
FBD
Piedmont
Accomo-
dations
GovTrack
GovWILD
Google
Art
wrapper
gnoss
GESIS
GeoWord
Net
Geo
Species
Geo
Names
Geo
Linked
Data
GEMET
GTAA
STITCH
SIDER
Project
Guten-
berg
Medi
Care
Euro-
stat
(FUB)
EURES
Drug
Bank
Disea-
some
DBLP
(FU
Berlin)
Daily
Med
CORDIS
(FUB)
Freebase
flickr
wrappr
Fishes
of Texas
Finnish
Munici-
palities
ChEMBL
FanHubz
Event
Media
EUTC
Produc-
tions
Eurostat
Europeana
EUNIS
EU
Insti-
tutions
ESD
stan-
dards
EARTh
Enipedia
Popula-
tion (En-
AKTing)
NHS
(En-
AKTing) Mortality
(En-
AKTing)
Energy
(En-
AKTing)
Crime
(En-
AKTing)
CO2
Emission
(En-
AKTing)
EEA
SISVU
educatio
n.data.g
ov.uk
ECS
South-
ampton
ECCO-
TCP
GND
Didactal
ia
DDC Deutsche
Bio-
graphie
data
dcs
Music
Brainz
(DBTune)
Magna-
tune
John
Peel
(DBTune)
Classical
(DB
Tune)
Audio
Scrobbler
(DBTune)
Last.FM
artists
(DBTune)
DB
Tropes
Portu-
guese
DBpedia
dbpedia
lite
Greek
DBpedia
DBpedia
data-
open-
ac-uk
SMC
Journals
Pokedex
Airports
NASA
(Data
Incu-
bator)
Music
Brainz
(Data
Incubator)
Moseley
Folk
Metoffice
Weather
Forecasts
Discogs
(Data
Incubator)
Climbing
data.gov.uk
intervals
Data
Gov.ie
data
bnf.fr
Cornetto
reegle
Chronic-
ling
America
Chem2
Bio2RDF
Calames
business
data.gov.
uk
Bricklink
Brazilian
Poli-
ticians
BNB
UniSTS
UniPath
way
UniParc
Taxono
my
UniProt
(Bio2RDF)
SGD
Reactome
PubMed
Pub
Chem
PRO-
SITE
ProDom
Pfam
PDB
OMIM
MGI
KEGG
Reaction
KEGG
Pathway
KEGG
Glycan
KEGG
Enzyme
KEGG
Drug
KEGG
Com-
pound
InterPro
Homolo
Gene
HGNC
Gene
Ontology
GeneID
Affy-
metrix
bible
ontology
BibBase
FTS
BBC
Wildlife
Finder
BBC
Program
mes BBC
Music
Alpine
Ski
Austria
LOCAH
Amster-
dam
Museum
AGROV
OC
AEMET
US Census
(rdfabout)
Media
Geographic
Publications
Government
Cross-domain
Life sciences
User-generated content
http://lod-cloud.net
datacatalogs.org: 285 data catalogues
original data (= ground truth) still often missing

Data Integration in Large Organisations
Enterprise information integration: a key field of
business of the OK department at Fraunhofer IAIS
production-critical information often maintained in
dedicated IS already:
ERP, CRM, SCM, . . .
challenge: integration of these systems (with each
other, and with external sources)
Daimler, e.g., runs 3,000 independent IT systems
(after a decade of consolidation!)

XML, Web Services, SOA: Pros and Cons
Previous approaches to enterprise IT:
XML syntactic data representation
Web services data exchange protocols
SOA holistic approach for distributed system
architecture and communication
Still insufficient for data integration
SOA is good for transaction processing, . . .
. . . Linked Data is more efficient for networking
and integrating data: access to LOD Cloud,
lightweight, flexible

From Documents to Data
Web 1.0 static documents
“Web 1.5” content management and e-commerce
systems, exposing databases in a user- and
context-specific way
Web 2.0 user-generated content; mashups aggregating
data from different sources
Web of Data popular examples: schema.org, Google
Knowledge Graph, Facebook Open Graph

Web of Data: schema.org
initiative of major search engine operators
annotation vocabulary for structuring web pages
(creative works, events, organisations, persons,
places, products)
Example (Movie description)
Avatar
Director: James Cameron (born August 16, 1954)
Science fiction
Trailer

places, products)
<div class="movie">
<h1>Avatar</h1>
<div class="director">
Director: James Cameron
(born August 16, 1954)
</div>
<span class="genre">Science fiction</span>
<a href="../movies/avatar-theatrical-trailer.html"
Trailer</a>
</div>

places, products)
<div itemscope itemtype="http://schema.org/Movie">
<h1 itemprop="name">Avatar</h1>
<div itemprop="director" itemscope itemtype="http://schema.org/Person">
Director: <span itemprop="name">James Cameron</span>
(born <span itemprop="birthDate">August 16, 1954</span>)
</div>
<span itemprop="genre">Science fiction</span>
<a href="../movies/avatar-theatrical-trailer.html"
itemprop="trailer">Trailer</a>
</div>

places, products)
Movie Avatar Person
James Cameron
August 16, 1954Science fiction../movies/. . .
type
nam
e
director
genre
trailer
type
name
birthDate

Social Data with schema.org
review or rating of a creative work, organization or
product (written by a person)
social network of a person: “knows”, “works for”, “is
colleague of”, “has parent/sibling/spouse/child/relative”
Example (Reviews of a movie)
Movie type
Avatar
name
reviews
authorreviewRating
reviews
author
reviewRating
6
ratingValue
8.5
ratingValue
Pünktchen
name
Anton
name
Person
type
type
knows

schema.org in a Search Engine

The Web and Intranets: Evolution

Web and Intranet: Common Properties
Especially large organisations share these properties of
the WWW:
Decentral organisation
Self-dependent units, often free to choose their
information architecture
Heterogeneous information:
domain-specific applications,
knowledge bases,
document templates,
data formats
. . . vary across organisational units

Linked Organisational Data Principles
The 5-star Linked Data principles (above), plus:
evolve existing thesauri, taxonomies, wikis and
master data management systems into corporate
knowledge bases and knowledge hubs
establish an organisation-wide URI scheme
extend existing information system in the intranet
by linked data interfaces
establish links between sources of related
information

Organisational Knowledge Bases
Organisation and Domain-specific knowledge is in:
glossaries, taxonomies, internal documents, data
schemas.
Large organisations often standardise terminology
in multilingual thesaury

Mercedes-Benz Search Demo I
Search before

Mercedes-Benz Search Demo II
OntoWiki with car model data

Mercedes-Benz Search Demo III
OntoWiki
with car
model data

Mercedes-Benz Search Demo IV
Management of Enterprise Taxonomies with OntoWiki
based on the W3C SKOS standard
Corporate Language Management at Daimler:
500K concepts in 20 languages

Mercedes-Benz Search Demo V
Search after
Showing recommen-
dations from the
knowledge base in-
tegrating car model
data and enterprise
taxonomy

Mercedes-Benz Search Demo VI
You can search for
“Kombi” (station
wagon) and find
“T-Models” (Daim-
ler term for the
same)

Identification by an
Organisation-wide URI Schema
Unique identifiers are a key prerequisite for
information integration:
general: persons, places, organisations
specific: terms, data sources, products, contracts
On the Web: URI for identification, URLs for making
information accessible.
In Linked Data: use URLs as URIs

Properties of URIs
Decentral maintenance: different levels,
combinations possible (next slide)
Dereferenceability (i.e. URIs = URLs)
Provenance (URI reveals organisational unit ↝
authenticity and correctness of information)

Identifier Management Strategies
Management strategy + –
Issue uniform URIs cen-
trally
easy overview of re-
sources
uniform identifier struc-
ture
single point of failure
low flexibility
hard to ensure derefer-
enceability
Issue decentrally, regis-
ter centrally
easy overview of re-
sources
resilient against techni-
cal failure and organisa-
tional changes
requires synchronisa-
tion
Manage completely de-
centrally
highly flexible
highly resilient against
technical failure and or-
ganisational changes
lack of central overview
lack of structural unifor-
mity

Linked Organisational Data Lifecycle
Of particular interest:
RDF data management:
including relational sources
Authoring
Linking: detect links
between datasets
Classification, Enrichment
Quality Assessment: data
from the Web “fit for use”?
Evolution, Repair
Search, Exploration

Interdependence of Lifecycle Stages
Lifecycle stages depend on each other
⇒ addressing one also affects the others, e.g.:
1
enrich knowledge base with links to a new
knowledge hub
2
auto-linker will find additional matches
Schema and instance levels influence each other, e.g.:
rich schema prevents instance-level problems
can learn schema-level matches from instances
LOD2.eu project has developed tools for the
whole life cycle (available for Debian and
others) [Aue+12]

Linked Data Quality: Metrics
Quality = “fitness for use”. Subjective? There are objec-
tive, even application-independent metrics! [Zav+13]
Accessibility: actually linked data; machine-readable license;
performance of access?
Intrinsic aspects: no logical inconsistencies; no malformed
literal values; no redundancies?
Trust: provenance metadata; digital signature?
Dynamicity: recent data?
Contextual aspects: broad use of schema’s features; good
coverage of domain?
Representation: existing terms reused; human-readable
documentation?

Analysing Linked Data Quality
Java library for quality metrics in progress
We support big datasets (streaming triples)
Output once more as linked data [DLA14] – why?
complexity: data cube with dimensions metric, dataset,
time, intended application, . . . (e.g. “completeness of
DBpedia 3.9 for a Tallinn tourist guide”)
can → browse datasets by quality

Relational Data to RDF
Most existing information systems use relational
databases – choice between
1
materialising relational database into linked data
2
expose it as virtual RDF graph by on-demand query
translation

R2RML
R2RML (relational database to RDF mapping language),
W3C Recommendation
Example (mapping a thesaurus)
SUBJECT CONCEPTS
+===+=========+=========+=========+
+===============+ |ID | SUBJECT | TERM_EN | TERM_ET |
|ID | SUBJECT | +===+=========+=========+=========+
+===+===========+ | 1 | 1 | hammer | Vasar |
| 1 | tools | | 2 | 1 | file | Viil |
| 2 | chemistry | | 3 | 2 | oil | Õli |
+===+===========+ +===+=========+=========+=========+

R2RML
SUBJECT CONCEPTS
+===+=========+=========+=========+
|ID | SUBJECT | +===+=========+=========+=========+
+===+===========+ | 1 | 1 | hammer | Vasar |
| 1 | tools | | 2 | 1 | file | Viil |
+===+===========+ +===+=========+=========+=========+
:ConceptsTriplesMap
rr:logicalTable [ rr:tableName "CONCEPTS" ] ;
rr:subjectMap [
rr:template "http://example.com/term/concept/{ID}" ;
rr:class skos:Concept ;
] ;
rr:predicateObjectMap [
rr:predicate skos:prefLabel ;
rr:objectMap [ rr:column "TERM_ET" ; rr:language "et" ] ;
] .Lange/Auer (Bonn) Interlinking Data and Knowledge in Enterprises, Research and Society with Linked Data 2014-06-09 35

R2RML
SUBJECT CONCEPTS
+===+=========+=========+=========+
|ID | SUBJECT | +===+=========+=========+=========+
+===+===========+ | 1 | 1 | hammer | Vasar |
| 1 | tools | | 2 | 1 | file | Viil |
+===+===========+ +===+=========+=========+=========+
:ConceptsTriplesMap
rr:logicalTable [ rr:tableName "CONCEPTS" ] ;
rr:subjectMap [
rr:template "http://example.com/term/concept/{ID}" ;
rr:class skos:Concept ;
] ;
rr:predicateObjectMap [
rr:predicate skos:prefLabel ;
rr:objectMap [ rr:column "TERM_ET" ; rr:language "et" ] ;
] .
<http://example.com/term/concept/1> a skos:Concept .
<http://example.com/term/concept/1> skos:prefLabel "Vasar"@et .

Data Portals
How to discover suitable open datasets?
⇒ look into data catalogues, e.g. http://datahub.io
Quality-based filtering and ranking

Link Discovery Tools
1
Found a dataset that’s “fit for use”?
2
Link them to existing organisational datasets!
LOD2 tools Silk and LIMES help with this
Rule example: similar name, and ⋃︀price − price′ < 0.1⋃︀ ⇒
create owl:sameAs link
3
<foo> owl:sameAs <bar> means: all properties
of foo also hold for bar, and vice versa.
Linking particularly pays off on the terminology level;
DBpedia serves as a common referencing target for
almost anything of interest.

Enterprise Knowledge Hub [Fri+13]

Take Home Messages
Linked Data: promising technology for closing the
gap between SOA and unstructured information
management
wealth of LOD can be leveraged as background
knowledge for Enterprise applications
application of Linked Data in large organisations (in
enterprises, research and society) is still largely
unexplored (⇒ opportunity!)
Linked Data will make Organisational Information
Integration more
flexible
iterative
cost effective

References
References I
5 star Open Data. Apr. 3, 2012. url:
http://5stardata.info/ (visited on 2013-09-18).
S. Auer, L. Bühmann, C. Dirschl, O. Erling,
M. Hausenblas, R. Isele, J. Lehmann, M. Martin,
P. N. Mendes, B. van Nuffelen, C. Stadler, S. Tramp,
and H. Williams. “Managing the life-cycle of Linked
Data with the LOD2 Stack”. In: Proceedings of
International Semantic Web Conference (ISWC 2012).
22% acceptance rate. 2012. url:
http://iswc2012.semanticweb.org/sites/
default/files/76500001.pdf.

References
References II
J. Debattista, C. Lange, and S. Auer. “daQ, an Ontology
for Dataset Quality Information”. In: Linked Data on
the Web (LDOW). (Seoul, Apr. 8, 2014). Ed. by C. Bizer,
T. Heath, S. Auer, and T. Berners-Lee. 2014. url:
http://events.linkeddata.org/ldow2014/.
P. Frischmuth, S. Auer, S. Tramp, J. Unbehauen,
K. Holzweißig, and C.-M. Marquardt. “Towards Linked
Data based Enterprise Information Integration”. In:
Proceedings of the Workshop on Semantic Web
Enterprise Adoption and Best Practice (WASABI) 2013.
2013. url: http://www.wasabi-
ws.org/papers/wasabi03/paper.pdf.

References
References III
E. Hyvönen, J. Tuominen, M. Alonen, and E. Mäkelä.
“Linked Data Finland: A 7-star Model and Platform for
Publishing and Re-using Linked Datasets”. In:
M. Kerber, C. Lange, and C. Rowat. ForMaRE. Formal
Mathematical Reasoning in Economics. url: http://
cs.bham.ac.uk/research/projects/formare/
(visited on 2013-02-10).
C. Lange. “Enabling Collaboration on Semiformal
Mathematical Knowledge by Semantic Web
Integration”. PhD thesis. Jacobs University Bremen,
2011.

References
References IV
A. Zaveri, A. Rula, A. Maurino, R. Pietrobon,
J. Lehmann, and S. Auer. “Quality Assessment
Methodologies for Linked Open Data (Under
Review)”. In: Semantic Web Journal (2013). This article
is still under review. url: http://www.semantic-
web-journal.net/content/quality-
assessment-linked-open-data-survey.

Interlinking Data and Knowledge in Enterprises, Research and Society with Linked Data

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Interlinking Data and Knowledge in Enterprises, Research and Society with Linked Data