The document discusses methods for discovering related data sources within data portals, emphasizing the importance of integrating and querying multiple open data sets. It outlines a framework utilizing entity extraction, similarity measures, and clustering to identify complementary data sources, illustrated through examples like GDP comparisons. Additionally, it presents future work on user interfaces for querying across data sets and enhancing interactive visualizations.

1. Discovering
Related
Data
Sources
in
Data
Portals
Andreas
Wagner,
Peter
Haase,
Achim
Re4nger,
Holger
Lamm
1st
Interna:onal
Workshop
on
Seman:c
Sta:s:cs
Sydney,
Oct
22,
2013

2. Poten&al
of
Open
(Sta&s&cs)
Data
WORLD BANK

3. fluidOps
Open
Data
Portal
• Data
collec&on
• Integra&on
of
major
open
data
catalogs
• Automated
provisioning
of
10.000s
data
sets
• Portal
for
search
and
explora&on
of
data
sets
• Rich
metadata
based
on
open
standards
• Both
descrip&ve
and
structural
metadata
• Integrated
querying
across
interlinked
data
sets
• Easy
to
use
queries
against
mul&ple
data
sets
• Using
federa&on
technologies
• Self-­‐service
UI
• Custom
queries
and
visualiza&ons
• Widgets,
dashboarding,
etc.
WORLD BANK

5. Finding
Related
Data
Sets
• Many
informa&on
needs
require
analysis
of
mul&ple
data
sets
• Example:
Compare
and
correlate
GDP,
popula&on
and
public
debt
of
countries
over
&me
• Task
of
finding
related
data
sets
• Iden&fy
data
sets
that
are
similar,
but
complementary
• To
support
queries
across
mul&ple
data
sets,
e.g.
in
the
form
of
joins
and
unions
• Inspira&on:
Finding
related
tables
• En&ty
complement:
same
aVributes,
complemen&ng
en&&es
• Schema
complement:
same
en&&es,
complemen&ng
aVributes

6. Finding
Related
Data
Sources
via
Related
En&&es
• Data
Model:
Data
source
is
a
set
of
mul&ple
RDF
graphs
• Intui&on:
if
data
sources
contain
similar
en&&es,
they
are
somehow
related
Cluster
2
Cluster
1
• Approach:
En&&es
1. En&ty
Extrac&on
2. En&ty
Similarity
3. En&ty
Clustering
Related?!
Source
1
Source
3
Source
2

7. Related
En&&es
(2)
1. En&ty
Extrac&on
– Sample
over
en&&es
in
data
graphs
in
D
– For
each
en&ty
crawl
its
surrounding
sub-­‐graph
[1]
2. En&ty
Similarity
– Define
dissimilarity
measure
between
two
en&&es
based
on
kernel
func&ons
– Compare
en&ty
structure
and
literals
via
different
kernels
[2,3]
3. En&ty
Clustering
– Apply
k-­‐means
clustering
to
discover
similar
en&&es
[4]

8. Contextualisa&on
Score
• Contextualiza&on
score
for
data
source
D’’
given
D’:
ec(D’’|D’)
and
sc(D’’|D’)
• En*ty
complement
score
• Schema
complement
score

10. Search
for
Gross
Domes&c
Product

12. Querying
the
Data
Set

13. Visualizing
the
Results

14. Queries
Across
Related
Data
Sets
• Query
for
GDP
of
Germany
• Union
of
results
from
• Worldbank:
GDP
(current
US$
)
(up
to
2010)
• Eurostat:
GDP
at
Market
Prices
(including
projected
values
un&l
2014)

15. Queries
Across
Related
Data
Sets
Data
from
Worldbank
Data
from
Eurostat

16. Summary
and
Outlook
• Techniques
for
finding
related
data
sets
– Based
on
finding
related
en&&es
• Implementa&on
available
in
open
data
portal
• Outlook
– Finding
relevant
related
data
sources
for
a
given
informa&on
need
– End
user
interfaces
for
formula&ng
queries
across
data
sets
(see
Op&que
project)
– Operators
for
combining
data
cubes
– Interac&ve
visualiza&on
and
explora&on
of
combined
data
cubes
(see
OpenCube
project)

17. References
[1]
G.
A.
Grimnes,
P.
Edwards,
and
A.
Preece.
Instance
based
clustering
of
seman:c
web
resources.
In
ESWC,
2008.
[2]
U.
Lösch,
S.
Bloehdorn,
and
A.
Reenger.
Graph
kernels
for
RDF
data.
In
ESWC,
2012.
[3]
J.
Shawe-­‐Taylor
and
N.
Cris&anini.
Kernel
Methods
for
PaPern
Analysis.
2004.
[4]
R.
Zhang
and
A.
Rudnicky.
A
large
scale
clustering
scheme
for
kernel
k-­‐means.
In
PaVern
Recogni&on,
2002.