Discovering Related Data Sources in Data Portals

Oct 21, 20130 likes1,532 views

Peter Haase

Slides from my presentation at the 1st International Workshop on Semantic Statistics Sydney, Oct 22, 2013

Poten&al
of
Open
(Sta&s&cs)
Data

WORLD BANK

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

Discovering Related Data Sources in Data Portals

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

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

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]

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

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)

Queries
Across
Related
Data
Sets

Data
from
Worldbank

Data
from
Eurostat

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)

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.

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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.

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