2. Motivation:
Music!
2
Visualiza3on
Module
Metadata
Streaming
providers
Physical
Wrapper
Downloads
Data
acquisi3on
R2R
Transf.
LD
Wrapper
Musical
Content
Applica3on
Analysis
&
Mining
Module
LD
Dataset
Access
LD
Wrapper
RDF/
XML
Integrated
Dataset
Interlinking
Cleansing
Vocabulary
Mapping
SPARQL
Endpoint
Publishing
RDFa
Other
content
EUCLID
–
Interac3on
with
Linked
Data
3. Motivation:
Music!
(2)
EUCLID
–
Interac3on
with
Linked
Data
3
• Our
aim:
build
a
music-‐based
portal
using
Linked
Data
technologies
• So
far,
we
have
studied
different
mechanisms
to
consume
Linked
Data:
• Execu3ng
SPARQL
queries
• Dereferencing
URIs
• Downloading
RDF
dumps
• Extrac3ng
RDFa
data
• The
output
of
these
mechanisms
corresponds
to
data
in
machine-‐readable
formats
CH
2
CH
3
CH
1
5. Visualiza=ons
techniques
are
needed
in
order
to
transform
the
machine-‐readable
data
into
this:
Motivation:
Music!
(4)
EUCLID
–
Interac3on
with
Linked
Data
5
Source:
hZp://musicbrainz.fluidops.net/
6. In
addi3on,
visualiza=on
techniques
allow
for:
Motivation:
Music!
(5)
EUCLID
–
Interac3on
with
Linked
Data
6
• Telling
a
story
• Engaging
our
paZern
matching
brain
• Iden3fying
data
characteris3cs
which
cannot
be
directly
inferred
from
sta3s3cal
proper3es:
• Anscombe’s
quartet:
4
datasets
very
different,
but
with
same
sta3s3cal
values.
Image:
hZp://en.wikipedia.org/wiki/Anscombe's_quartet
Source:
Donaldson,
I.
and
Lamere
P.
Using
Visualiza,ons
for
Music
Discovery
Image:
Chan
W.,
Qu.
H,
Mak,
W.
Visualizing
the
Seman,c
Structure
in
Classical
Musical
Works.
7. Agenda
1. Linked
Data
visualiza=on
2. Linked
Data
search
3. Methods
for
Linked
Data
analysis
7
EUCLID
–
Interac3on
with
Linked
Data
9. LD
Visualization
Techniques
• Linked
Data
visualiza3on
techniques
should
provide
graphical
representa=ons
of
the
informa3on
within
the
LD
datasets
• Visualiza3on
techniques
should
be
selected
accordingly
to:
– The
type
of
data:
Specific
types
of
data
should
be
visualized
in
a
certain
way
– The
purpose
of
the
visualiza=on:
Depending
on
the
type
of
analysis/applica3on
to
employ
9
EUCLID
–
Interac3on
with
Linked
Data
10. LD
Visualization
Techniques
(2)
EUCLID
–
Interac3on
with
Linked
Data
10
• (Raw)
RDF
data:
Instance
data,
taxonomies,
ontologies,
vocabularies.
• Analy=cally
extracted
data:
Subset
of
the
data
denominated
region
of
interest
(ROI),
obtained
via
data
extrac,on
mechanisms,
for
example,
SPARQL
queries.
• Visualiza=on
abstrac=on:
It
is
obtained
by
applying
visualiza,on
transforma,ons
to
render
the
data
into
displayable
informa3on.
• View:
Final
result.
The
visual
mapping
transforma3ons
obtain
a
graphic
representa3on
of
the
data
using
the
selected
visualiza3on
technique.
• User
interac=on:
The
user
interacts
(click,
zoom,
etc.)
with
the
visualiza3on,
which
may
trigger
a
new
visualiza3on
process.
RDF
data
Analy3cally
extracted
data
Visualiza3on
abstrac3on
View
Data
extraction
Visualization
transformation
Visual
mapping
transformation
Overview
of
the
Linked
Data
Visualization
process
Process
par3ally
based
on:
Brunej
,
J.M.;
Auer,
S.;
García,
R.
The
Linked
Data
Visualiza,on
Model.
(Op3onal)
User
interaction
11. country
releases
United
Kingdom
225
United
States
140
Germany
30
Luxembourg
29
LD
Visualization
Techniques
(3)
EUCLID
–
Interac3on
with
Linked
Data
11
Example
of
the
Linked
Data
Visualization
process
…
RDF
data
Analy3cally
extracted
data
…
Visualiza3on
abstrac3on
SELECT
?country
(COUNT(?release)
AS
?releases)
WHERE
{
<http://dbpedia.org/resource/The_Beatles>
foaf:made
?release
.
?release
a
mo:Release
;
mo:label
?label
.
?label
foaf:based_near
?country
.}
GROUP
BY
?country
ORDER
BY
DESC(?releases)
Data
extraction
SPARQL
query:
Retrieve
number
of
releases
per
country
of
The
Beatles
#widget
:
HeatMap
|
input
=
'country_code'
|
output
=
{{
'releases'
}}
Visualization
transformation
country_code
releases
GB
225
US
140
DE
30
LU
29
?country_code2
:=
REPLACE(str(?country),
"hZp://ontologi.es/place/",
"",
"i”)
?country_code
:=
REPLACE(?country_code2,
"%",
"",
"i")
Formajng
the
names
of
the
countries
View
Visual
mapping
transformation
Selec3ng
the
visualiza3on
technique
(input,
output)
Can
be
performed
in
a
single
step
…
…
12. LD
Visualization
Techniques
(3)
EUCLID
–
Interac3on
with
Linked
Data
12
Example
of
the
Linked
Data
Visualization
process
View
13. Challenges
for
Linked
Data
Visualization
EUCLID
–
Interac3on
with
Linked
Data
13
• Enabling
user
interac=on
– Users
must
be
able
to
navigate
through
the
data
by
exploi3ng
the
connec3ons
between
Linked
Data
resources
– The
user
might
edit
the
underlying
data
to
enrich
it
by:
• Crea3ng
addi3onal
metadata
• Highligh3ng
or
correc3ng
errors
• Valida3ng
data
• Suppor3ng
data
reusability
– The
output
(the
ploZed
data
or
the
visualiza3on
itself)
might
be
encoded
using
standard
ontologies
and
vocabularies
• Scalability
– Linked
Data
visualiza3on
techniques
should
support
the
display
of
large
amount
of
data
in
an
efficient
way
14. Challenges
for
Linked
Open
Data
Visualization
EUCLID
–
Interac3on
with
Linked
Data
14
• Extrac3ng
data
from
different
repositories
– A
Linked
Data
set
might
be
par33oned
into
several
repositories
– The
region
of
interest
(ROI)
might
include
data
from
different
data
sets,
requiring
the
access
to
distributed
repositories
• Handling
heterogeneous
data
– The
same
data
(concepts)
might
be
modeled
differently,
for
example,
using
different
vocabularies
– Certain
values
might
have
different
formats,
for
example,
dates
represented
as
DD-‐MM-‐YYYY,
MM-‐DD-‐YYYY
or
just
YYYY
• Dealing
with
missing
values
– Due
to
the
semi-‐structuredness
of
Linked
Data,
some
instances
might
have
missing
values
for
certain
proper3es
15. Classification
of
Visualization
Techniques
15
EUCLID
–
Interac3on
with
Linked
Data
Task
Visualiza=on
techniques
Comparison
of
aZributes
/
values
• Bar/column
and
pie
chart
• Line
charts
• Histogram
Analysis
of
rela3onships
and
hierarchies
• Graph
• Arc
diagram
• Matrix
• Node-‐link
visualiza3ons
• Space-‐filling
techniques:
Treemaps,
icicles
and
sunburst,
circle
packing
and
rose
diagrams
Analysis
of
temporal
or
geographical
events
• Timeline
• Maps
Analysis
of
mul3-‐
dimensional
data
• Parallel
coordinates
• Radar/star
chart
• ScaZer
plot
16. Bar/column
chart
Allows
the
comparison
of
values
of
different
categories.
Pie
chart
Useful
for
performing
comparison
of
percentages
or
propor3ons.
Comparison
of
Attributes
/
Values
16
EUCLID
–
Interac3on
with
Linked
Data
Line
chart
Allows
visualizing
data
as
a
series
of
data
points,
where
the
measurement
points
(x-‐axis)
are
ordered.
Histogram
Graphical
representa3on
of
the
distribu3on
of
the
data.
Image
source:
hZp://mbostock.github.io/protovis/
Image
source:
hZp://musicbrainz.fluidops.net
Image
source:
hZp://mbostock.github.io/protovis/
Image
source:
hZp://musicbrainz.fluidops.net
17. Arc
diagram
The
nodes
are
displayed
in
one
dimension,
and
the
arcs
represent
the
connec3ons.
Analysis
of
Relationships
and
Hierarchies
Graph
The
data
entries
are
represented
as
nodes
and
the
links
as
edges.
17
EUCLID
–
Interac3on
with
Linked
Data
Adjacency
Matrix
diagram
The
nodes
are
displayed
as
rows
and
columns,
and
the
links
between
the
nodes
are
entries
in
the
matrix.
Node-‐link
visualiza3ons
The
data
is
organized
in
hierarchies.
Source
of
images:
hZp://mbostock.github.io/protovis/
18. Icicles
and
sunburst
Hierarchies
are
represented
by
adjacencies.
Analysis
of
Relationships
and
Hierarchies
(2)
Treemaps
Subdivide
area
into
rectangles.
18
EUCLID
–
Interac3on
with
Linked
Data
Circle-‐packing
Containment
is
used
to
represent
the
hierarchies.
Rose
diagrams
Areas
are
equal
angles
and
the
data
is
represented
by
the
extension
of
the
area.
Source
of
images:
hZp://mbostock.github.io/protovis/
Space-‐filling
techniques
19. Analysis
of
Temporal
or
Geographical
Events
Timeline
19
EUCLID
–
Interac3on
with
Linked
Data
Maps
Source:
hZp://mbostock.github.io/protovis/
Choropleth
maps
Aggregate
data
by
geographical
area
Loca3on
maps
Display
geo-‐points
on
a
map
Dorling
cartograms
Aggregate
data
and
replace
each
area
with
a
circle
Discrete
data
points
in
3me
Con3nuous
data
in
3me
Source:
hZp://www.koZke.org/08/08/2008-‐movie-‐box-‐office-‐chart
Source:
hZp//musicbrainz.fluidops.net
Source:
Google
Map
API
Source:
hZp//musicbrainz.fluidops.net
20. ScaZer
plot
Useful
for
performing
comparison
of
percentages
or
propor3ons.
Analysis
of
Multidimensional
Data
Radar/star
chart
Displays
mul3variate
data
as
a
two-‐
dimensional
chart.
The
axes
correspond
to
the
variables.
20
EUCLID
–
Interac3on
with
Linked
Data
Parallel
coordinates
Allows
visualizing
high-‐dimensional
data.
Each
ver3cal
axis
denotes
a
dimension,
and
a
mul3dimensional
point
is
represented
as
a
polyline
with
ver3ces
on
the
axes.
Source:
hZp://mbostock.github.io/protovis/
Source:
hZp://mbostock.github.io/protovis/
Source:
hZp://mbostock.github.io/protovis/
21. Other
Visualization
Techniques
EUCLID
–
Interac3on
with
Linked
Data
21
• Text-‐based
visualiza3ons:
tag
clouds
• Some
of
the
previously
presented
techniques
can
be
combined
to
produce
more
complex
data
visualiza3ons
Phrase
Net
of
Beatles
Lyrics
DBpedia
music
genres
Source:
hZp://www.wordle.net
Source:
hZp://many-‐eyes.com
22. • Get
an
overview
of
the
data
• Iden3fica3on
of
relevant
resources,
classes
or
proper=es
in
datasets
• Learning
about
certain
underlying
characteris=cs
of
the
data,
e.g.,
vocabularies
or
ontologies
• Detec3ng
missing
links
between
nodes
in
an
RDF
graph
• Discovering
new
paths
between
nodes
in
an
RDF
graph
• Iden3fying
hidden
paUerns
in
the
data
• Finding
errors
or
atypical
values
(outliers)
22
EUCLID
–
Interac3on
with
Linked
Data
Applications
of
Linked
Data
Visualization
Techniques
23. Linked
Data
Visualization
Tool
Requirements
The
requirements
for
visualiza3on
tools
that
consume
Linked
Data
can
be
summarized
as
follows:
• Data
naviga=on
and
explora=on
capabili3es
in
order
to
understand
the
structure
and
the
content
• Exploi3ng
data
structures:
• Links
to
visualize
hierarchies
or
graphs
• Mul3-‐dimensional
• User
interac=on:
• Basic
and
advanced
querying
• Filtering
values
• Interac3ve
UI:
responsive
to
the
user
input
• Publica=on/syndica=on
of
the
graphical
representa3on
of
the
data
• Data
extrac=on
in
order
to
export
the
data
such
that
can
be
reused
by
third
par3es
23
EUCLID
–
Interac3on
with
Linked
Data
24. Linked
Data
Visualization
Tool
Types
1.
LD
browsers
with
text-‐based
representation
• Dereference
URIs
to
retrieve
the
resource
descrip3on
• Use
a
textual
representa3on
of
LD
resources
• Display
adequately
texts
and
images
• Mainly
support
exploratory
browsing
and
knowledge
discovery
2.
LD
and
RDF
browsers
with
visualization
options
• Exploit
picture,
graphics,
images
and
other
visual
representa3ons
of
the
data
• Support
user
interac3on:
allows
for
querying,
filtering
and
jumping
between
resources
• Suitable
for
browsing
and
knowledge
discovery
as
well
as
analy3c
ac3vi3es
24
EUCLID
–
Interac3on
with
Linked
Data
25. Linked
Data
Visualization
Tool
Types
(2)
3.
Visualization
toolkits
• Frameworks
providing
a
wide
range
of
visualiza3on
techniques
• General
toolkits
support
LD
visualiza3on
by
applying
a
set
of
transforma3ons
of
the
data
• Some
toolkits
are
specially
designed
to
consume
LD
4.
SPARQL
visualization
• These
tools
allow
transforming
the
output
of
SPARQL
queries
into
graphics
• Contact
SPARQL
endpoints
in
order
to
evaluate
the
query
• Suitable
for
analy3cal
ac3vi3es
25
EUCLID
–
Interac3on
with
Linked
Data
26. Linked
Data
Visualization
Tool
Types
(3)
26
EUCLID
–
Interac3on
with
Linked
Data
LD
browsers
with
text-‐
based
presenta3ons
Sig.ma
Sindice
OpenLink
RDF
Browser
Marbles
Disco
Hyperdata
Browser
Piggy
Bank
(SIMILE)
Zitgist
DataViewer
iLOD
URI
Burner
Dipper
–
Talis
Pla•orm
Browser
LD
and
RDF
browsers
with
visualiza=on
op3ons
Tabulator
IsaViz
OpenLink
Data
Explorer
RDF
Gravity
RelFinder
DBpedia
Mobile
LESS
SIMILE
Exhibit
Haystack
FoaF
Explorer
Humboldt
LENA
Noadster
Visualiza3on
toolkits
Linked
Data
tools:
Informa3on
Workbench
Visual
RDF
(by
Graves)
LOD
Live
LOD
Visualiza3on
Data-‐Driven
Documents
(D3)
NetworkX
Many
Eyes
Tableau
Prefuse
SPARQL
visualiza3on
Informa3on
Workbench
Google
Visualiza3on
API
SPARQL
package
for
R
Gruff
(for
AllegroGraph)
Linked
Data:
General
data:
27. Linked
Data
Visualization
Examples
(1)
EUCLID
–
Interac3on
with
Linked
Data
27
Sig.ma
Source:
hZp://sig.ma/search?q=The+Beatles
Retrieves
informa3on
from
different
LD
sources
Keyword
search
Displays
values
per
predicate
Displays
the
source
for
each
value
28. Linked
Data
Visualization
Examples
(2)
EUCLID
–
Interac3on
with
Linked
Data
28
Sig.ma
Source:
hZp://sig.ma/search?q=The+Beatles
Displays
values
per
predicate:
May
include
(redundant)
informa3on
in
different
languages,
for
example:
annés
and
anno
Summary:
• Sig.ma
lists
all
the
triples,
and
group
them
per
predicate
• Useful
for
browsing
predicates
and
values
within
data
sets
• The
meaning
of
the
values
is
not
evident
URIs
are
clickable,
allowing
naviga3on
through
RDF
resources
29. Linked
Data
Visualization
Examples
(3)
EUCLID
–
Interac3on
with
Linked
Data
29
Sindice
Keyword
search
Filtering
per
type
of
document
Retrieves
links
to
documents
Allows
accessing
cache
documents
Allows
inspec3ng
resources
Source:
hZp://sindice.com/search?q=The+Beatles
30. Linked
Data
Visualization
Examples
(4)
EUCLID
–
Interac3on
with
Linked
Data
30
Sindice
Both
interfaces
display
the
set
of
triples
related
to
the
inspected
resource
Cache
triples
Live
triples
31. Linked
Data
Visualization
Examples
(5)
EUCLID
–
Interac3on
with
Linked
Data
31
Information
Workbench
• Demo
available
at:
hZp://musicbrainz.fluidops.net
• Displays
human-‐readable
content
about
Linked
Data
resources
• Supports
visualiza=on
techniques
(different
types
of
charts,
maps,
3melines,
etc.)
to
plot
results
from
SPARQL
queries
• Allows
the
user
to
interact
with
the
displayed
data
32. Linked
Data
Visualization
Examples
(6)
EUCLID
–
Interac3on
with
Linked
Data
32
Information
Workbench:
Browsing
a
music
artist
(1)
Search
op3ons
(2)
Search
results
33. Linked
Data
Visualization
Examples
(7)
EUCLID
–
Interac3on
with
Linked
Data
33
Information
Workbench:
Browsing
a
music
artist
(3)
Browsing
the
selected
resource
34. Linked
Data
Visualization
Examples
(8)
EUCLID
–
Interac3on
with
Linked
Data
34
Information
Workbench:
Visualization
techniques
(3)
Browsing
the
selected
resource
35. Linked
Data
Visualization
Examples
(9)
EUCLID
–
Interac3on
with
Linked
Data
35
Information
Workbench:
User
interaction
LD
visualiza3ons
must
support
naviga3on
through
the
data
Source:
hZp://musicbrainz.fluidops.net/resource/Analy3cal5
36. Linked
Data
Visualization
Examples
(9)
EUCLID
–
Interac3on
with
Linked
Data
36
Information
Workbench:
SPARQL
Visualization
Implements
widgets
which
allow:
• Retrieving
ROI
via
SPARQL
queries
• Selec3ng
the
appropriate
visualiza3on
technique
• Configuring
parameters
of
the
visualiza3on
37. Linked
Data
Visualization
Examples
(10)
EUCLID
–
Interac3on
with
Linked
Data
37
Information
Workbench:
SPARQL
visualization
SELECT
?release
((SUM(xsd:double(?duration/60000)))
AS
?avg)
WHERE
{
<http://dbpedia.org/resource/The_Beatles>
foaf:made
?release
.
?release
mo:record
?record
.
?record
mo:track
?track
.
?track
mo:duration
?duration
.}
GROUP
BY
?release
ORDER
BY
DESC(?avg)
LIMIT
10
SPARQL
Query
Result
set
Top
ten
The
Beatles
releases
according
to
the
sum
of
track
dura,ons
in
minutes
38. Linked
Data
Visualization
Examples
(11)
EUCLID
–
Interac3on
with
Linked
Data
38
Information
Workbench:
SPARQL
visualization
Top
ten
The
Beatles
releases
according
to
the
sum
of
track
dura,ons
in
minutes
Widget
Visualization:
Bar
chart
{{#widget:
BarChart
|
query
='SELECT
(COUNT(?Release)
AS
?COUNT)
?
label
WHERE
{
<http://musicbrainz.org/artist/8538e728-‐ca0b-‐4321-‐b7e5-‐
cff6565dd4c0#_>
foaf:made
?Release.
?Release
rdf:type
mo:Release
.
?Release
dc:title
?label
.}
GROUP
BY
?label
ORDER
BY
DESC(?COUNT)
LIMIT
20'
|
settings
=
'Settings:barvertical_mb'
|
asynch
=
'true'
|
input
=
'label'
|
output
=
'COUNT'
|
height
=
'300’}}
39. Linked
Data
Visualization
Examples
(12)
EUCLID
–
Interac3on
with
Linked
Data
39
Information
Workbench:
SPARQL
visualization
Top
ten
The
Beatles
releases
according
to
the
sum
of
track
dura,ons
in
minutes
Other
visualiza3ons
of
the
same
result
set
…
Line
chart:
Pie
chart:
40. Linked
Data
Visualization
Examples
(13)
EUCLID
–
Interac3on
with
Linked
Data
40
Information
Workbench:
Automated
Widget
Suggestion
Bar
chart
Line
chart
Pie
chart
1
2
3
Table
Pivot
view
Select
a
suggested
visualiza3on
Visualiza3on
automa3cally
built
41. Linked
Data
Visualization
Examples
(14)
EUCLID
–
Interac3on
with
Linked
Data
41
Other
tools
Source:
hZp://en.lodlive.it
Source:
hZp://lodvisualiza3on.appspot.com
LOD
Visualization
LOD
live
• Graph
visualiza3ons
• Interac3ve
UI
(the
graph
can
be
expanded
by
clicking
on
the
nodes)
• Live
access
to
SPARQL
endpoints
• Hierarchy
visualiza3ons:
treemaps
and
trees
• Live
access
to
SPARQL
endpoints
(suppor3ng
JSON
and
SPARQL
1.1)
42. Linking
Open
Data
Cloud
Visualization
(1)
42
EUCLID
–
Interac3on
with
Linked
Data
“The
Linking
Open
Data
cloud
diagram”
by
Richard
Cyganiak
and
Anja
Jentzsch
Source:
hZp://lod-‐cloud.net
• The
nodes
correspond
to
Linked
Data
sets
• The
edges
represent
connec3ons
between
Linked
Data
sets
• The
size
of
the
nodes
is
propor3onal
to
the
number
of
triples
in
each
data
set
• The
datasets
are
categorized
by
knowledge
domains
represented
with
colors
43. Linking
Open
Data
Cloud
Visualization
(2)
43
EUCLID
–
Interac3on
with
Linked
Data
Image
source:
hZp://twitpic.com/17qj1h
“Linked
Open
Data
Cloud”
generated
by
Gephis
• The
central
cluster
(green)
displays
DBpedia
as
a
central
focus
• The
size
of
the
nodes
reflect
the
size
of
the
datasets
• The
length
of
the
connec=ons
encode
informa3on
about
the
data
structure
Source:
A.
Dadzie
and
M.
Rowe.
Approaches
to
Visualizing
Linked
Data:
A
Survey.
2011
44. Linking
Open
Data
Cloud
Visualization
(3)
44
EUCLID
–
Interac3on
with
Linked
Data
“Linked
Open
Data
Graph”
by
Protovis
Source:
hZp://inkdroid.org/lod-‐graph/
• The
data
to
be
displayed
are
retrieved
using
the
CKAN
API
• The
nodes
represent
Linked
Data
sets
available
in
the
Data
Hub
“lod-‐
cloud”
group
• The
size
of
the
nodes
is
propor3onal
to
the
data
set
size
• Edges
are
connec3ons
between
data
sets
• The
colors
reflect
the
CKAN
ra3ng
and
the
intensity
of
the
color
reflects
the
number
of
received
ra3ngs
• The
nodes
can
be
clicked
to
go
to
the
data
set
CKAN
page
45. LD
Reporting
EUCLID
–
Interac3on
with
Linked
Data
45
• Visualiza3ons
techniques
are
used
in
the
crea3on
of
reports
included
in
data
monitoring
and
management
solu3ons
• Provides
and
overview
of
the
dataset
by
genera3ng
a
low-‐level
descrip=ve
analysis:
• Quan3ta3ve
informa3on
about
the
dataset
• Users
may
interact
with
the
data
via
dashboards
• Some
systems
support
this
feature
over
structured
data:
• Google
Webmaster
Tools
(hZps://www.google.com/webmasters/tools)
• Informa3on
Workbench
(hZp://www.fluidops.com/informa3on-‐workbench)
• eCloudManager
(hZp://www.fluidops.com/ecloudmanager)
46. Google
Webmaster
Tool:
Structure
Data
Dashboard
(1)
EUCLID
–
Interac3on
with
Linked
Data
46
• Provides
to
webmasters
informa3on
about
the
structured
data
embedded
in
their
websites
(and
recognized
by
Google)
• The
dashboard
three
levels:
i. Site-‐level
view:
aggregates
the
data
by
classes
defined
in
the
vocabulary
schema
ii. Item-‐type-‐level
view:
provides
details
per
page
for
each
type
of
resource
iii. Page-‐level
view:
shows
the
aZributes
of
every
type
of
resource
on
a
given
web
page
47. Google
Webmaster
Tool:
Structure
Data
Dashboard
(2)
EUCLID
–
Interac3on
with
Linked
Data
47
Source:
hZp://googlewebmastercentral.blogspot.de/2012/07/introducing-‐structured-‐data-‐dashboard.html
Site-‐level
view
48. Google
Webmaster
Tool:
Structure
Data
Dashboard
(3)
EUCLID
–
Interac3on
with
Linked
Data
48
Source:
hZp://googlewebmastercentral.blogspot.de/2012/07/introducing-‐structured-‐data-‐dashboard.html
Page-‐level
view
Site-‐level
view
50. Semantic
Search
Process
Using
semantic
models
for
the
search
process
50
EUCLID
–
Interac3on
with
Linked
Data
Faceted
Search
Seman=c
Search
Image
based
on:
Tran,
T.,
Herzig,
D.,
Ladwig,
G.
SemSearchPro-‐
Using
seman3cs
through
the
search
process
Data
graphs
Query
Result
visualiza=on/
presenta=on
User
query
(e.g.
keywords,
NL)
Query
visualiza=on
(Op3onal)
User
System
Refinement
Presenta3on
Analysis
Presenta3on
/
Ranking
Graph
matching
En3ty
Extrac3on
/
Seman3c
query
analysis
51. Image
Source:
hZp://musicontology.com
Semantic
Search:
Example
(1)
51
EUCLID
–
Interac3on
with
Linked
Data
User
query
(NL)
“songs
wriZen
by
members
of
the
beatles”
En=ty
extrac=on:
Query
expansion:
song
track
melody
tune
synonym
synonym
mo:Track
Candidates
…
song
member
(of)
wriZen
by
(the)
beatles
En=ty
mapping:
52. Semantic
Search:
Example
(2)
52
EUCLID
–
Interac3on
with
Linked
Data
User
query
(NL)
“songs
wriZen
by
members
of
the
beatles”
En=ty
extrac=on:
Query
expansion:
writer
composer
creator
synonym
mo:composer
Image
Source:
hZp://musicontology.com
Candidates
wriZen
by
inverse
of
…
song
member
(of)
wriZen
by
(the)
beatles
En=ty
mapping:
53. Semantic
Search:
Example
(3)
53
EUCLID
–
Interac3on
with
Linked
Data
User
query
(NL)
“songs
wriZen
by
members
of
the
beatles”
En=ty
extrac=on:
song
member
(of)
wriZen
by
(the)
beatles
Query
expansion:
member
(of)
mo:member
_of
mo:member
inverse
of
Image
Source:
hZp://musicontology.com
En=ty
mapping:
54. Semantic
Search:
Example
(4)
54
EUCLID
–
Interac3on
with
Linked
Data
User
query
(NL)
“songs
wriZen
by
members
of
the
beatles”
En=ty
extrac=on:
song
member
(of)
wriZen
by
(the)
beatles
En=ty
mapping:
(the)
beatles
Candidates
Beatles
(Book)
The
Beatles
(Music
Group)
Beatle
(Animal)
Beatle
(Automobile)
How
to
iden3fy
the
right
“Beatle”?
Examine
the
context
(Contextual
Analysis)
55. Semantic
Search:
Example
(5)
55
EUCLID
–
Interac3on
with
Linked
Data
User
query
(NL)
“songs
wriZen
by
members
of
the
beatles”
En=ty
extrac=on:
song
member
(of)
wriZen
by
(the)
beatles
En=ty
mapping:
(the)
beatles
Contextual
Analysis
foaf:Agent
mo:composer
mo:Track
mo:
MusicAr3st
rdfs:subClassOf
mo:
MusicGroup
mo:member
rdfs:subClassOf
This
subgraph
is
part
of
the
query
The
Beatles
(Music
Group)
dbpedia:
The_Beatles
En=ty
mapping:
56. Semantic
Search:
Example
(6)
56
EUCLID
–
Interac3on
with
Linked
Data
User
query
(NL)
“songs
wriZen
by
members
of
the
beatles”
En=ty
extrac=on:
song
member
(of)
wriZen
by
(the)
beatles
?y
Mo:Track
?x
mo:composer
a
dbpedia:
The_Beatles
mo:member
Results
(I
want
to)
Come
Home
Angel
in
Disguise
Another
Day
…
Answers
presented
to
the
user
The
results
could
be
ranked
Query
foaf:Agent
a
57. Semantic
Search
• Aims
at
understanding
the
meaning
of
the
resources
specified
in
the
query
• Different
approaches
to
exploit
seman3cs:
• Query
expansion
using
ontologies
Since
ontologies
represent
knowledge
about
specific
domains,
they
can
be
used
to
expand
the
query
by
incorpora3ng
related
ontology
terms
into
the
query.
• Contextual
analysis
In
LD,
this
approach
may
explore
the
resources
specified
in
the
query
and
their
adjacent
nodes
in
the
RDF
graph.
Mainly
applied
to
disambiguate
query
terms.
• Reasoning
In
some
cases,
the
answer
to
a
specific
query
is
not
explicitly
contained
in
the
data,
but
it
can
be
computed
by
using
reasoning
methods.
57
EUCLID
–
Interac3on
with
Linked
Data
58. Semantic
Search
&
Linked
Data
58
EUCLID
–
Interac3on
with
Linked
Data
Component
Seman=c
search
SPARQL
query
Keyword
or
NL
/
concept
matching
Performs
en3ty
extrac3on
and
matching
to
formal
concepts
Not
supported
Fuzzy
concepts/rela3on/
logics
Allows
the
applica3on
of
fuzzy
qualifiers
as
query
constrains
Not
supported
Graph
paZerns
Uses
the
context
and
other
seman3c
informa3on
to
locate
interes3ng
sub-‐graphs
Applies
paZern
matching
Path
discovery
Finds
new
interes=ng
links
that
may
lead
to
addi3onal
informa3on
Not
supported
Semantic
Search
vs.
SPARQL
query
59. Semantic
Search:
Google
(1)
59
EUCLID
–
Interac3on
with
Linked
Data
Input:
query
in
NL
Output:
List
of
answers
Google
performs
seman3c
search
on
certain
en33es
and
queries!
60. Semantic
Search:
Google
(2)
60
EUCLID
–
Interac3on
with
Linked
Data
Input:
ques3on
in
NL
Output:
List
of
web
pages
ranked
using
the
algorithm
Google
PageRank
to
display
the
most
relevant
pages
first
61. Semantic
Search:
DuckDuckGo
(1)
61
EUCLID
–
Interac3on
with
Linked
Data
Input:
ques3on
in
NL
Output:
List
of
answers
62. Semantic
Search:
DuckDuckGo
(2)
62
EUCLID
–
Interac3on
with
Linked
Data
Performs
disambigua=on
of
the
query
terms.
The
45
sugges=ons
are
grouped
by
classes
according
to
their
corresponding
knowledge
domain:
This
approach
is
denominated
Faceted
Search
63. Faceted
Search:
Example
Information
Workbench:
Searching
for
artists
in
categories
63
EUCLID
–
Interac3on
with
Linked
Data
Facet
Facet
Facet
Source:
hZp://musicbrainz.fluidops.net/resource/mo:MusicAr3st?view=pivot
Depic3ons
of
ar3sts
64. Faceted
Search
• Facets
=
proper3es
• Suitable
for
browsing
mul=-‐dimensional
taxonomies
based
on
the
search
aZributes
• Allows
user
to
explore
the
data:
• User
submits
a
(keyword)
query
• Faceted
system
dynamically
iden3fies
the
relevant
facets
(proper3es)
for
the
given
query
and
the
constrains
(values
of
those
proper3es),
and
display
the
search
results
• User
may
“drill
down”
by
selec3ng
specific
constrains
to
the
search
results
• Informa3on
can
be
accessed
and
ranked
in
mul3ple
ways
64
EUCLID
–
Interac3on
with
Linked
Data
65. Faceted
Search
(2)
Challenges
for
supporting
Faceted
Search
• Iden3fying
which
facets
to
surface:
• In
heterogeneous
datasets,
data
entries
may
have
different
facets
• Dynamically
iden3fy
the
most
appropriate
facets
for
each
query
• Ordering
the
facets
depending
on
the
relevance
to
the
query
• Compu3ng
previews:
• Accurately
predic3ng
counts,
without
examining
all
the
results
• Offering
facet
preview
to
give
users
an
idea
of
what
to
expect
65
EUCLID
–
Interac3on
with
Linked
Data
Source:
Teevan
,
J.,
Dumais,
S.,
GuZ.
Z.
Challenges
for
Suppor3ng
Faceted
Search
in
Large,
Heterogeneous
Corpora
like
the
Web
66. Faceted
Search:
LD
Example
(1)
FacetedDBLP
• Retrieves
informa3on
from
the
DBLP
collec=on
• Shows
the
result
set
with
different
facets:
• Publica3on
years
• Authors
• Conferences
• It
is
implemented
upon
the
DBLP++
dataset
(enhancement
of
DBLP
including
addi3onal
keywords
and
abstracts):
• DBLP
++
is
stored
in
a
MySQL
database
• Uses
D2R
server
to
consume
RDF
triples
66
EUCLID
–
Interac3on
with
Linked
Data
67. Faceted
Search:
LD
Example
(2)
67
EUCLID
–
Interac3on
with
Linked
Data
Input:
“crowdsourcing”
Facets
485
results
FacetedDBLP
68. Classification
of
Search
Engines
68
EUCLID
–
Interac3on
with
Linked
Data
Seman=c
Search
Systems
Faceted
Search
Systems
Google
(GKG)
Bing
KIM
sig.ma
LOD
cloud
cache
/facet
Longwell
mSpace
Exhibit
(SIMILE)
PoolParty
Seman3c
Search
Server
DuckDuckGo
Hakia
SenseBot
PowerSet
DeepDive
Kosmix
Fac3bles
Lexxe
Informa3on
Workbench
69. Searching
for
Semantic
Data
69
EUCLID
–
Interac3on
with
Linked
Data
Search
for
• Ontologies
• Vocabularies
• RDF
documents
70. Semantic
Data
Search
Engines
(1)
EUCLID
–
Interac3on
with
Linked
Data
70
Searching
for
ontologies
Swoogle
hZp://kmi-‐web05.open.ac.uk/WatsonWUI
hZp://swoogle.umbc.edu
Watson
Keyword
search
Keyword
search
71. Semantic
Data
Search
Engines
(2)
Searching
for
vocabularies:
LOV
Portal
• Allows
to
search
proper=es,
classes
or
vocabularies
in
the
Linked
Open
Vocabulary
(LOV)
catalog
• The
LOV
search
engine
implement
faceted
search
on:
• The
knowledge
domain
• The
role
of
the
resource
matched
from
the
input
query
• The
vocabulary
containing
the
resource
• Results
are
ranked
according
to
a
score
considering:
• Relevancy
to
the
query
(string)
• Element
labels
matched
importance
• Number
of
LOV
vocabularies
that
refer
to
the
element
71
EUCLID
–
Interac3on
with
Linked
Data
72. Semantic
Data
Search
Engines
(3)
72
EUCLID
–
Interac3on
with
Linked
Data
Facets
84
results
Input:
“ar3st”
CH
3
Searching
for
vocabularies:
LOV
Portal
73. Semantic
Data
Search
Engines
(4)
EUCLID
–
Interac3on
with
Linked
Data
73
Searching
for
documents
hZp://swse.deri.org
hZp://sindice.com
Seman3c
Web
Search
Engine
Sindice
74. METHODS
FOR
LINKED
DATA
ANALYSIS
EUCLID
–
Interac3on
with
Linked
Data
74
75. Features
of
Data
Analysis
75
EUCLID
–
Interac3on
with
Linked
Data
Sta3s3cal
analysis
• Allows
describing
the
data
via
Exploratory
Data
Analysis
(EDA)
methods
• Includes
sta3s3cal
inference
and
predic3on
Data
aggrega3on
&
filtering
• One
of
the
first
steps
in
data
analysis
is
pre-‐processing
in
order
to
select
the
appropriate
data
to
study
Visualiza=on
techniques
can
be
built
on
top
of
these
as
part
of
data
analysis
Machine
learning
• Focuses
on
predic3on
•
Combines
Ar3ficial
Intelligence
and
Sta3s3cs
•
Includes
supervised
and
unsupervised
learning
(not
covered
in
this
course)
76. LD
Data
Aggregation
&
Filtering
EUCLID
–
Interac3on
with
Linked
Data
76
• Data
aggrega3on
refers
to
merging/summarizing
several
values
into
a
single
a
one
• Filtering
allows
retrieving
relevant
data
proper3es
and
selec3ng
a
par3cular
range
of
data
values
• SPARQL
is
able
to
perform
these
features
via
SELECT
queries
as
follows:
Features
SPARQL
capabili=es
Aggrega3on
Combining
aggregate
func3ons
(COUNT,
SUM,
AVG,
…
)
and
GROUP
BY
operator
Filtering
Combining
projec3on,
FILTER
and
HAVING
operators
77. LD
Statistical
Analysis
EUCLID
–
Interac3on
with
Linked
Data
77
• Sta3s3cal
analysis
supports
descrip=ve
and
predic=ve
opera3ons
• SPARQL
supports
some
descrip=ve
opera=ons
(average,
maximum,
minimum)
but
does
not
offer
more
sophis3cated
sta3s3cal
features
like:
• Fijng
distribu3ons
• Linear
regressions
•
Analysis
of
variance
• …
• Some
approaches
are
able
to
consume
data
retrieved
from
SPARQL
endpoints:
–
“R
for
SPARQL”
by
Willen
Robert
van
Hage
&
Tomi
Kauppinen
– “Performing
Sta,s,cal
Methods
on
Linked
Data”
by
Zapilko
&
Mathiak
78. R
–
Statistical
Computing
EUCLID
–
Interac3on
with
Linked
Data
78
• R
is
a
language
and
environment
for
sta=s=cal
compu=ng
• R
provides
a
wide
variety
of
sta=s=cal
and
graphical
techniques
• Linear
and
nonlinear
modeling
• Classical
sta3s3cal
tests
• Time-‐series
analysis
• Classifica3on
(Machine
Learning)
• Clustering
(Machine
Learning)
• Extensible
with
further
func3onali3es
• R
is
available
as
Free
So_ware
(under
the
terms
of
the
GNU
general
public
license)
80. R
for
SPARQL
EUCLID
–
Interac3on
with
Linked
Data
80
• The
R
for
SPARQL
Package
enables
to:
• Connect
a
SPARQL
endpoint
over
HTTP
• Pose
a
SELECT
query
or
an
UPDATE
opera3on
(LOAD,
INSERT,
DELETE)
• If
given
a
SELECT
query,
it
returns
the
results
as
a
data
frame
• The
results
can
directly
be
mapped
and
visualized
• Posing
requests:
• If
the
parameter
query
is
given,
it
is
assumed
that
the
input
is
a
SELECT
query
and
a
GET
request
will
be
performed
to
get
the
results
from
the
URL
of
the
endpoint
• If
the
parameter
update
is
given,
it
is
assumed
that
the
input
is
an
UPDATE
opera3on
and
a
POST
request
will
be
submit
to
the
URL
of
the
endpoint.
Nothing
is
returned
Source:
hZp://linkedscience.org/tools/sparql-‐package-‐for-‐r/
81. R
for
SPARQL:
Example
(1)
EUCLID
–
Interac3on
with
Linked
Data
81
1.
Download
the
R
package
and
load
it:
• library(SPARQL)
• Library(sp)
#user
for
plotting
spatial
data
2.
Define
the
endpoint
with
the
triples
• endpoint
=
"http://spatial.linkedscience.org/sparql"
3.
Define
the
query
• q
=
"SELECT
?cell
?row
?col
?polygon
?DEFOR_2002
WHERE
{
?cell
a
<http://linkedscience.org/lsv/ns#Item>
;
<http://spatial.linkedscience.org/context/amazon/Lin>
?row
;
<http://spatial.linkedscience.org/context/amazon/Col>
?col;
<http://observedchange.com/tisc/ns#geometry>
?polygon
.
<http://spatial.linkedscience.org/context/amazon/
DEFOR_2002>
?DEFOR_2002
.
}"
Source:
hZp://linkedscience.org/tools/sparql-‐package-‐for-‐r
82. R
for
SPARQL:
Example
(2)
EUCLID
–
Interac3on
with
Linked
Data
82
4.
Link
the
result
to
an
object
• res
<-‐
SPARQL(endpoint,q)$results
5.
Handling
the
results
• res$row
<-‐
-‐res$row
• coordinates(res)
<-‐
~col
-‐
row
6.
Chose
the
graphical
format
and
plot
the
results
• spplot(res,"DEFOR_2002",col.regions=rev(heat.colors(
17))[-‐1],
at=(0:16)/100,
main="relative
deforestation
per
pixel
during
2002")
Source:
hZp://linkedscience.org/tools/sparql-‐package-‐for-‐r
83. R
for
SPARQL:
Example
(3)
EUCLID
–
Interac3on
with
Linked
Data
83
Source:
hZp://linkedscience.org/tools/sparql-‐package-‐for-‐r
84. Machine
Learning
EUCLID
–
Interac3on
with
Linked
Data
84
• Machine
Learning
techniques
allow
to
extract
interes3ng
informa3on
from
data
sources,
and
can
be
used
to
discover
hidden
paUerns
within
datasets
by
generalizing
from
examples
• Different
ML
approaches
can
be
applied:
• Clustering:
groups
similar
data
into
data
par33ons
called
clusters
• Associa=on
rule
learning:
discovers
rela3ons
between
variables
• Decision
tree
learning:
analyses
observa3ons
to
build
a
predic3ve
model
represented
as
a
tree
• Many
others
…
• Weka
is
a
Data
Mining
framework
commonly
used
to
apply
ML
on
tabular
data:
– www.cs.waikato.ac.nz/ml/weka
85. Machine
Learning
on
LD
EUCLID
–
Interac3on
with
Linked
Data
85
Challenges
for
applying
Machine
Learning
on
LD
• LD
heterogeneity
introduces
noise
to
the
data:
– Same
LD
resources,
different
URIs
– Predicates
with
similar
seman3cs,
but
different
constraints
• The
data
is
not
independent
and
iden3cally
distributed
(iid):
– It
does
not
consist
of
only
one
type
of
objects
– The
en33es
are
related
to
each
other
• LD
rarely
contains
nega=ve
examples
needed
for
ML
algorithms:
– For
example,
owl:differentFrom
Source
hZp://www.cip.ifi.lmu.de/~nickel/iswc2012-‐slides
86. Applications
of
Machine
Learning
on
LD
EUCLID
–
Interac3on
with
Linked
Data
86
• Node
ranking:
– Ranking
nodes
according
to
their
relevance
for
a
query
• Link
predic=on:
– Infer
edges
between
LD
resources
– Predict
the
new
edges
that
will
be
added
to
the
RDF
graph
• En=ty
resolu=on:
– Determine
whether
two
URIs
correspond
to
the
same
real-‐
world
object
• Taxonomy
learning:
– Infer
taxonomies
or
concept
hierarchies
from
a
given
vocabulary
or
ontology
87. Summary
EUCLID
–
Interac3on
with
Linked
Data
87
• Linked
Data
visualiza3on
techniques:
• Visualiza3ons
must
be
chosen
according
the
type
of
the
data
• Wide
variety
of
tools
suppor3ng
SPARQL
results’
visualiza=on
• Might
be
used
in
dashboards
for
suppor3ng
administra3ve
tasks
• Linked
Data
search
• Seman=c
search:
exploits
the
meaning
of
user
queries
(NL
or
set
of
keywords)
to
present
useful
results
• Faceted
search:
allows
browsing
mul3-‐dimensional
data
• Linked
Data
analysis:
• Includes
data
manipula3on
such
as
aggrega=on
&
filtering
• Applies
sta=s=cal
methods
to
get
a
beZer
understanding
of
the
data
• Machine
Learning
techniques
can
be
applied
for
predic3ve
analysis
• Visualiza=on
techniques
can
be
built
on
top
of
the
previous
features
88. For
exercises,
quiz
and
further
material
visit
our
website:
EUCLID
-‐
Providing
Linked
Data
88
@euclid_project
euclidproject
euclidproject
http://www.euclid-‐project.eu
Other
channels:
eBook
Course
