Slides from my keynote at the 1st International Workshop on Semantic Music and Media (SMAM2013) http://iswc2013.semanticweb.org/content/smam-2013

1. Mapping,
Interlinking
and
Exposing
MusicBrainz
as
Linked
Data
1st
Interna*onal
Workshop
on
Seman*c
Music
and
Media
(SMAM2013)
Sydney,
Oct
21,
2013
Peter
Haase

2. What
this
talk
is
about
A
Linked
Data
Perspec=ve
worksOn
publishedTo
affiliation
affiliation (previous)
isAbout
builtWith
participatesIn
participatesIn

3. EUCLID:
EdUca=onal
Curriculum
for
the
usage
of
LinkedData
http://www.euclid-project.eu
Course
eBook
Other channels
@euclid_project
euclidproject
euclidproject

4. Analysis
&
Mining
Module
Visualiza*on
Module
RDFa
Data acquisition
LD Dataset
Access
Application
EUCLID
Scenario
SPARQL
Endpoint
Vocabulary
Mapping
Publishing
Interlinking
Physical
Wrapper
Streaming providers
Downloads
Musical Content
Cleansing
LD
Wrapper
R2R
Transf.
Integrated
Dataset
LD
Wrapper
RDF/
XML
Metadata
Other content

5. MusicBrainz
• MusicBrainz
is
an
open
music
encyclopedia
that
collects
music
metadata
and
makes
it
available
to
the
public.
• MusicBrainz
aims
to
be:
•
The
ul=mate
source
of
music
informa=on
by
allowing
anyone
to
contribute
and
releasing
the
data
under
open
licenses.
•
The
universal
lingua
franca
for
music
by
providing
a
reliable
and
unambiguous
form
of
music
iden*fica*on,
enabling
both
people
and
machines
to
have
meaningful
conversa*ons
about
music.
• Like
Wikipedia,
MusicBrainz
is
maintained
by
a
global
community
of
users
and
we
want
everyone
—
including
you
—
to
par*cipate
and
contribute.
• MusicBrainz
is
operated
by
the
MetaBrainz
Founda*on,
dedicated
to
keeping
MusicBrainz
free
and
open
source.

6. LD
Dataset
Access
Publishing
Rela=onal
Databases
as
RDF:
W3C
RDB2RDF
SPARQL
Endpoint
Publishing
Integrated
Data
in
Triplestore
Vocabulary
Mapping
Interlinking
R2RML
Engine
Cleansing
Task:
Publish
data
from
rela*onal
DBMS
as
Linked
Data
Approach:
map
from
rela*onal
schema
to
seman*c
vocabulary
with
R2RML
Publishing:
two
alterna*ves
–
Data
acquisi*on
•
•
Rela*onal
DBMS
Translate
SPARQL
into
SQL
on
the
fly
Batch
transform
data
into
RDF,
infer,
index
,
integrate
and
provide
SPARQL
access
in
a
triplestore

7. Publishing
MusicBrainz
h"ps://wiki.musicbrainz.org/Next_Genera;on_Schema
MusicBrainz
DB
h"p://musicontology.com
Music
Ontology
R2RML
Concrete
Example
Mapping
Table
Recording(gid,
length)
R2RML
Mapping
Ontology
concept
mo:recording

8. MusicBrainz
Next
Gen
Schema
ar=st
As
pre-­‐NGS,
but
further
a`ributes
ar=st_credit
Allows
joint
credit
release_group
Cf.
‘album’
versus:
• work
release
• track
medium
• tracklist
• recording
https://wiki.musicbrainz.org/Next_Generation_Schema

9. Music
Ontology
OWL
ontology
with
following
core
concepts
(classes)
and
rela*onships
(proper*es):
Source: http://musicontology.com

10. R2RML
Class
Mapping
Mapping
tables
to
classes
is
‘easy’:
lb:Artist
a
rr:TriplesMap
;
rr:logicalTable
[rr:tableName
"artist"]
;
rr:subjectMap
[rr:class
mo:MusicArtist
;
rr:template
"http://musicbrainz.org/artist/{gid}#_"]
;
rr:predicateObjectMap
[rr:predicate
mo:musicbrainz_guid
;
rr:objectMap
[rr:column
"gid"
;
rr:datatype
xsd:string]]
.

11. R2RML
Property
Mapping
Mapping
columns
to
proper*es
can
be
easy:
lb:artist_name
a
rr:TriplesMap
;
rr:logicalTable
[rr:sqlQuery
"""SELECT
artist.gid,
artist_name.name
FROM
artist
INNER
JOIN
artist_name
ON
artist.name
=
artist_name.id"""]
;
rr:subjectMap
[rr:template
"http://musicbrainz.org/artist/{gid}#_"]
;
rr:predicateObjectMap
[rr:predicate
foaf:name
;
rr:objectMap
[rr:column
"name"]]
.

12. NGS
Advanced
Rela=ons
Major
en**es
(Ar*st,
Release
Group,
Track,
etc.)
plus
URL
are
paired
(l_ar*st_ar*st)
Each
pairing
of
instances
refers
to
a
Link
Links
have
types
(cf.
RDF
proper*es)
and
a`ributes
http://wiki.musicbrainz.org/Advanced_Relationship

13. R2RML
Mapping
Editor
R2RML: Expose data from
relational DBMS as RDF /
via SPARQL Endpoint
Problem: R2RML
Mappings are
hard to create
R2RML
Engine
R2RML
Mappings
R2RML
Edi*ng
Made
Easy!
Hides
vocabulary
intricacies
from
end-­‐user
Access
to
metadata
about
rela*onal
databases
Preview
of
generated
triples
and
SQL
queries
Very
expressive
(Supports
most
of
R2RML)
SPARQL
Endpoint
Rela*onal
Database
See our R2RML Mapping Editor in the ISWC Demo Session on Wednesday!

14. Scale
MusicBrainz
RDF
derived
via
R2RML:
150M
Triples
lb:artist_member
a
rr:TriplesMap
;
rr:logicalTable
[rr:sqlQuery
"""SELECT
a1.gid,
a2.gid
AS
band
FROM
artist
a1
INNER
JOIN
l_artist_artist
ON
a1.id
=
l_artist_artist.entity0
INNER
JOIN
link
ON
l_artist_artist.link
=
link.id
INNER
JOIN
link_type
ON
link_type
=
link_type.id
INNER
JOIN
artist
a2
on
l_artist_artist.entity1
=
a2.id
WHERE
link_type.gid='5be4c609-­‐9afa-­‐4ea0-­‐910b-­‐12ffb71e3821'"""]
;
rr:subjectMap
[rr:template
"http://musicbrainz.org/artist/{gid}
#_"]
;
rr:predicateObjectMap
[rr:predicate
mo:member_of
;
rr:objectMap
[rr:template
"http://musicbrainz.org/artist/{band}
#_"
;
rr:termType
rr:IRI]]
.

15. Some
Sta=s=cs
–
RDF
Dump
(Lead) Table
area
artist
dbpedia
label
medium
recording
release_group
release
track
work
Triples
59798
36868228
172017
201832
18069143
11400354
3050818
9764887
75506495
1728955
156822527
Time (s)
2
423
13
3
163
209
31
151
794
20
1809

16. Informa=on
Workbench
PlaGorm
for
Linked
Data
Applica=ons
§ Seman*cs-­‐
&
Linked
Data-­‐based
integra=on
of
private
and
public
data
sources
based
on
data
providers
•
•
•
Generic
and
specific
providers
for
various
data
formats
and
sources
Supports
established
mapping
frameworks
(e.g.
R2RML,
SILK,
…)
Named
graphs
for
managing
contexts
and
provenance
§ Intelligent
Data
Access
and
Analy=cs
•
•
•
Flexible
self-­‐service
UI
Visualiza*on,
explora*on,
dashboarding
and
repor*ng
Seman*c
search
§ Collabora=on
and
knowledge
management
•
•
Cura*on
&
authoring
Collabora*ve
workflows
§
Open
standards
and
technologies
•
•
•
Seman*c
Wiki
based
frontend
(Using
SMW
Syntax)
Suppor*ng
W3C
standards
(OWL,
RDF,
SPARQL,,
…)
Community
Edi*on
(Open
Source)
+
Enterprise
Edi*on
(Commercial)

17. Realiza=on
within
the
Informa=on
Workbench
Architecture
Customized
applica*on
solu*ons
Reusable
UI
and
data
integra*on
components
Data
storage
and
management
plajorm
External
resources
to
reuse
data
and
create
mashups

18. The
“MusicBrainz
Explorer”
Applica=on
Music Ontology
Ontology
Data
R2RML
Data Providers
Templates
Widgets

19. Ontology
as
a
“Structural
Backbone”
Resource
page
Defining
UI
structure
Resource
page
mo:Track
mo:Ar=st
Defining
data
structure
rdf:type
Yesterday
UI
templates
Template:
…
Template:mo:Track
Template:mo:Ar=st
Ontology
(RDFS/OWL)
rdf:type
The_Beatles
RDF
Data
Graph

20. Information
Workbench:
Browsing
a
Music
Artist

21. Information
Workbench:
Visualization
techniques

22. Naviga=on
Through
the
Data
Source: http://musicbrainz.fluidops.net/resource/Analytical5

23. SPARQL
visualization
Top ten The Beatles releases according to the sum of
track durations in minutes
SPARQL
Query
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
Result
set

24. SPARQL
visualization
Top ten The Beatles releases according to the sum of track durations
in minutes
Widget
{{#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’}}
Visualization:
Bar
chart

25. Information
Workbench:
SPARQL
visualization
Top ten The Beatles releases according to the sum of track
durations in minutes
Other
visualiza*ons
of
the
same
result
set
…
Line
chart:
Pie
chart:

26. Automated
Widget
Suggestion
1
Table
Pivot
view
Bar chart
Line chart
Pie chart
2
Select a suggested visualization
3
Visualization
automatically
built

27. Try
it
out!
R2RML
Mappings
•
h`ps://github.com/LinkedBrainz/MusicBrainz-­‐R2RML
MusicBrainz
RDF
Dump
•
h`p://mbsandbox.org/~barry/
MusicBrainz
Linked
Data
Demo
system
• h`p://musicbrainz.fluidops.net/
Informa*on
Workbench
•
h`p://www.fluidops.com/informa*on-­‐workbench/
Euclid
Project
•
h`p://euclid-­‐project.eu/

28. Acknowledgements
The
Euclid
Project
Barry
Norton
Michael
Meier
Andriy
Nikolov
Yves
Raimond
Kurt
Jacobson
Thomas
Gaengler
Juan
Sequeda
Simon
Dixon
(in
no
par;cular
order)

29. Thank
you!
Contact
Peter
Haase
fluid
Opera*ons
AG
Altro`str.
31
Walldorf
Germany
+49
(0)
6227
358087-­‐0
www.fluidops.com
peter.haase@fluidOps.com