ESWC 2015 - EU Networking Session @ Portoroz, June 3th 2015

1. ESWC
2015:
EU
Project
Networking
Session
3rd
June
2015
(14:00
–
17:00)
Room
Adria
I/II,
Grand
Hotel
Bernardin,
Portoroz

2. EU
Project
Networking
Session
2015:
Organizers
Frédérique
Segond
(Viseo,
Grenoble,
France)
Sergio
Consoli
(STLab
ISTC-­‐CNR,
Italy)
Jun
Zhao
(Lancaster
University,
UK)
Erik
Mannens
(iMinds-­‐Ghent
Univ.-­‐MMLab,
Be)

3. EU
Project
Networking
Session
2015:
Purpose
To
provide
EU
projects
with
the
ability
q to
connect
with
each
other
and
engage
in
discussions
about
their
respecNve
research
and
development,
q to
establish
opportuni2es
for
knowledge
and
technology
sharing,
q to
iden2fy
complementary
ac2vi2es
and
goals
which
can
form
the
basis
for
§ future
collaboraNons,
§ research
proposals,
§ researcher
exchange,
and/or
§ joint
parNcipaNon
at
events/iniNaNves

4. EU
Project
Networking
Session
2015:
Program
14:05-­‐14:15
à
Session
Opening
14:15-­‐14:50
à
Madness-­‐Presenta2ons
15:00-­‐16:00
à
Projects’
Posters
16:00-­‐17:00
à
Thema2c
Tables
16:55-­‐17:00
à
Session
Wrap-­‐Up

5. EU
Project
Networking
Session
2015:
One
Minute
Madness
14:15-­‐14:50
à
Madness-­‐Presenta2ons
q You
have
two
minutes
to
present
your
project!
Strict
6me
window!
q Flash
highlight
of
the
project
q Just
answer
the
Five
Ws:
WHO;
WHAT;
WHEN;
WHERE;
WHY
q Please
follow
the
order
list
we
have
given
you,
and
be
ready
when
it
is
your
turn!

6. 1
LEO

7. Linked Open Earth Observation Data for
Precision Farming (LEO, http://
www.linkedeodata.eu/)
Manolis Koubarakis
National and Kapodistrian
University of Athens

8. The project LEO
04/06/15 Manolis Koubarakis 8
• LEO studies the life cycle of linked EO data and develops tools
that support it. The results of the project are the following:
– GeoTriples: a tool that extracts linked geospatial data from their
native formats (e.g., shapefiles) and transforms them into RDF.
– Extension of Silk: techniques for interlinking linked geospatial and
temporal data.
– LEO DSE, LEODroid, Sextant: tools for searching, visualizing and
exploring linked EO data and linked geospatial data. Developed for
mobile platforms (Android).
– LEOpatra: a precision farming application that exploits linked
geospatial data.

9. 2
Open
Data
Monitor

10. OpenDataMonitor
FP7-ICT-2013.4.3 SME initiative on analytics
Project number: 611988
OpenDataMonitor
Monitoring, analysis and visualisation of open data catalogues, hubs and repositories
What we aim to do
• Gain an overview of the open data ecosystem
• Analyse and visualise data catalogues using
innovative technology
How we do it
• Harvest and harmonize multilingual metadata
from data catalogues
• Make information available at municipal,
national, and pan-European levels
project.opendatamonitor.eu

11. Open Data Monitor: opendatamonitor.eu

12. 3
MARIO

13. MARIO: Managing active and healthy Aging with use of caRing servIce rObots
Diego Reforgiato Recupero, Aldo Gangemi, Misael Mongiovi, Stefano Nolfi, Andrea G. Nuzzolese, Valentina
Presutti, Massimiliano Raciti, Thomas Messervey, Dympna Casey, Vincent Dupourque, Geoff Pegman,
Alexandros Gkiokas, Andy Bleaden, Antonio Greco, Christos Kouroupetroglou, Siegfried Handschuh
www.mario-project.eu
Started in February 2015

14. Robot semantics based on Semantic
Web practices and technologies: Linked
Data principles, RDF, SPARQL, RIF.
Mario Ontology Network (MON) will
reuse and extend the Ontologies for
Robotics and Automation. MON will
evolve over time by integrating
ontologies emerging from interaction
with assisted humans, sensors or with
other robots.
Semantic Web-based machine reading/
listening in robots. FRED, will be
extended and improved for dealing with
c o n t e x t - b a s e d g r o u n d i n g a n d
interpretation of natural language input.
Ability to advance robot knowledge by
learning new ontology patterns from its
experience with users and the robot
network in place. New emerging patterns
and expressions are fed back to the
robot’s cognitive system in order to
address emotional needs of end users in
compliance with the social and behavioral
objectives of MARIO.
Robot social skills: a sentiment analysis
framework based on deep parsing of
natural language and supported by MON
will deal with moods and expression
recognition providing robots.
“ E n t i t y - c e n t r i c ” k n o w l e d g e
management: each entity and its
relations have a public identity that
provides a first “grounding” to the
knowledge used by robots. Such identity
is given by resolvable URIs that use
simple Web and Internet protocols to
provide useful knowledge as a
representative of real world entities.
KOMPAI PLATFORM
from Robosoft

15. 4
PHEME

17. 5
EGI-­‐ENGAGE

18. EGI
ENGAGE
DARIAH
competence
centre
what
?
establish
science-­‐oriented
competence
centre
providing
support
for
researchers
within
the
humaniNes,
arts
and
social
sciences

19. EGI
ENGAGE
DARIAH
competence
centre
what
?
establish
science-­‐oriented
competence
centre
providing
support
for
researchers
within
the
humaniNes,
arts
and
social
sciences
à
further
use
of
infrastructures
in
the
arts
&
humaniNes
à
support
development
of
&
research
in
digital
arts
&
humaniNes

20. EGI
ENGAGE
DARIAH
competence
centre
how
?
DARIAH
arts
&
humaniNes
use
case
(e-­‐infrastructure,
educaNon+training,
use
cases,
data)

21. EGI
ENGAGE
DARIAH
competence
centre
how
?
DARIAH
arts
&
humaniNes
use
case
(e-­‐infrastructure,
educaNon+training,
use
cases,
data)
language
resources,
AT
varieNes

22. EGI
ENGAGE
DARIAH
competence
centre
how
?
DARIAH
arts
&
humaniNes
use
case
(e-­‐infrastructure,
educaNon+training,
use
cases,
data)
language
resources,
AT
varieNes
à
join
in
to
increase
infrastructures
for
the
arts
&
humaniNes!
eveline.wandl-­‐vogt@oeaw.ac.at
[disseminaNon
manager
@
DARIAH
CC]

23. 6
x-­‐LIME

24. LiMexLiMe – crossLingual crossMedia knowledge extraction
supported by
mainstream/
professionally
produced
social/
user generated
social
video,
social
photos
audio
from TV
audio
from
social
media
tweets,
blogs,
comments
reviews
TV,
videos
photos,
images
news,
annotatio
nof audio/
video
visual
auditiv
textual
Real-time
content-based
augmentation
of Live-TV
Real-time
Semantic
Search across
modalities

25. LiMexLiMe – crossLingual crossMedia knowledge extraction
Visit us at
www.xlime.eu XLiMe project
supported by

26. 7
Facts4Workers

27. 03.06.2015 FACTS4WORKERS – 27
Start: 1.1.2015
Duration: 4 years
Partners:

28. • We create Smart factories for the worker.
• The worker is the smartest part of a smart factory, use his creativity
and experience
• Use semantics to create workflows. We need systems which can
adapt to the worker, not the other way around.
• Our semantic workflow engine combines and calls different
resources to fulfill a user’s goal. It is highly adaptive and can react to
knowledge entered by the user.
• Humans and machines can work together!
03.06.2015 FACTS4WORKERS – 28

29. 8
COMSODE

30. The COMSODE project has received funding from the Seventh Framework Programme
of the European Union in the grant agreement number 611358.
COMSODE @ ESWC2015
maurino@disco.unimib.it

31. The COMSODE project
• Develop a quality aware
open data pubblication
platfrom (open data node)
• Provide a quality aware
methodology for selecting
and publishig dataset

33. 9
ProaSense

34. ProaSense:
The
ProacNve
Sensing
Enterprise
• WHAT:
– Support
transiNon
from
Sensing
to
ProacOve
Sensing
Enterprises
– Go
from
search,
sensing,
anOcipaOng,
to
proacOng.
– Knowing
"what
might
happen"
and
doing
"what
should
be
the
best
acOon"
• HOW:
– Observe-­‐Orient-­‐Decide-­‐Act
loop
of
situaNonal
awareness
– Parallel
and
distributed
processing
of
high-­‐velocity
data
from
IoT
– SemanNc-­‐descripNon-­‐supported
development
of
proacNve
real-­‐Nme
applicaNons
• WHEN:
3-­‐years
project
started
in
November
2013
• WHO:

35. ProaSense:
Use
Case
1:
HELLA
• GOAL:
– Building
millions
of
lamps
annually
– Reducing
scrap-­‐rate
and
down-­‐Nme
• CHALLENGES:
– Heterogeneous
event
schemas
– …messaging
protocols
– …event
processors
• TALK
TO
ME
ABOUT:
– How
to
make
use
of
semanNcs
for
designing
stream
processing
pipelines?
– How
to
scale
up
stream
processing?
– How
to
integrate
data
from
IoT
devices
for
decision
support?

36. 10
MixedEmoNons

37. ObjecOves
-­‐
large-­‐scale
emoNon
analysis
and
fusion
-­‐
heterogeneous
data:
mulNlingual
text,
speech,
image,
video,
social
media
-­‐
semanNc-­‐level
informaNon
aggregaNon
and
integraNon
-­‐
robust
extracNon
of
social
semanNc
knowledge
graphs
for
emoNon
analysis
Pilots
Social
TV,
enriching
TV
shows
with
social
media
emoNons,
for
editors
and
TV
audience
Brand
ReputaOon
Mgmt,
tracking
emoNons
around
brands
menNons
in
social
media,
news,
TV
etc.
Call
Centres,
analysing
consumer
and
call
centre
operator
emoNons
MixedEmoNons
Social
SemanNc
EmoNon
Analysis
for
InnovaNve
MulNlingual
Big
Data
AnalyNcs
Markets
Gabriela
Vulcu,
Insight
Centre
for
Data
AnalyNcs,
NaNonal
University
of
Ireland,
Galway

38. MixedEmoOons
PlaYorm
MixedEmoNons
Social
SemanNc
EmoNon
Analysis
for
InnovaNve
MulNlingual
Big
Data
AnalyNcs
Markets
Gabriela
Vulcu,
Insight
Centre
for
Data
AnalyNcs,
NaNonal
University
of
Ireland,
Galway

39. 11
Dem@Care

40. Demen%a Ambient Care: Mul%-­‐Sensing
Monitoring for Intelligent Remote
Management and Decision Support
Georgios
Meditskos,
Ioannis
Kompatsiaris

41. Dem@Care Facts and Figures
• CollaboraNve
Project
funded
under
FP7
ICT
Call
8
• ObjecNve
ICT-­‐2011.5.1
“Personal
Health
Systems
for
Remote
Management
of
Diseases,
Treatment
and
RehabilitaNon”
• DuraNon:
November
2011
–
November
2015
• Budget:
~11
millions
• ConsorNum
of
11
partners

42. The Dem@Care Vision
• Plaqorm
for
the
remote
care
of
people
with
demenNa
• Provide
personalized
care
opNons
• Support
individuals
in
their
daily
life
• Help
clinicians
and
informal
caregivers
provide
beser
feedback
• This
is
achieved
through
mulN-­‐sensor
monitoring
and
analysis
• Key
Features
• ConNnuous
sensor-­‐based
monitoring
and
analysis
of
various
modaliNes
• SemanNc
integraNon,
analysis
and
interpretaNon
of
sensor
measurements
• Personalized
high-­‐level
descripNons
of
the
person’s
condiNon
and
its
evoluNon
• Easy-­‐to-­‐use
interfaces
for
the
people
with
demenNa
and
their
caregivers/clinicians
Context
Descriptor
dependency
[allValuesFrom]
dul:Situation
dul:isSettingFor
describes
[exactly
1]
dul:isSettingFor
leo:Event
em:Event
em:Observationem:Activity
em:Posture
em:Object
em:Action
em:Location
time:TemporalEntity
dul:Agent
leo:involvedAgent

43. Seman%c Web Technologies in
Dem@Care

44. 12
FREME

45. THE
FREME
PROJECT
• Two
year
H2020
InnovaNon
acNon;
start
February
2015
• Industry
partners
leading
four
business
cases
around
digital
content
and
(linked)
data
• Technology
development
bridging
language
and
data
• Outreach
and
business
modelling
demonstraNng
moneNzaNon
of
the
mulNlingual
data
value
chain

47. 13
BYTE

48. Big data roadmap and cross-disciplinary community for addressing
societal externalities
@BYTE_E www.byte-project.eu
BYTE
aims
to
assist
European
science
and
industry
to
gain
a
greater
share
of
the
big
data
market
by
2020.
In
order
to
do
so,
BYTE
will
idenNfy
measures
that
will
help
big
data
users
to
capture
and
amplify
the
posiOve
externaliOes
associated
with
big
data
(e.g.,
efficiency,
innovaNon,
data
sharing,
etc.)
in
a
manner
that
enables
them
to
diminish
the
associated
negaOve
externaliOes
(e.g.,
privacy,
data
protecNon,
discriminaNon,
etc.).
q
CoordinaNon
and
Support
AcNon
q
Mar
2014
–
Feb
2017
(36
months)
q
Funded
by
DG-­‐CNCT:
€2.25
million
q
Grant
agreement
no:
619551
q
11
partners
from
10
countries
Key
Outcomes
q Report
on
societal
externaliNes
associated
with
big
data
q Vision
for
big
data
in
Europe
q Policy
roadmap
q Research
roadmap
q Build
the
big
data
community
Achievements
of
the
1st
Year
of
work
q Understand
the
Big
data
ecosystem
q DefiniNons
q 10
Big
Data
IniNaNves
q Collect
posiNve
and
negaNve
externaliNes
of
big
data
q Case
studies
analysis
q 1)
environmental
data,
2)
crisis
informaNcs,
3)
transport
data,
4)
smart
ciNes
data,
5)
cultural
data,
6)
energy
data
and
7)
health
data.
q Semi-­‐structured
interviews
and
mulNdisciplinary
group
discussions

49. 14
ENeL
COST

50. EU Project Networking
Session
June, 3rd 2015, Portoroz

51. G Ontology LexicaW3C community group :
The core model of Ontolex;
Figure created by John P. McCrae
F Babelnet :Thinking lexicography
outside the box :partner of COST ENeL
Partnership
between
ENeL and
Linked Data
Initiative
http://www.lider-project.eu/
https://www.w3.org/community/ontolex/ http://babelnet.org/

52. 15
WDAqua
ITN

55. 16
LIDER

56. ESWC’15
EU
Project
Networking
session
03/06/2015
Portoroz
(Slovenia)
56
Jorge
Gracia
(UPM)
Language
resources
(lexicon,
corpora,...)
...
LLOD
generaNon
MulNlingual
content
metadata
LLOD
(Language
resources
as
Linked
Open
Data)
LOD-­‐aware
NLP
services
MulNmedia
MulNlingual
Content
Content
AnalyNcs
Content
AnalyNcs
consumers
Metadata
generaNon
Content
providers
Target
scenario
56

57. ESWC’15
EU
Project
Networking
session
03/06/2015
Portoroz
(Slovenia)
57
Jorge
Gracia
(UPM)
Reference
Architecture
Certification
Benchmarking & Validation
Discovery
LLD Linking
LLD Publishing
Metadata
Service Composition
LLD-aware Services
Licensing Provenance
Vocabularies Hosting Scalability Streaming Interoperability
GuidelinesandStandardization
Multilingual Data

58. EU
Project
Networking
Session
2015:
Thematic
Tables
16:00-­‐17:00
à
Thema2c
Tables
q The
organisa6on
of
the
tables
has
been
based
on
the
topics
of
each
project
q Please
volunteers
to
take
notes
J
q Table
1
:
Health,
EducaNon,
Arts
and
HumaniNes
§ 3,
5,
11,
15
q Table
2
:
(Linked)(Open)(Big)(Geo)
Data
§ 1,
2,
4,
13
q Table
3
:
MulNlingual
§ 6,
10,
14,
16
q Table
4
:
Industry
and
Business
§ 7,
9,
12,
8

59. EU
Project
Networking
Session
2015:
Session
Wrap-­‐Up
16:55-­‐17:00
à
Session
Wrap-­‐Up
q Generic
Findings
§ Beser
to
have
Round
Table
right
a{er
Minute
Madness
&
end
with
(well-­‐asended)
poster
session,
which
can
then
go
on
as
long
as
needed
(we
had
to
end
it
now)
§ There
was
also
a
lot
of
“networking”
beyond
the
EU-­‐projects
themselves
and
related
demo’s
were
given
on
several
tables
q Specific
Results
…
see
herea{er
Thank
you
very
much

60. EU
Project
Networking
Session
2015
–
Discussion
Minutes
Table
1:
Health,
HumaniNes,
Arts

61. DARIAH
and
EGI-­‐ENGAGE
• We
presented
the
new
collaboraNon
between
DARIAH
(hsps://www.dariah.eu/)
and
the
new
EGI-­‐Engage
(hsps://www.egi.eu/about/egi-­‐engage/).
• Discussion
at
the
poster
was
lively,
and
interest
in
sharing
data
in
the
social
science
was
shown
(Andrea
Maurino,
Project
COMSODE)
and
also
from
people
not
asending
the
session,
e.g.,
Frank
Michel
(Sophia-­‐
AnNpolis),
suggesNng
further
cooperaNon.
• A
special
topic
discussed
concerned
the
quality
of
the
data
to
be
processed
(Christophe
Lange,
Bonn)
• Interest
in
the
health
data
generated
by
Dem@Care
and
MARIO.

62. Dem@Care
-­‐
MARIO
• DemenNa-­‐related
domains
• Dem@Care
(FP7,
from
November
2011
to
November
2015)
• MARIO
(H2020,
from
February
2015
to
February
2018)
• Outcomes
from
Dem@Care
can
be
used
in
MARIO
as
starNng
points
– QuesNonnaires,
data,
privacy/ethical
guidelines,
acceptability
of
sensors/technologies
• MARIO’s
pilot
data
can
be
collected
and
sent
to
Dem@Care
to
be
processed
and
analyzed
by
the
exisNng
plaqorm/components

63. EU
Project
Networking
Session
2015
–
Discussion
Minutes
Table
2
&
3
:
(Linked)
(Open)
(Big)
(Geo)
(MulNlingual)
Data

64. ● MixedEmotions already working with LIDER;
interested in PHEME, XLIME, FREME
● LIDER interested in XLIME, MixedEmotions, ENeL
COST, FREME, WDAque ITN
● WDAqua ITN interested in PHEME, LIDER, ENeL
COST, COMSODE
● FREME interested in MixedEmtions, ENeL COST,
Open Data Monitor Project
● ProaSense discussed with PHEME (challenges in real-
time processes), MARIO, Dem@Care (data integration
and health care), Facts4Workers (benefits of
semantics in the industry)

65. ENeL COST:
Cooperation already established with LIDER.
To be extended with cooperation with FREME
(new project), especially on publishing
lexicographic data in RDF in the LOD (also with
the partner iMinds)
The topic of quality checks was here also
discussed (with Christophe Lange, Bonn).

66. ENeL COST:
W i t h t h e W 3 C C o m m u n i t y G r o u p
„Ontolex“ (supported by LIDER), we discussed
the possible representation of Gender issues in
the ontolex model (and extensions), as well as
the indication of the various temporal
information incuded in lexicographic resources.
With MixedEmotions we plan a cooperation
on the LOD/RDF representation of sentiment
words in the authorative dictionaries in the
ENeL network

67. EU Project Networking
Session 2015
–
Discussion Minutes
Table 4: Industry & Business

68. • InteresNng
contact
with
STI
/
BYTE
on
sharing
knowledge
for
Big
Data
Europe
• Temporal
context
has
great
value
in
analyzing
Big
Data.
A
feedback
loop
which
updates
a
limited,
but
easily
accessible,
LD
model
gives
similarity
to
the
Lambda
Architecture.
• PraNcal
use
of
conNnuous
event
processing
demonstrated
by
Leon
Derczynski
• "We
want
a
new
networking
session
in
a
few
months."
-­‐-­‐
Andrea
Maurino
• "We
could
use
Graphical
VisualisaNon
to
summarize
models"
-­‐-­‐
Aidan
Delaney

69. • "We
need
profiles
to
describe
Big
Data
streams"
–
abstartup
• Strabon
and
Marmo`a
can
be
used
as
temporal
database
• Reasoning
and
summarisaNon
is
a
problem
with
large
datasets.
Visual
and
formal
summaries
are
useful
for
different
cases.
• Temporality
is
important
and
a
near-­‐ubiquiNous
challenge,
in
different
forms.
Making
the
best
decision
as
you
can
is
possible
in
real-­‐Nme,
but
it
also
useful
to
be
able
to
rewind
and
determine
the
support
for
a
decision
that
was
made
in
the
past.
cf.
databases
Strabon
and
Marmo`a

70. • Reasoning
for
streamed
data
is
tough.
It
can
be
useful
and
so
it
is
useful
to
process
streams
using
summaries
of
representaNons,
to
keep
things
fast.
• InformaNon
is
volaNle
and
the
volaNlity
of
informaNon
can
indicate
reliability,
as
can
its
age.
However,
old
or
very
new
asserNons
are
not
necessarily
correct.
Reliability
scoring
likely
evolves
best
with
a
Bayesian
model.
• Event
Registry
is
an
enNty-­‐centric
event
exploraNon
tool,
from
xLime/xLike,
useful
to
other
partners
(with
links
to
Pheme).
(cf.
cola.js)

71. • TELIOS
describes
visualisaNon
of
temporal
changes
on
a
map,
over
Nme,
from
linked
data.
• The
WDAqua
ITN
looks
at
quesNon
answering
and
is
concerned
with
the
reliability
of
data
for
providing
support
in
answering,
and
is
interested
in
visits
to
and
from
other
insNtuNons
working
on
related
topics
(e.g.
Pheme's
veracity
challenges).