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ESWC	
  2015:	
  	
  
EU	
  Project	
  Networking	
  
Session	
  
3rd	
  June	
  2015	
  
(14:00	
  –	
  17:00)	
  
Room	
  Adria	
  I/II,	
  Grand	
  Hotel	
  Bernardin,	
  Portoroz	
  
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)	
  
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	
  	
  
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	
  
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!	
  
1	
  
LEO	
  
Linked Open Earth Observation Data for
Precision Farming (LEO, http://
www.linkedeodata.eu/)
Manolis Koubarakis
National and Kapodistrian
University of Athens
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.
2	
  
Open	
  Data	
  Monitor	
  
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
Open Data Monitor: opendatamonitor.eu
3	
  
MARIO	
  
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
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
4	
  
PHEME	
  
ESWC 2015 - EU Networking Session
5	
  
EGI-­‐ENGAGE	
  
EGI	
  ENGAGE	
  	
  
DARIAH	
  competence	
  centre	
  
what	
  ?	
  
	
  
establish	
  science-­‐oriented	
  competence	
  centre	
  
providing	
  support	
  for	
  researchers	
  within	
  the	
  
humaniNes,	
  arts	
  and	
  social	
  sciences	
  
	
  
	
  
	
  
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	
  	
  
	
  	
  	
  	
  	
  	
  
EGI	
  ENGAGE	
  	
  
DARIAH	
  competence	
  centre	
  
how	
  ?	
  	
  
	
  
DARIAH	
  arts	
  &	
  humaniNes	
  use	
  case	
  
(e-­‐infrastructure,	
  educaNon+training,	
  use	
  cases,	
  data)	
  
	
  
	
  
	
  
EGI	
  ENGAGE	
  	
  
DARIAH	
  competence	
  centre	
  
how	
  ?	
  	
  
	
  
DARIAH	
  arts	
  &	
  humaniNes	
  use	
  case	
  
(e-­‐infrastructure,	
  educaNon+training,	
  use	
  cases,	
  data)	
  
	
  
	
  	
  	
  	
  	
  	
  	
  	
  language	
  resources,	
  AT	
  varieNes	
  
	
  
	
  
	
   	
   	
  	
  
	
  	
  
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]	
  
	
  
6	
  
x-­‐LIME	
  
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
LiMexLiMe – crossLingual crossMedia knowledge extraction
Visit us at
www.xlime.eu XLiMe project
supported by
7	
  
Facts4Workers	
  
03.06.2015 FACTS4WORKERS – 27
Start: 1.1.2015
Duration: 4 years
Partners:
•  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
8	
  
COMSODE	
  
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
The COMSODE project
•  Develop a quality aware
open data pubblication
platfrom (open data node)
•  Provide a quality aware
methodology for selecting
and publishig dataset
ESWC 2015 - EU Networking Session
9	
  
ProaSense	
  
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:	
  
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?	
  
10	
  
MixedEmoNons	
  
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	
  	
  
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	
  	
  
11	
  
Dem@Care	
  
Demen%a  Ambient  Care:  Mul%-­‐Sensing  
Monitoring  for  Intelligent  Remote  
Management  and  Decision  Support
Georgios	
  Meditskos,	
  Ioannis	
  Kompatsiaris	
  
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	
  
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
Seman%c  Web  Technologies  in  
Dem@Care
12	
  
FREME	
  
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	
  
ESWC 2015 - EU Networking Session
13	
  
BYTE	
  
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	
  
14	
  
ENeL	
  COST	
  
EU Project Networking
Session
June, 3rd 2015, Portoroz
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/
15	
  
WDAqua	
  ITN	
  
ESWC 2015 - EU Networking Session
ESWC 2015 - EU Networking Session
16	
  
LIDER	
  
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
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
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	
  
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	
  
EU	
  Project	
  Networking	
  Session	
  2015	
  	
  
–	
  	
  
Discussion	
  Minutes	
  
Table	
  1:	
  Health,	
  HumaniNes,	
  Arts	
  	
  
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.	
  
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	
  	
  
EU	
  Project	
  Networking	
  Session	
  2015	
  	
  
–	
  	
  
Discussion	
  Minutes	
  
Table	
  2	
  &	
  3	
  :	
  (Linked)	
  (Open)	
  (Big)	
  
(Geo)	
  (MulNlingual)	
  Data	
  
●  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)
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).
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
EU Project Networking
Session 2015
–
Discussion Minutes
Table 4: Industry & Business
• 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	
  
	
  
• "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	
  
	
  
• 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)	
  
• 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).	
  

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ESWC 2015 - EU Networking Session

  • 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!  
  • 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
  • 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
  • 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]    
  • 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
  • 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
  • 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
  • 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?  
  • 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    
  • 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
  • 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  
  • 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  
  • 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/
  • 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).