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Impact of Open Data and
Linked Open Data
Venezuela

Maria-Esther Vidal

Universidad Simón Bolívar

mvidal@ldc.usb.ve	
  
h1p://www.ldc.usb.ve/~mvidal	
  
Twi1er	
  @Maria11576561	
  
Skype:	
  mevs2006	
  

1	
  
Lights	
  around	
  the	
  London's	
  2012	
  Olympic	
  stadium	
  describe	
  Sir	
  Tim	
  Berners-­‐Lee's	
  invenKon,	
  the	
  	
  
World	
  Wide	
  Web.	
  The	
  Open	
  Data	
  InsKtute,	
  which	
  he	
  co-­‐founded,	
  declares	
  a	
  mandate	
  of	
  
	
  'Knowledge	
  for	
  Everyone'.	
  
“The	
  ODI	
  announced	
  new	
  13	
  nodes:	
  
US,	
  Canada,	
  France,	
  Dubai,	
  Italy,	
  
Russia,	
  Sweden	
  and	
  ArgenKna.”	
  
Oct	
  29	
  	
  2103	
  

Sir	
  Tim	
  Berners-­‐Lee	
  (right)	
  and	
  Sir	
  Nigel	
  Shadbolt	
  (leT)	
  
Agenda
Ø Open Data
Ø Linked Open Data
ü Linked Open Data in Journalism

Ø Linked Open Data Applications
ü Linked Open Data at USB

Ø Conclusions and Future Directions
OPEN	
  DATA	
  
Open Data
Definition http://opendefinition.org/:
“A piece of data or content is open if anyone is
free to use, reuse, and redistribute it — subject
only, at most, to the requirement to attribute
and/or share-alike.”
Open_Data_stickers.jpg 1,024×768 pixels

7/1/13 9:33 PM

Availability and access
Reuse and Distribution
Universal Participation
6	
  
Open Data
	
  Availability and Access:
Data should be available as a whole,
preferably downloading via the Internet.
Data should be available in a convenient
format.
Should be free or at most at a reproduction
cost.

7	
  
Open Data
	
  
Reuse and Distribution:
Data should be offered in a way that it can be
reused, distributed and be interrelated with
other datasets.

8	
  
Open Data
	
  
Universal Participation:
Any person should be able to use, reuse and
distribute.
NO discrimination:
Commercial vs. NOT commercial
Educational vs. NOT educational
Profit vs. No Profit

9	
  
Type of Open Data
Why Open Data?

Interoperability	
  

Transparency	
  

11	
  
Why Open Data?
Avoid	
  CorrupKon	
  

Wealth	
  
Only	
  in	
  Europe	
  over	
  140	
  billion	
  of	
  euros	
  per	
  year	
  

h1p://www.economist.com/news/business/21578084-­‐making-­‐official-­‐data-­‐publi
could-­‐spur-­‐lots-­‐innovaKon-­‐new-­‐goldmine	
  
12	
  
Why Open Data?

Research	
  and	
  Development	
  

Quality	
  of	
  Life	
  
13	
  
Why Open Data?

Improve	
  Public	
  AdministraKon	
  
Data	
  Quality	
  
14	
  
Why Open Data?

Citizens can express themselves and unite so that their
voices can be heard.
15	
  

h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html	
  
Open	
  
Licenses	
  

Open	
  Data	
  

Open	
  
ParKcipaKon	
  

Open	
  Source	
  

Open	
  
Standards	
  
What is and what is not Open Data
Open	
  Data.	
  

“A piece of content or data is open if you are free to use,
reuse, and redistribute it — subject only, at most, to the
requirement to attribute and share-alike.”
Difference between open data and data that is publicly available lies in the use of
formats that may be read, used and redistributed by any citizen.
Examples of public data that is not open data: data in spreadsheets, pdf, etc. Usually
open data are csv.
h1p://opensource.com/government/10/12/what-­‐“open-­‐data”-­‐means-­‐–-­‐and-­‐what-­‐it-­‐doesn’t	
  
Opening Up Data
Rules
Ø  Keep it simple
Ø  Engage early and
engage often
Ø  Address common fears
and misunderstandings

Four Steps
Ø  Choose your Dataset(s)
Ø  Apply an Open License
Ø  Make the data available
Ø  Make it discoverable
Open Data Conditions
Data Providers Requirements

Distributing Open Data

Ø  Attribution: data
providers may require to
receive credit.
Ø  Integrity: data providers
may require that users
indicate if data change.
Ø  Share-alike: data
providers may impose
that any dataset created
using their data are also
open.

Ø  Data is machine-readable
Ø  Data is available in bulk
more than using an API.

h1p://opendatahandbook.org/en/	
  
OPEN	
  DATA	
  APPLICATIONS	
  
OPEN	
  DATA	
  AND	
  GOVERNMENT	
  
Some Open Data Applications

Al	
  menos	
  77	
  países	
  cumplen	
  nivel	
  >	
  2	
  
h1p://wheredoesmymoneygo.org/	
  

22	
  
Why Open
Data?
Citizens may
unite so that
their voices can
be heard
Why Open Data?

Monrovia	
  África	
  

h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
Why Open Data?

Tanzanía	
  

h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
Why Open Data?

Tanzanía	
  

h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
Why Open Data?

Tanzanía	
  

h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
h1p://www.tableausoTware.com/public/gallery/london-­‐deprivaKon	
  
OPEN	
  DATA	
  AND	
  PUBLIC	
  HEALTH	
  
Vaccines and Immunisation in Australia

h1p://www.theguardian.com/society/datablog/interacKve/2013/oct/16/children-­‐vaccinaKon-­‐australia-­‐map	
  
h1p://www.cfr.org/interacKves/GH_Vaccine_Map/index.html#map	
  
Applications of Open Data	
  
h1p://pinterest.com/socrata/open-­‐data-­‐applicaKons/	
  

Kenia	
  
OPEN	
  DATA	
  AND	
  SOCIETY	
  	
  
ApplicaKons	
  of	
  Open	
  Data	
  

h1p://www.crimemapping.com/	
  
h1p://www.tableausoTware.com/public/gallery/tcc13friends	
  
OPEN	
  DATA	
  AND	
  FINANCES	
  
h1p://opencorporates.com/viz/financial/index.html#bankofamerica/ch/934	
  

Who Owns Who
h1p://opencorporates.com/viz/financial/index.html#goldman/ch/2213	
  

Who Owns Who
OPEN	
  DATA	
  AND	
  ENVIRONMENT	
  
Why Open Data?
Improve	
  Public	
  Services	
  

Smart	
  CiKes	
  

Mejorar	
  la	
  
Administración	
  Pública	
  
40	
  
h1p://visualizaKon.geblogs.com/visualizaKon/co2/	
  
h1p://www.visualizing.org/full-­‐screen/27036	
  
h1p://www.visualizing.org/full-­‐screen/27036	
  
Applications of Open Data	
  
h1p://opendatachallenge.org/	
  
	
  
LINKED	
  OPEN	
  DATA	
  
h1p://www.ted.com/talks/lang/en/Km_berners_lee_the_year_open_data_went_worldwide.html	
  
What to do with Open Data?

46	
  
What to do with Open Data?

At	
  least	
  77	
  countries	
  comply	
  level	
  >	
  2	
  
h1p://www.slideshare.net/mgarrigap/opendata-­‐en-­‐el-­‐ararteko	
  

47	
  
What to do with Open Data?

At least 11 countries comply level > 4
h1p://www.slideshare.net/mgarrigap/opendata-­‐en-­‐el-­‐ararteko	
  

48	
  
What to do with Open Data?
	
  

h1p://www.theguardian.com/news/datablog/2013/oct/28/uk-­‐top-­‐open-­‐data-­‐index-­‐how-­‐countries-­‐compare#!	
  
h1p://www.theguardian.com/news/datablog/2013/oct/28/uk-­‐top-­‐open-­‐data-­‐index-­‐how-­‐countries-­‐compare#!	
  
Bottom 10 by Open Data Index Score
	
  

h1p://www.theguardian.com/news/datablog/2013/oct/28/uk-­‐top-­‐open-­‐data-­‐index-­‐how-­‐countries-­‐compare	
  
Local	
  Governments	
  must	
  use	
  
Open	
  Data	
  to	
  stay	
  connected	
  
with	
  the	
  ciKzens!	
  
MoKvaKon	
  SemanKc	
  Web	
  EvoluKon	
  
The Linked Open Data cloud, using the
Web to connect related data that was not
previously linked!
Published Data are enhanced with semantics!
Standards to annotate and describe data:
XML, RDF, RDFS, OWL.
Standards to query data: SPARQL.
Ontologies representing almost any domain.
Hyperlink-based systems.
Protocols: http, uri, html
Documents and data were published
Arpanet: four servers connected
Files were transferred
Tools: ftp, telnet, e-mail
80’s

IRMLs	
  2010-­‐ESWC	
  2010
	
  

90’s

00’s

Now
The Linked Open Data Cloud
• Explosion in the number of:
	
  	
  
– Linking Open Data
resources and databases
– Different quality
parameters.
Molecular databases 1170, 95 more 	

– Controlledthan 2008 and 110 more than the year before ! 	

vocabularies:
– MeSH, GO, PO… tools published	

Services and
– Highly interconnected
by these databases follow a similar progression! 	

data sources:
In October 2007, Cloud of Linked Data 	

Different Sizes
datasets consisted of over two billion RDF triples, 	

Many links

which were interlinked by over two million RDF links. 	

• Different in- and outBy May 2009 this had grown to 4.2 billion RDF triples, 	

degrees, etc
interlinked by around 142 billions RDF links! Today 	

• Biological Web: large
the Linked Open Data cloud has at least 295 datasets,
datasets of linking data.
31,634,213,770	
  triples, and 503,998,829	
  links. 	

• Genes, Diseases,
Clinical Drugs, Proteins,
and so on.
StaKsKcs	
  
LINKED	
  DATA	
  IN	
  JOURNALISM	
  
Open Data in Journalism	
  
Ø It may be trendy but not new.
Ø Open Data implies Open Data Journalism.
Ø Data is not necessarily curated.
Ø Bigger Datasets and Small Things.
Ø Data Journalism is 80% perspiration, 10% great
ideas, 10% output.
Ø Long and short-form.
Ø Anyone can do it.
Ø Visualization is important.
Ø Data publishers do not have to be programmers.
Ø It is all about stories.
h1p://www.theguardian.com/news/datablog/2011/jul/28/data-­‐journalism	
  
Breaking	
  
News	
  

Open	
  
Data	
  

Running	
  
Events	
  

Shared	
  Data	
  

Open Data in
Journalism
Breaking	
  
News	
  

Open	
  
Data	
  

Open Data in
Journalism

Running	
  
Events	
  

Shared	
  Data	
  
•  Data Cleansing
•  Conflict Resolution

Data	
  IntegraKon	
  

SemanKficaKon	
  

•  Meta-Data
Annotation
•  Vocabularies

•  Visualization
•  Publishing the Story

PublicaKon	
  
Meta-Data
BBC News	
  

This will help users to
find news content about
the stories they want to
know about and
ultimately help to open
up references to the
data contained in
those stories.

h1p://www.bbc.co.uk/blogs/internet/posts/News-­‐Linked-­‐Data-­‐Ontology	
  
Data Management ToolsBBC News	
  

h1p://www.bbc.co.uk/blogs/internet/posts/Linked-­‐Data-­‐ConnecKng-­‐together-­‐the-­‐BBCs-­‐Online-­‐Content	
  
h1p://www.slideshare.net/moustaki/linked-­‐data-­‐on-­‐the-­‐bbc-­‐2638734	
  
More	
  
Ontologies	
  to	
  
represent	
  
Meta-­‐Data	
  
VISUALIZING	
  LINKED	
  OPEN	
  DATA	
  
Challenges	
  for	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Linked	
  Data	
  
Visualization	
  
•  Enabling	
  user	
  interacKon	
  

–  Users	
  must	
  be	
  able	
  to	
  navigate	
  through	
  the	
  data	
  by	
  exploiKng	
  the	
  
connecKons	
  between	
  Linked	
  Data	
  resources	
  
–  The	
  user	
  might	
  edit	
  the	
  underlying	
  data	
  to	
  enrich	
  it	
  by:	
  	
  
•  CreaKng	
  addiKonal	
  metadata	
  
•  HighlighKng	
  or	
  correcKng	
  errors	
  
•  ValidaKng	
  data	
  

•  SupporKng	
  data	
  reusability	
  
–  The	
  output	
  (the	
  plo1ed	
  data	
  or	
  the	
  visualizaKon	
  itself)	
  might	
  be	
  
encoded	
  using	
  standard	
  ontologies	
  and	
  vocabularies	
  	
  	
  

•  Scalability	
  
–  Linked	
  Data	
  visualizaKon	
  techniques	
  should	
  support	
  the	
  display	
  of	
  
large	
  amount	
  of	
  data	
  in	
  an	
  efficient	
  way	
  
EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

74	
  
Challenges	
  for	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  Linked	
  Open	
  
Data	
  Visualization	
  
•  ExtracKng	
  data	
  from	
  different	
  repositories	
  

–  A	
  Linked	
  Data	
  set	
  might	
  be	
  parKKoned	
  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	
  properKes	
  
EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

75	
  
Linked	
  Open	
  Data	
  VisualizaKon	
  Techniques	
  	
  

View	
  
EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

76	
  
Comparison	
  of	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
A1ributes	
  /	
  Values	
  
Bar/column	
  chart	
  	
  

Pie	
  chart	
  

Allows	
  the	
  comparison	
  of	
  values	
  of	
  
different	
  categories.	
  
	
  	
  

Useful	
  for	
  performing	
  comparison	
  
of	
  percentages	
  or	
  proporKons.	
  
	
  
	
  

Image	
  source:	
  h1p://musicbrainz.fluidops.net	
  

Image	
  source:	
  h1p://mbostock.github.io/protovis/	
  	
  	
  

Line	
  chart	
  

Histogram	
  

Allows	
  visualizing	
  data	
  as	
  a	
  series	
  of	
  
data	
  points,	
  where	
  the	
  measurement	
  
points	
  (x-­‐axis)	
  are	
  ordered.	
  	
  	
  

Graphical	
  representaKon	
  of	
  the	
  
distribuKon	
  of	
  the	
  data.	
  

	
  
Image	
  source:	
  h1p://mbostock.github.io/protovis/	
  	
  	
  
Image	
  source:	
  h1p://musicbrainz.fluidops.net	
  
EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

77	
  
Analysis	
  of	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  RelaKonships	
  and	
  
Hierarchies	
  	
  
Graph	
  	
  

Arc	
  diagram	
  

The	
  data	
  entries	
  are	
  represented	
  as	
  
nodes	
  and	
  the	
  links	
  as	
  edges.	
  	
  
	
  	
  

The	
  nodes	
  are	
  displayed	
  in	
  one	
  
dimension,	
  and	
  the	
  arcs	
  represent	
  
the	
  connecKons.	
  
	
  
	
  

Adjacency	
  Matrix	
  diagram	
  

Node-­‐link	
  visualizaKons	
  

The	
  nodes	
  are	
  displayed	
  as	
  rows	
  and	
  
columns,	
  and	
  the	
  links	
  between	
  the	
  
nodes	
  are	
  entries	
  in	
  the	
  matrix.	
  

The	
  data	
  is	
  organized	
  in	
  hierarchies.	
  

	
  
Source	
  of	
  images:	
  h1p://mbostock.github.io/protovis/	
  	
  	
  

EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

78	
  
Analysis	
  of	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  RelaKonships	
  and	
  
Hierarchies	
  (2)	
  	
  
Space-­‐filling	
  techniques	
  

Treemaps	
  

Icicles	
  and	
  sunburst	
  

Subdivide	
  area	
  into	
  rectangles.	
  

Hierarchies	
  are	
  represented	
  by	
  
adjacencies.	
  	
  

Circle-­‐packing	
  	
  	
  

Rose	
  diagrams	
  

Containment	
  is	
  used	
  to	
  represent	
  the	
  
hierarchies.	
  

Areas	
  are	
  equal	
  angles	
  and	
  the	
  data	
  
is	
  represented	
  by	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
the	
  extension	
  of	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  	
  
the	
  area.	
  

Source	
  of	
  images:	
  h1p://mbostock.github.io/protovis/	
  	
  	
  

EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

79	
  
Analysis	
  of	
  	
  Temporal	
  or	
  Geographical	
  
Events	
  	
  
ConKnuous	
  data	
  in	
  Kme	
  

Timeline	
  
	
  

Discrete	
  data	
  points	
  in	
  Kme	
  

Source:	
  h1p//musicbrainz.fluidops.net	
  
Source:	
  h1p://www.ko1ke.org/08/08/2008-­‐movie-­‐box-­‐office-­‐chart	
  

Display	
  geo-­‐points	
  on	
  a	
  map	
  

Choropleth	
  maps	
  

Dorling	
  cartograms	
  

Aggregate	
  data	
  by	
  
geographical	
  area	
  

Aggregate	
  data	
  and	
  replace	
  
each	
  area	
  with	
  a	
  circle	
  

	
  

Maps	
  

LocaKon	
  maps	
  

Source:	
  Google	
  Map	
  API	
  

Source:	
  h1p//musicbrainz.fluidops.net	
  
EUCLID	
  –	
  InteracKon	
  with	
  Linked	
  Data	
  

Source:	
  h1p://mbostock.github.io/protovis/	
  	
  	
  
80	
  
Libraries	
  
	
  

h1ps://github.com/mbostock/d3/wiki/Gallery	
  
APPLICATIONS	
  
Tasks	
  to	
  be	
  Solved	
  …	
  
Traverse and Consume
Linked Data from the LOD cloud or
locally.

SPARQL endpoints have been developed to access data from the LOD cloud.
83	
  
SPARQL	
  ENDPOINTS	
  
	
  
select	
  disKnct	
  *	
  where	
  {<h1p://dbpedia.org/resource/Venezuela>	
  ?p	
  ?o}	
  	
  
h1p://dbpedia.org/sparql	
  
All	
  the	
  informaKon	
  related	
  to	
  Venezuela	
  

SPARQL	
  Query	
  
h1p://worldbank.270a.info/about.html	
  
SPARQL	
  Endpoint	
  
	
  URL	
  

SPARQL	
  Query	
  
SPARQL	
  Query	
  
Data:	
  
foaf:made	
  

dbpedia:	
  
The_Beatles	
  

foaf:made	
  

foaf:made	
  
<h1p://	
  
musicbrainz.org/
record/...>	
  

dc:Ktle	
  

<h1p://	
  
musicbrainz.org/
record/...>	
  

dc:Ktle	
  
"Help!"	
  

"Abbey	
  Road"	
  

<h1p://	
  
musicbrainz.org/
record/...>	
  

dc:Ktle	
  
"Let	
  It	
  Be"	
  
SELECT	
  ?x	
  ?name	
  ?mbox	
  ?country	
  ?reviewer	
  ?product	
  ?title	
  
WHERE	
  {	
  	
  
<http://www4.wiwiss.fu-­‐berlin.de/bizer/bsbm/v01/instances/dataFromRatingSite293/
Review2883011>	
  rev:reviewer	
  ?x	
  .	
  	
  
	
  	
  ?x	
  <http://www.w3.org/1999/02/22-­‐rdf-­‐syntax-­‐ns#type>	
  <http://xmlns.com/foaf/0.1/Person>	
  .	
  
	
  	
  ?x	
  <http://xmlns.com/foaf/0.1/name>	
  ?name	
  .	
  
	
  	
  ?x	
  <http://xmlns.com/foaf/0.1/mbox_sha1sum>	
  ?mbox	
  .	
  
	
  	
  ?x	
  <http://www4.wiwiss.fu-­‐berlin.de/bizer/bsbm/v01/vocabulary/country>	
  ?country	
  .	
  
	
  	
  ?reviewer	
  <http://purl.org/stuff/rev#reviewer>	
  ?x	
  .	
  
	
  	
  ?reviewer	
  <http://www4.wiwiss.fu-­‐berlin.de/bizer/bsbm/v01/vocabulary/reviewFor>	
  ?product	
  .	
  
	
  	
  ?reviewer	
  <http://purl.org/dc/elements/1.1/title>	
  ?title	
  }	
  
Graph	
  Databases	
  
APPLICATIONS	
  DEVELOPED	
  AT	
  USB	
  
FEDERATED	
  QUERIES	
  
ANAPSID	
  

SPARQL-DQP 	
  

Federations of Endpoints
h1ps://github.com/anapsid/anapsid	
  

Federated Queries

ANAPSID	
  

“Genes and diseases that have been studied
for drugs tested in clinical trials where Breast Cancer was studied”

SELECT	
  DISTINCT	
  ?D1?TGD	
  ?GN1	
  ?GN2	
  
WHERE	
  {	
  
	
  ?CT1	
  <http://data.linkedct.org/resource/linkedct/condition>	
  ?C1	
  .	
  
	
  ?CT1<http://data.linkedct.org/resource/linkedct/intervention>	
  ?I	
  .
	
  	
  
	
  ?CT1<http://data.linkedct.org/resource/linkedct/intervention>	
  ?I	
  .	
  
	
  ?I<http://data.linkedct.org/resource/linkedct/intervention_type>	
  "Drug"	
  .	
  
	
  ?C1	
  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>	
  ?D1	
  .	
  
	
  ?I	
  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>	
  ?I1	
  .	
  
	
  ?C	
  <http://data.linkedct.org/resource/linkedct/condition_name>	
  "Breast	
  Cancer"	
  .	
  
	
  ?CT	
  <http://data.linkedct.org/resource/linkedct/intervention>	
  ?I	
  .	
  
	
  ?CT	
  <http://data.linkedct.org/resource/linkedct/condition>	
  ?A4	
  .	
  
	
  ?II	
  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/target>	
  ?TGD	
  .	
  
	
  ?TGD	
  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/genbankIdGene>	
  ?GN1	
  .	
  
	
  ?D1	
  <http://www4.wiwiss.fu-­‐berlin.de/diseasome/resource/diseasome/associatedGene>	
  ?GN2	
  .	
  
}	
  

Life
Sciences
Query:

97	
  
Federated Queries

h1ps://github.com/anapsid/anapsid	
  
ANAPSID	
  

SELECT	
  DISTINCT	
  ?D1	
  ?TGD	
  ?GN1	
  ?GN2	
  
WHERE	
  {	
  	
  
	
  	
  	
  	
  {	
  SERVICE	
  <http://virtuoso.bd.cesma.usb.ve/sparql>	
  {	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?C1	
  <http://data.linkedct.org/resource/linkedct/condition_name>	
  "Breast	
  Cancer"	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?C1	
  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>	
  ?D1	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?C3	
  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>	
  ?D1	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?CT3	
  <http://data.linkedct.org/resource/linkedct/condition>	
  ?C3	
  }}	
  .	
  	
  
	
  
	
  	
  	
  	
  {	
  SERVICE	
  <http://virtuoso.bd.cesma.usb.ve/sparql>	
  {	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?C1	
  <http://data.linkedct.org/resource/linkedct/condition_name>	
  "Breast	
  Cancer"	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?I	
  <http://data.linkedct.org/resource/linkedct/intervention_type>	
  "Drug"	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?CT1	
  <http://data.linkedct.org/resource/linkedct/condition>	
  ?C1	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?CT1	
  <http://data.linkedct.org/resource/linkedct/intervention>	
  ?I	
  }}	
  .	
  	
  
	
  
	
  	
  	
  	
  {	
  SERVICE	
  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/>	
  {	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?I1	
  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/target>	
  ?TGD	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?TGD	
  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/genbankIdGene>	
  ?GN1	
  }}	
  .	
  
	
  	
  
	
  	
  	
  	
  {	
  SERVICE	
  <http://virtuoso.bd.cesma.usb.ve/sparql>	
  {	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?I	
  <http://data.linkedct.org/resource/linkedct/intervention_type>	
  "Drug"	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?I	
  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>	
  ?I1	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?CT3	
  <http://data.linkedct.org/resource/linkedct/intervention>	
  ?I	
  .	
  	
  
	
  	
  	
  	
  	
  	
  	
  	
  ?CT3	
  <http://data.linkedct.org/resource/linkedct/condition>	
  ?C3	
  }}	
  .	
  	
  
}	
  
	
  

S1:	
  

S2:	
  

S3:	
  
S4:	
  

98	
  
Federated Queries

h1ps://github.com/anapsid/anapsid	
  
ANAPSID	
  

S1	
  

S2	
  

S3	
  
S4	
  

99	
  
ANAPSID	
  
ANAPSID	
  
ANAPSID	
  

h1p://silurian.thalassa.cbm.usb.ve/	
  
“Drugs that possibly target Leukemia”
SELECT	
  DISTINCT	
  ?drug1	
  	
  	
  	
  
WHERE	
  {	
  
?drug1	
  drugbank:possibleDiseaseTarget	
  
diseasome:673	
  .	
  	
  	
  	
  
?drug1	
  drugbank:target	
  ?o.	
  	
  	
  	
  	
  
?o	
  drugbank:genbankIdGene	
  ?g.	
  	
  	
  	
  	
  
?o	
  drugbank:locus	
  ?l.	
  	
  	
  	
  	
  
?o	
  drugbank:molecularWeight	
  ?mw.	
  	
  	
  	
  	
  
?o	
  drugbank:hprdId	
  ?hp.	
  	
  	
  	
  	
  
?o	
  drugbank:swissprotName	
  ?sn.	
  	
  	
  	
  	
  
?o	
  drugbank:proteinSequence	
  ?ps.	
  	
  	
  	
  
?o	
  drugbank:generalReference	
  ?gr.	
  	
  	
  	
  	
  
?drug	
  drugbank:target?o.	
  	
  	
  	
  	
  
?drug	
  drugbank:synonym?o1	
  .	
  
	
  	
  OPTIONAL	
  {	
  
	
  	
  	
  	
  ?drug	
  owl:sameAs	
  ?drug5	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?drug5	
  rdf:type	
  dbcategory:Drug	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?drug	
  drugbank:keggCompoundId	
  ?cpd	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?enzyme	
  kegg:xSubstrate	
  ?cpd	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?enzyme	
  rdf:type	
  kegg:Enzyme	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?reaction	
  kegg:xEnzyme	
  ?enzyme	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?reaction	
  kegg:equation	
  ?equation	
  .	
  	
  	
  
}	
  }	
  

h1p://silurian.thalassa.cbm.usb.ve/	
  

101	
  
“Drugs that possibly target Leukemia”
SELECT	
  DISTINCT	
  ?drug1	
  	
  	
  	
  
WHERE	
  {	
  
?drug1	
  drugbank:possibleDiseaseTarget	
  
diseasome:673	
  .	
  	
  	
  	
  
?drug1	
  drugbank:target	
  ?o.	
  	
  	
  	
  	
  
?o	
  drugbank:genbankIdGene	
  ?g.	
  	
  	
  	
  	
  
?o	
  drugbank:locus	
  ?l.	
  	
  	
  	
  	
  
?o	
  drugbank:molecularWeight	
  ?mw.	
  	
  	
  	
  	
  
?o	
  drugbank:hprdId	
  ?hp.	
  	
  	
  	
  	
  
?o	
  drugbank:swissprotName	
  ?sn.	
  	
  	
  	
  	
  
?o	
  drugbank:proteinSequence	
  ?ps.	
  	
  	
  	
  
?o	
  drugbank:generalReference	
  ?gr.	
  	
  	
  	
  	
  
?drug	
  drugbank:target?o.	
  	
  	
  	
  	
  
?drug	
  drugbank:synonym?o1	
  .	
  
	
  	
  OPTIONAL	
  {	
  
	
  	
  	
  	
  ?drug	
  owl:sameAs	
  ?drug5	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?drug5	
  rdf:type	
  dbcategory:Drug	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?drug	
  drugbank:keggCompoundId	
  ?cpd	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?enzyme	
  kegg:xSubstrate	
  ?cpd	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?enzyme	
  rdf:type	
  kegg:Enzyme	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?reaction	
  kegg:xEnzyme	
  ?enzyme	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?reaction	
  kegg:equation	
  ?equation	
  .	
  	
  	
  
}	
  }	
  

h1p://silurian.thalassa.cbm.usb.ve/	
  

102	
  
“Drugs that possibly target Leukemia”
SELECT	
  DISTINCT	
  ?drug1	
  	
  	
  	
  
WHERE	
  {	
  
?drug1	
  drugbank:possibleDiseaseTarget	
  
diseasome:673	
  .	
  	
  	
  	
  
?drug1	
  drugbank:target	
  ?o.	
  	
  	
  	
  	
  
?o	
  drugbank:genbankIdGene	
  ?g.	
  	
  	
  	
  	
  
?o	
  drugbank:locus	
  ?l.	
  	
  	
  	
  	
  
?o	
  drugbank:molecularWeight	
  ?mw.	
  	
  	
  	
  	
  
?o	
  drugbank:hprdId	
  ?hp.	
  	
  	
  	
  	
  
?o	
  drugbank:swissprotName	
  ?sn.	
  	
  	
  	
  	
  
?o	
  drugbank:proteinSequence	
  ?ps.	
  	
  	
  	
  
?o	
  drugbank:generalReference	
  ?gr.	
  	
  	
  	
  	
  
?drug	
  drugbank:target?o.	
  	
  	
  	
  	
  
?drug	
  drugbank:synonym?o1	
  .	
  
	
  	
  OPTIONAL	
  {	
  
	
  	
  	
  	
  ?drug	
  owl:sameAs	
  ?drug5	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?drug5	
  rdf:type	
  dbcategory:Drug	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?drug	
  drugbank:keggCompoundId	
  ?cpd	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?enzyme	
  kegg:xSubstrate	
  ?cpd	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?enzyme	
  rdf:type	
  kegg:Enzyme	
  .	
  	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?reaction	
  kegg:xEnzyme	
  ?enzyme	
  .	
  	
  	
  	
  	
  	
  	
  	
  
	
  	
  	
  	
  ?reaction	
  kegg:equation	
  ?equation	
  .	
  	
  	
  
}	
  }	
  

h1p://silurian.thalassa.cbm.usb.ve/	
  

103	
  
APPLICATIONS-­‐	
  
LINK	
  PREDICTION	
  AND	
  PATTERN	
  
DISCOVERY	
  	
  
Tasks	
  to	
  be	
  Solved	
  …(2)	
  
Patterns of connections
between people to understand
functioning of society.
!#
$%%	#
Topological properties
of graphs can be used to identify
patterns that reveal phenomena,
anomalies and potentially lead to
a discovery.

A significant increase of graph data in the form of social  biological information.
105	
  
Annotation Graph

107	
  
Pa1erns	
  or	
  Signatures	
  
Brentuzumab_vedoKn	
  
And	
  Catumaxomab	
  	
  

108	
  
Annotation Similarity between two
genes based on shared GO
annotations
Vacuolar	
  
GO	
  Paths	
  
Membrane	
  

Vacuolar	
  
Membrane	
  

Golgi	
  	
  
apparatus	
  
Plant-­‐type	
  
vacuole	
  

Chloroplast	
  
Gene	
  
AtVHA-­‐C5	
  

Vacuole	
  	
  
proton-­‐	
  
TransporKng	
  	
  
V-­‐type	
  	
  
ATPase	
  ,	
  V1	
  	
  
domain	
  	
  

Gene	
  
AtVHA-­‐C	
  

Chloroplast	
  
Vacuole	
  
GO	
  Terms	
  

Vacuole	
  
GO	
  Terms	
  
109	
  
 
Pa1erns	
  or	
  Signatures	
  between	
  
genes	
  AtVHA-­‐C5	
  and	
  AtVHA-­‐C	
  
	
  

110	
  
Drug-Target Interaction Network	
  
	
  

Pa1erns	
  
Between	
  
InteracKons	
  	
  

PotenKal	
  new	
  
interacKon	
  
112	
  
Patterns of connections
between people to understand
functioning of society.

113	
  
h1p://www.visualizing.org/full-­‐screen/27036	
  
Conclusions
Ø  Open Data:
ü  Transparency
ü  Interoperability
ü  Avoid Corruption
ü  Impulse research and development
ü  Data Quality

Ø  Linked Open Data:
ü  RDF data
ü  Linked to existing datasets
ü  Endpoints can be used to access data

116	
  

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Presentación Prof. Maria Esther Vida. DataBootCampVE/31 octubre 2013

  • 1. Impact of Open Data and Linked Open Data Venezuela Maria-Esther Vidal Universidad Simón Bolívar mvidal@ldc.usb.ve   h1p://www.ldc.usb.ve/~mvidal   Twi1er  @Maria11576561   Skype:  mevs2006   1  
  • 2. Lights  around  the  London's  2012  Olympic  stadium  describe  Sir  Tim  Berners-­‐Lee's  invenKon,  the     World  Wide  Web.  The  Open  Data  InsKtute,  which  he  co-­‐founded,  declares  a  mandate  of    'Knowledge  for  Everyone'.  
  • 3. “The  ODI  announced  new  13  nodes:   US,  Canada,  France,  Dubai,  Italy,   Russia,  Sweden  and  ArgenKna.”   Oct  29    2103   Sir  Tim  Berners-­‐Lee  (right)  and  Sir  Nigel  Shadbolt  (leT)  
  • 4. Agenda Ø Open Data Ø Linked Open Data ü Linked Open Data in Journalism Ø Linked Open Data Applications ü Linked Open Data at USB Ø Conclusions and Future Directions
  • 6. Open Data Definition http://opendefinition.org/: “A piece of data or content is open if anyone is free to use, reuse, and redistribute it — subject only, at most, to the requirement to attribute and/or share-alike.” Open_Data_stickers.jpg 1,024×768 pixels 7/1/13 9:33 PM Availability and access Reuse and Distribution Universal Participation 6  
  • 7. Open Data  Availability and Access: Data should be available as a whole, preferably downloading via the Internet. Data should be available in a convenient format. Should be free or at most at a reproduction cost. 7  
  • 8. Open Data   Reuse and Distribution: Data should be offered in a way that it can be reused, distributed and be interrelated with other datasets. 8  
  • 9. Open Data   Universal Participation: Any person should be able to use, reuse and distribute. NO discrimination: Commercial vs. NOT commercial Educational vs. NOT educational Profit vs. No Profit 9  
  • 10. Type of Open Data
  • 11. Why Open Data? Interoperability   Transparency   11  
  • 12. Why Open Data? Avoid  CorrupKon   Wealth   Only  in  Europe  over  140  billion  of  euros  per  year   h1p://www.economist.com/news/business/21578084-­‐making-­‐official-­‐data-­‐publi could-­‐spur-­‐lots-­‐innovaKon-­‐new-­‐goldmine   12  
  • 13. Why Open Data? Research  and  Development   Quality  of  Life   13  
  • 14. Why Open Data? Improve  Public  AdministraKon   Data  Quality   14  
  • 15. Why Open Data? Citizens can express themselves and unite so that their voices can be heard. 15   h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html  
  • 16. Open   Licenses   Open  Data   Open   ParKcipaKon   Open  Source   Open   Standards  
  • 17. What is and what is not Open Data Open  Data.   “A piece of content or data is open if you are free to use, reuse, and redistribute it — subject only, at most, to the requirement to attribute and share-alike.” Difference between open data and data that is publicly available lies in the use of formats that may be read, used and redistributed by any citizen. Examples of public data that is not open data: data in spreadsheets, pdf, etc. Usually open data are csv. h1p://opensource.com/government/10/12/what-­‐“open-­‐data”-­‐means-­‐–-­‐and-­‐what-­‐it-­‐doesn’t  
  • 18. Opening Up Data Rules Ø  Keep it simple Ø  Engage early and engage often Ø  Address common fears and misunderstandings Four Steps Ø  Choose your Dataset(s) Ø  Apply an Open License Ø  Make the data available Ø  Make it discoverable
  • 19. Open Data Conditions Data Providers Requirements Distributing Open Data Ø  Attribution: data providers may require to receive credit. Ø  Integrity: data providers may require that users indicate if data change. Ø  Share-alike: data providers may impose that any dataset created using their data are also open. Ø  Data is machine-readable Ø  Data is available in bulk more than using an API. h1p://opendatahandbook.org/en/  
  • 21. OPEN  DATA  AND  GOVERNMENT  
  • 22. Some Open Data Applications Al  menos  77  países  cumplen  nivel  >  2   h1p://wheredoesmymoneygo.org/   22  
  • 23. Why Open Data? Citizens may unite so that their voices can be heard
  • 24. Why Open Data? Monrovia  África   h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
  • 25. Why Open Data? Tanzanía   h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
  • 26. Why Open Data? Tanzanía   h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
  • 27. Why Open Data? Tanzanía   h1p://www.ted.com/talks/sanjay_pradhan_how_open_data_is_changing_internaKonal_aid.html
  • 29. OPEN  DATA  AND  PUBLIC  HEALTH  
  • 30. Vaccines and Immunisation in Australia h1p://www.theguardian.com/society/datablog/interacKve/2013/oct/16/children-­‐vaccinaKon-­‐australia-­‐map  
  • 32. Applications of Open Data   h1p://pinterest.com/socrata/open-­‐data-­‐applicaKons/   Kenia  
  • 33. OPEN  DATA  AND  SOCIETY    
  • 34. ApplicaKons  of  Open  Data   h1p://www.crimemapping.com/  
  • 36. OPEN  DATA  AND  FINANCES  
  • 39. OPEN  DATA  AND  ENVIRONMENT  
  • 40. Why Open Data? Improve  Public  Services   Smart  CiKes   Mejorar  la   Administración  Pública   40  
  • 44. Applications of Open Data   h1p://opendatachallenge.org/    
  • 45. LINKED  OPEN  DATA   h1p://www.ted.com/talks/lang/en/Km_berners_lee_the_year_open_data_went_worldwide.html  
  • 46. What to do with Open Data? 46  
  • 47. What to do with Open Data? At  least  77  countries  comply  level  >  2   h1p://www.slideshare.net/mgarrigap/opendata-­‐en-­‐el-­‐ararteko   47  
  • 48. What to do with Open Data? At least 11 countries comply level > 4 h1p://www.slideshare.net/mgarrigap/opendata-­‐en-­‐el-­‐ararteko   48  
  • 49. What to do with Open Data?   h1p://www.theguardian.com/news/datablog/2013/oct/28/uk-­‐top-­‐open-­‐data-­‐index-­‐how-­‐countries-­‐compare#!  
  • 51. Bottom 10 by Open Data Index Score   h1p://www.theguardian.com/news/datablog/2013/oct/28/uk-­‐top-­‐open-­‐data-­‐index-­‐how-­‐countries-­‐compare  
  • 52.
  • 53.
  • 54. Local  Governments  must  use   Open  Data  to  stay  connected   with  the  ciKzens!  
  • 55. MoKvaKon  SemanKc  Web  EvoluKon   The Linked Open Data cloud, using the Web to connect related data that was not previously linked! Published Data are enhanced with semantics! Standards to annotate and describe data: XML, RDF, RDFS, OWL. Standards to query data: SPARQL. Ontologies representing almost any domain. Hyperlink-based systems. Protocols: http, uri, html Documents and data were published Arpanet: four servers connected Files were transferred Tools: ftp, telnet, e-mail 80’s IRMLs  2010-­‐ESWC  2010   90’s 00’s Now
  • 56. The Linked Open Data Cloud • Explosion in the number of:     – Linking Open Data resources and databases – Different quality parameters. Molecular databases 1170, 95 more – Controlledthan 2008 and 110 more than the year before ! vocabularies: – MeSH, GO, PO… tools published Services and – Highly interconnected by these databases follow a similar progression! data sources: In October 2007, Cloud of Linked Data Different Sizes datasets consisted of over two billion RDF triples, Many links which were interlinked by over two million RDF links. • Different in- and outBy May 2009 this had grown to 4.2 billion RDF triples, degrees, etc interlinked by around 142 billions RDF links! Today • Biological Web: large the Linked Open Data cloud has at least 295 datasets, datasets of linking data. 31,634,213,770  triples, and 503,998,829  links. • Genes, Diseases, Clinical Drugs, Proteins, and so on.
  • 57.
  • 58.
  • 59.
  • 61. LINKED  DATA  IN  JOURNALISM  
  • 62. Open Data in Journalism   Ø It may be trendy but not new. Ø Open Data implies Open Data Journalism. Ø Data is not necessarily curated. Ø Bigger Datasets and Small Things. Ø Data Journalism is 80% perspiration, 10% great ideas, 10% output. Ø Long and short-form. Ø Anyone can do it. Ø Visualization is important. Ø Data publishers do not have to be programmers. Ø It is all about stories. h1p://www.theguardian.com/news/datablog/2011/jul/28/data-­‐journalism  
  • 63. Breaking   News   Open   Data   Running   Events   Shared  Data   Open Data in Journalism
  • 64. Breaking   News   Open   Data   Open Data in Journalism Running   Events   Shared  Data   •  Data Cleansing •  Conflict Resolution Data  IntegraKon   SemanKficaKon   •  Meta-Data Annotation •  Vocabularies •  Visualization •  Publishing the Story PublicaKon  
  • 65. Meta-Data BBC News   This will help users to find news content about the stories they want to know about and ultimately help to open up references to the data contained in those stories. h1p://www.bbc.co.uk/blogs/internet/posts/News-­‐Linked-­‐Data-­‐Ontology  
  • 66. Data Management ToolsBBC News   h1p://www.bbc.co.uk/blogs/internet/posts/Linked-­‐Data-­‐ConnecKng-­‐together-­‐the-­‐BBCs-­‐Online-­‐Content  
  • 67.
  • 69.
  • 70. More   Ontologies  to   represent   Meta-­‐Data  
  • 71.
  • 72.
  • 74. Challenges  for                                                                    Linked  Data   Visualization   •  Enabling  user  interacKon   –  Users  must  be  able  to  navigate  through  the  data  by  exploiKng  the   connecKons  between  Linked  Data  resources   –  The  user  might  edit  the  underlying  data  to  enrich  it  by:     •  CreaKng  addiKonal  metadata   •  HighlighKng  or  correcKng  errors   •  ValidaKng  data   •  SupporKng  data  reusability   –  The  output  (the  plo1ed  data  or  the  visualizaKon  itself)  might  be   encoded  using  standard  ontologies  and  vocabularies       •  Scalability   –  Linked  Data  visualizaKon  techniques  should  support  the  display  of   large  amount  of  data  in  an  efficient  way   EUCLID  –  InteracKon  with  Linked  Data   74  
  • 75. Challenges  for                                                                    Linked  Open   Data  Visualization   •  ExtracKng  data  from  different  repositories   –  A  Linked  Data  set  might  be  parKKoned  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  properKes   EUCLID  –  InteracKon  with  Linked  Data   75  
  • 76. Linked  Open  Data  VisualizaKon  Techniques     View   EUCLID  –  InteracKon  with  Linked  Data   76  
  • 77. Comparison  of                                                                                                       A1ributes  /  Values   Bar/column  chart     Pie  chart   Allows  the  comparison  of  values  of   different  categories.       Useful  for  performing  comparison   of  percentages  or  proporKons.       Image  source:  h1p://musicbrainz.fluidops.net   Image  source:  h1p://mbostock.github.io/protovis/       Line  chart   Histogram   Allows  visualizing  data  as  a  series  of   data  points,  where  the  measurement   points  (x-­‐axis)  are  ordered.       Graphical  representaKon  of  the   distribuKon  of  the  data.     Image  source:  h1p://mbostock.github.io/protovis/       Image  source:  h1p://musicbrainz.fluidops.net   EUCLID  –  InteracKon  with  Linked  Data   77  
  • 78. Analysis  of                                          RelaKonships  and   Hierarchies     Graph     Arc  diagram   The  data  entries  are  represented  as   nodes  and  the  links  as  edges.         The  nodes  are  displayed  in  one   dimension,  and  the  arcs  represent   the  connecKons.       Adjacency  Matrix  diagram   Node-­‐link  visualizaKons   The  nodes  are  displayed  as  rows  and   columns,  and  the  links  between  the   nodes  are  entries  in  the  matrix.   The  data  is  organized  in  hierarchies.     Source  of  images:  h1p://mbostock.github.io/protovis/       EUCLID  –  InteracKon  with  Linked  Data   78  
  • 79. Analysis  of                                          RelaKonships  and   Hierarchies  (2)     Space-­‐filling  techniques   Treemaps   Icicles  and  sunburst   Subdivide  area  into  rectangles.   Hierarchies  are  represented  by   adjacencies.     Circle-­‐packing       Rose  diagrams   Containment  is  used  to  represent  the   hierarchies.   Areas  are  equal  angles  and  the  data   is  represented  by                                                             the  extension  of                                                                                       the  area.   Source  of  images:  h1p://mbostock.github.io/protovis/       EUCLID  –  InteracKon  with  Linked  Data   79  
  • 80. Analysis  of    Temporal  or  Geographical   Events     ConKnuous  data  in  Kme   Timeline     Discrete  data  points  in  Kme   Source:  h1p//musicbrainz.fluidops.net   Source:  h1p://www.ko1ke.org/08/08/2008-­‐movie-­‐box-­‐office-­‐chart   Display  geo-­‐points  on  a  map   Choropleth  maps   Dorling  cartograms   Aggregate  data  by   geographical  area   Aggregate  data  and  replace   each  area  with  a  circle     Maps   LocaKon  maps   Source:  Google  Map  API   Source:  h1p//musicbrainz.fluidops.net   EUCLID  –  InteracKon  with  Linked  Data   Source:  h1p://mbostock.github.io/protovis/       80  
  • 83. Tasks  to  be  Solved  …   Traverse and Consume Linked Data from the LOD cloud or locally. SPARQL endpoints have been developed to access data from the LOD cloud. 83  
  • 85. select  disKnct  *  where  {<h1p://dbpedia.org/resource/Venezuela>  ?p  ?o}    
  • 86. h1p://dbpedia.org/sparql   All  the  informaKon  related  to  Venezuela   SPARQL  Query  
  • 87.
  • 89. SPARQL  Endpoint    URL   SPARQL  Query  
  • 90.
  • 92. Data:   foaf:made   dbpedia:   The_Beatles   foaf:made   foaf:made   <h1p://   musicbrainz.org/ record/...>   dc:Ktle   <h1p://   musicbrainz.org/ record/...>   dc:Ktle   "Help!"   "Abbey  Road"   <h1p://   musicbrainz.org/ record/...>   dc:Ktle   "Let  It  Be"  
  • 93. SELECT  ?x  ?name  ?mbox  ?country  ?reviewer  ?product  ?title   WHERE  {     <http://www4.wiwiss.fu-­‐berlin.de/bizer/bsbm/v01/instances/dataFromRatingSite293/ Review2883011>  rev:reviewer  ?x  .        ?x  <http://www.w3.org/1999/02/22-­‐rdf-­‐syntax-­‐ns#type>  <http://xmlns.com/foaf/0.1/Person>  .      ?x  <http://xmlns.com/foaf/0.1/name>  ?name  .      ?x  <http://xmlns.com/foaf/0.1/mbox_sha1sum>  ?mbox  .      ?x  <http://www4.wiwiss.fu-­‐berlin.de/bizer/bsbm/v01/vocabulary/country>  ?country  .      ?reviewer  <http://purl.org/stuff/rev#reviewer>  ?x  .      ?reviewer  <http://www4.wiwiss.fu-­‐berlin.de/bizer/bsbm/v01/vocabulary/reviewFor>  ?product  .      ?reviewer  <http://purl.org/dc/elements/1.1/title>  ?title  }  
  • 95. APPLICATIONS  DEVELOPED  AT  USB   FEDERATED  QUERIES  
  • 96. ANAPSID   SPARQL-DQP   Federations of Endpoints
  • 97. h1ps://github.com/anapsid/anapsid   Federated Queries ANAPSID   “Genes and diseases that have been studied for drugs tested in clinical trials where Breast Cancer was studied” SELECT  DISTINCT  ?D1?TGD  ?GN1  ?GN2   WHERE  {    ?CT1  <http://data.linkedct.org/resource/linkedct/condition>  ?C1  .    ?CT1<http://data.linkedct.org/resource/linkedct/intervention>  ?I  .      ?CT1<http://data.linkedct.org/resource/linkedct/intervention>  ?I  .    ?I<http://data.linkedct.org/resource/linkedct/intervention_type>  "Drug"  .    ?C1  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>  ?D1  .    ?I  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>  ?I1  .    ?C  <http://data.linkedct.org/resource/linkedct/condition_name>  "Breast  Cancer"  .    ?CT  <http://data.linkedct.org/resource/linkedct/intervention>  ?I  .    ?CT  <http://data.linkedct.org/resource/linkedct/condition>  ?A4  .    ?II  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/target>  ?TGD  .    ?TGD  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/genbankIdGene>  ?GN1  .    ?D1  <http://www4.wiwiss.fu-­‐berlin.de/diseasome/resource/diseasome/associatedGene>  ?GN2  .   }   Life Sciences Query: 97  
  • 98. Federated Queries h1ps://github.com/anapsid/anapsid   ANAPSID   SELECT  DISTINCT  ?D1  ?TGD  ?GN1  ?GN2   WHERE  {            {  SERVICE  <http://virtuoso.bd.cesma.usb.ve/sparql>  {                    ?C1  <http://data.linkedct.org/resource/linkedct/condition_name>  "Breast  Cancer"  .                    ?C1  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>  ?D1  .                    ?C3  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>  ?D1  .                    ?CT3  <http://data.linkedct.org/resource/linkedct/condition>  ?C3  }}  .              {  SERVICE  <http://virtuoso.bd.cesma.usb.ve/sparql>  {                    ?C1  <http://data.linkedct.org/resource/linkedct/condition_name>  "Breast  Cancer"  .                    ?I  <http://data.linkedct.org/resource/linkedct/intervention_type>  "Drug"  .                    ?CT1  <http://data.linkedct.org/resource/linkedct/condition>  ?C1  .                    ?CT1  <http://data.linkedct.org/resource/linkedct/intervention>  ?I  }}  .              {  SERVICE  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/>  {                    ?I1  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/target>  ?TGD  .                    ?TGD  <http://www4.wiwiss.fu-­‐berlin.de/drugbank/resource/drugbank/genbankIdGene>  ?GN1  }}  .              {  SERVICE  <http://virtuoso.bd.cesma.usb.ve/sparql>  {                    ?I  <http://data.linkedct.org/resource/linkedct/intervention_type>  "Drug"  .                    ?I  <http://www.w3.org/2000/01/rdf-­‐schema#seeAlso>  ?I1  .                    ?CT3  <http://data.linkedct.org/resource/linkedct/intervention>  ?I  .                    ?CT3  <http://data.linkedct.org/resource/linkedct/condition>  ?C3  }}  .     }     S1:   S2:   S3:   S4:   98  
  • 100. ANAPSID   ANAPSID   ANAPSID   h1p://silurian.thalassa.cbm.usb.ve/  
  • 101. “Drugs that possibly target Leukemia” SELECT  DISTINCT  ?drug1         WHERE  {   ?drug1  drugbank:possibleDiseaseTarget   diseasome:673  .         ?drug1  drugbank:target  ?o.           ?o  drugbank:genbankIdGene  ?g.           ?o  drugbank:locus  ?l.           ?o  drugbank:molecularWeight  ?mw.           ?o  drugbank:hprdId  ?hp.           ?o  drugbank:swissprotName  ?sn.           ?o  drugbank:proteinSequence  ?ps.         ?o  drugbank:generalReference  ?gr.           ?drug  drugbank:target?o.           ?drug  drugbank:synonym?o1  .      OPTIONAL  {          ?drug  owl:sameAs  ?drug5  .                        ?drug5  rdf:type  dbcategory:Drug  .                        ?drug  drugbank:keggCompoundId  ?cpd  .                          ?enzyme  kegg:xSubstrate  ?cpd  .                          ?enzyme  rdf:type  kegg:Enzyme  .                          ?reaction  kegg:xEnzyme  ?enzyme  .                        ?reaction  kegg:equation  ?equation  .       }  }   h1p://silurian.thalassa.cbm.usb.ve/   101  
  • 102. “Drugs that possibly target Leukemia” SELECT  DISTINCT  ?drug1         WHERE  {   ?drug1  drugbank:possibleDiseaseTarget   diseasome:673  .         ?drug1  drugbank:target  ?o.           ?o  drugbank:genbankIdGene  ?g.           ?o  drugbank:locus  ?l.           ?o  drugbank:molecularWeight  ?mw.           ?o  drugbank:hprdId  ?hp.           ?o  drugbank:swissprotName  ?sn.           ?o  drugbank:proteinSequence  ?ps.         ?o  drugbank:generalReference  ?gr.           ?drug  drugbank:target?o.           ?drug  drugbank:synonym?o1  .      OPTIONAL  {          ?drug  owl:sameAs  ?drug5  .                        ?drug5  rdf:type  dbcategory:Drug  .                        ?drug  drugbank:keggCompoundId  ?cpd  .                          ?enzyme  kegg:xSubstrate  ?cpd  .                          ?enzyme  rdf:type  kegg:Enzyme  .                          ?reaction  kegg:xEnzyme  ?enzyme  .                        ?reaction  kegg:equation  ?equation  .       }  }   h1p://silurian.thalassa.cbm.usb.ve/   102  
  • 103. “Drugs that possibly target Leukemia” SELECT  DISTINCT  ?drug1         WHERE  {   ?drug1  drugbank:possibleDiseaseTarget   diseasome:673  .         ?drug1  drugbank:target  ?o.           ?o  drugbank:genbankIdGene  ?g.           ?o  drugbank:locus  ?l.           ?o  drugbank:molecularWeight  ?mw.           ?o  drugbank:hprdId  ?hp.           ?o  drugbank:swissprotName  ?sn.           ?o  drugbank:proteinSequence  ?ps.         ?o  drugbank:generalReference  ?gr.           ?drug  drugbank:target?o.           ?drug  drugbank:synonym?o1  .      OPTIONAL  {          ?drug  owl:sameAs  ?drug5  .                        ?drug5  rdf:type  dbcategory:Drug  .                        ?drug  drugbank:keggCompoundId  ?cpd  .                          ?enzyme  kegg:xSubstrate  ?cpd  .                          ?enzyme  rdf:type  kegg:Enzyme  .                          ?reaction  kegg:xEnzyme  ?enzyme  .                        ?reaction  kegg:equation  ?equation  .       }  }   h1p://silurian.thalassa.cbm.usb.ve/   103  
  • 104. APPLICATIONS-­‐   LINK  PREDICTION  AND  PATTERN   DISCOVERY    
  • 105. Tasks  to  be  Solved  …(2)   Patterns of connections between people to understand functioning of society.
  • 106. !#
  • 107. $%% #
  • 108. Topological properties of graphs can be used to identify patterns that reveal phenomena, anomalies and potentially lead to a discovery. A significant increase of graph data in the form of social biological information. 105  
  • 109.
  • 111. Pa1erns  or  Signatures   Brentuzumab_vedoKn   And  Catumaxomab     108  
  • 112. Annotation Similarity between two genes based on shared GO annotations Vacuolar   GO  Paths   Membrane   Vacuolar   Membrane   Golgi     apparatus   Plant-­‐type   vacuole   Chloroplast   Gene   AtVHA-­‐C5   Vacuole     proton-­‐   TransporKng     V-­‐type     ATPase  ,  V1     domain     Gene   AtVHA-­‐C   Chloroplast   Vacuole   GO  Terms   Vacuole   GO  Terms   109  
  • 113.   Pa1erns  or  Signatures  between   genes  AtVHA-­‐C5  and  AtVHA-­‐C     110  
  • 114.
  • 115. Drug-Target Interaction Network     Pa1erns   Between   InteracKons     PotenKal  new   interacKon   112  
  • 116. Patterns of connections between people to understand functioning of society. 113  
  • 117.
  • 119. Conclusions Ø  Open Data: ü  Transparency ü  Interoperability ü  Avoid Corruption ü  Impulse research and development ü  Data Quality Ø  Linked Open Data: ü  RDF data ü  Linked to existing datasets ü  Endpoints can be used to access data 116  
  • 120. Conclusions Ø Open Data Applications: ü Citizens can developed applications to take control of their lives. Ø (Linked) Open Data can be used: Ø Link Prediction Ø Discover Complex Patterns. 117  
  • 122. THANKS! QUESTIONS Maria-Esther Vidal Universidad Simón Bolívar mvidal@ldc.usb.ve   h1p://www.ldc.usb.ve/~mvidal   Twi1er  @Maria11576561   Skype:  mevs2006   119