Linked Data: some social challenges
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Linked Data: some social challenges

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Presentation given at "Global Interoperability and Linked Data in Libraries", University of Florence, 18 Jun 2012

Presentation given at "Global Interoperability and Linked Data in Libraries", University of Florence, 18 Jun 2012

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    Linked Data: some social challenges Linked Data: some social challenges Presentation Transcript

    • L ink ed Da ta social challengessom e tech & michele barbera <barbera@netseven.it> @barbz79it
    • 1
    •   TECH
    •   CHALLENGES2
    •   SOCIAL
    •   CHALLENGES3
    •   LINKED
    •   DATA
    •   ECONOMY
    • smart data now! Unità Web of Data <spaziodati.eu> <netseven.it> <fbk.eu>
    • Is
    •   the
    •   Semantic
    •   Web
    •   real?
    • no*.*
    •   I’m
    •   provocative
    • we
    •   aimed
    •   at
    •   this:
    • and
    •   failed*.*
    •   But
    •   produced
    •   ~170k
    •   research
    •   papers
    •   in
    •   11
    •   years,
    •   not
    •   bad!
    • Pizza
    •   ontology?!
    • well,
    •   not
    •   really
    •   failed...
    • we’re
    •   still
    •   working
    •   on
    •   it
    • less
    •   pizza
    •   more
    •   engineering
    • A
    •   little
    •   semantics
    •   goesa
    •   long
    •   way... Semantic
    •   Web Linked
    •   Data
    • Semantic
    •   Web Linked
    •   Data Web
    •   of
    •   Data
    • it’s
    •   not
    •   just
    •   technology
    • it’s
    •   definetely
    •   not
    •   AI
    • it’s
    •    just
    •    about
    •    linking
    •   things
    •   together
    • your web site DATA IS LESS VALUABLE WHEN SILOED
    • because
    •   value
    •   is
    •   in
    •   context
    • content
    •   is
    •   king
    • xcontent
    •   is
    •   king
    • linking
    • 
    •    t e chs o m e es i s s u
    • 1
    •   SCALABILITY
    • is
    •   it
    •   all
    •   about
    •   size? Flexibility,
    •   dinamicity,
    •   scalability by Giovanni Tummarello
    • dataspaces by Giovanni Tummarello
    • Large
    •   Scale
    •   RDF
    •   summaries 12M
    •   relationshipsClass Level http://test01.sindice.net/szydan/dataset-view/dataset/default/www.bbc.co.uk by Giovanni Tummarello
    • Large
    •   Scale
    •   RDF
    •   summaries 12M
    •   relationshipsClass Level 10B
    •   relationships http://test01.sindice.net/szydan/dataset-view/dataset/default/www.bbc.co.uk by Giovanni Tummarello
    • 2
    •   -
    •   streaming
    •   linked
    •   data
    • 3
    •   -
    •   versioning moved deleted SPOT
    •   THE
    •   DIFFERENCE
    • <self_promotion>
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication spaziodati.3scale.net Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication spaziodati.3scale.net Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • SIREn Data Collection Settings 500M web data documents  Cluster of 4 nodes (RDF, RDFa, Microformat, etc.)  2 nodes for indexing 200K datasets  2 nodes for querying 50B triples  Replication spaziodati.3scale.net Indexing Performance Services Full index construction takes  Keyword and structured queries approx 24 hours  Dataset search 436K triples / second  >> 99% uptime
    • </self_promotion>
    • som e
    •    so ci al is su es
    • 1
    •   THINKING
    •   IN
    •   THE
    •   GRAPH
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • 1
    •   -
    •   thinking
    •   in
    •   tables
    • thinking
    •   in
    •   tables u_id f_id 1 2 1 3 3 4 4 3 id name age affiliation 1 Michele 33 net7 2 Mario 32 unipi 3 Silvia 28 unifi 4 Irene 27 unitn Institution City net7 pisa unipi pisa unifi firenze unitn trento
    • thinking
    •   in
    •   graphs? pisa Firenze place Trento e e plac plac unipi net7 unifi e plac ks ks unitn wor wor friend frien ks d michele
    •   (33) wor silvia
    •   (28) frien d irene
    •   (27) fri end mario
    •   (32)
    • e.g.soci al
    •   gr ap hs Mario
    •   342-2345672 ,
    •   12
    •   Apr,
    •   Via
    •   san
    •   G Giovanni
    •   333-231345 iuseeppe
    •   34 0,
    •   Bologna,
    •   via
    •   UgoAnna
    •   328-3422345, 
    •   Bassi
    •   12 
    •   Trento,
    •   p.zza
    •   VerMamma
    •   050-342212 di
    •   11 4,
    •   PisaAntonio
    •   051-34245 6,
    •   Bologna
    • 2
    •   -
    •   A.A.A.**
    •   “you
    •   don’t
    •   know
    •   what
    •   you’re
    •   talking
    •   about”
    • AAA library wikidb scholarly
    •   community
    • tbl“The
    •   less
    •    inviting
    •   side
    •    of
    •    sharing
    •    is
    •   losing
    •   some
    •    control.
    •    Indeed,
    •    at
    •    each
    •    layer
    •    ---
    •    “Net,
    •    Web,
    •    or
    •    Graph
    •    ---
    •    we
    •    have
    •    ceded
    •   some
    •   control
    •   for
    •   greater
    •   benefits” “ It
    •    is
    •    about
    •    getting
    •    excited
    •    about
    •    “ connections,
    •   rather
    •   than
    •   nervous”
    • 3)info
    •   vs.
    •   non-info
    • http-range-14 http://example.com/resource/CNR http://example.com/page/CNR 303
    •   redirection? http://example.com/data/CNR http://www.cnr.it/homepage#CNR http://www.cnr.it/homepage hash
    •   uri?
    • caution! http://universities.org/italy#cnr ns:president <a_person> ns:department <some_department> ns:department <some_department> owl:sameAs http://www.example.com/cnr ns:creator <jonnhy
    •   web
    •   developer> ns:date 12
    •   Jun
    •   2011 ns:name “The
    •   Website”
    • 4)Open
    •   World
    •   Assumption
    • Kbase Seat
    •   14
    •   is
    •   reserved Seat
    •   27
    •   is
    •   reserved OWA CWA is
    •   seat
    •   28
    •   reserved?UNKNOWN NO
    • -
    •   OWA
    •   is
    •   not
    •   difficult
    •   to
    •   understand-
    •   OWA
    •   is
    •   good
    •   to
    •   deal
    •   with
    •   inconsistencies
    •   anduniversal
    •   systems-
    •   We’re
    •   more
    •   familiar
    •   with
    •   CW
    •   reasoning-
    •   many
    •   existing
    •   tools
    •   are
    •   CW
    • 
    •    D a ta d i e my? k o n na 
    •    L o e c
    • -
    •   ~
    •   300
    •   datasets-
    •   not
    •   frequently
    •   updated Linked
    •   Data-
    •   0,1
    •   %
    •   of
    •   the
    •   Web
    •   of
    •   Data
    • Web
    •   of
    •   Data
    • <h1 id="name"><span class="fn n"> <span class="given-name">Michele </span> <span class="family-name">Barbera</span> </span></h1>
    • www.rottentomatoes.com
    • Schema.orghttp://www.linkedopendata.it/schema-org-e-le-responsabilita-dei- monopolisti
    • G
    •   knowledge
    •   graph
    • Freebase
    •   +
    •   Geonames
    •   +
    •   DBpedia
    •   +
    •   schema.org
    •   +
    •   search
    •   statistics? opaque/hidden
    •   identifiers
    •   =
    •   not
    •   reusable
    • 5 billion 40%global data mobile phones 30 billion pieces of content shared growth in projected generated per year vs 5% on facebook every month 235 terabytes 15 out of 17 data collected by US library of Congress 60% potential increas in retailers’ sectors in US have more data stored per company than the US Library in april 2011 operating margins possible with big data of Congress BIG DATA AND INFO OVERLOAD IN USE IN 2010: 250$ billion potential annual value 600$ billion 300$ to Europe’s public sector potential annual consumer surplus from using billion administration - more than GDP of Greece personal location data globallypotential annual value to US health care (more than double the total annual 60% 140.000-190.000 more deep analytical talent positions health care potential increase and 1,5 million more data-savvy managers spending in Spain) in retailers’ operating margins need to take full advantage of big data possible wiith big data with big dat only in United States
    • “ “ The
    •   real
    •   value
    •   of
    •   the
    •   GKG
    •   may
    •   be
    •   in
    •   what
    •   gets
    •    deleted
    •   instead
    •   of
    •   what
    •   gets
    •   added. Paul
    •   Houle,
    •   http://lists.w3.org/Archives/Public/public-lod/2012Jun/0038.html
    • Open
    •    Data
    •    (and
    •    digital
    •    public
    •    goods)
    •   r e p r e s e n t s
    •    a n
    •    u n p r e c e d e n t e d
    •   opportunity
    •    to
    •    build
    •    a
    •    (local?
    •   vertical?)
    •    data
    •    economy
    •    and
    •    to
    •   preserve
    •   our
    •   cultural
    •   diversity
    • “ The
    •    gist
    •    of
    •    the
    •    matter
    •    is
    •    to
    •    turn
    •    large
    •    streams
    •    of
    •    data
    •    into
    •    added
    •   value
    •   for
    •   the
    •   public
    •   and
    •   private
    •   sector
    •   [...] Clearly,
    •    research,
    •    engineering,
    •    policy
    •    making
    •    for
    •    the
    •    Data
    •    Economy
    •    and
    •    the
    •    exploitation
    •    of
    •    the
    •    unprecedented
    •    wealth
    •    of
    •    “ data
    •   have
    •   become
    •   keys
    •   to
    •   the
    •   Future
    •   of
    •   Europe.
    • WE CAN DO IT!!!
    • Thank
    •   you.@barbz79it