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<foaf:Person rdf:ID="me">
<foaf:name>Stian Danenbarger</foaf:name>
<foaf:givenname>Stian</foaf:givenname>
<foaf:family_name>Danenbarger</foaf:family_name>
<foaf:mbox rdf:resource="mailto:stian@bouvet.no"/>
<foaf:homepage rdf:resource="http://twitter.com/stidan"/>
<foaf:workplaceHomepage rdf:resource="http://www.bouvet.no"/>
</foaf:Person>



"MASKINER SOM LESER"
"semantisk"
RE - SEARCH ?
maskinell informasjonsekstrahering
Securities              Ship                                    India
securities   94.96324   ship        109.41212                   india            91.74842
firm         88.74591   coast       93.70902                    singh            50.34063
drexel       78.33697   guard       82.11109                    militants        49.21986
investment   75.51504   sea         77.45868                    gandhi           48.86809
bonds        64.23486   boat        75.97172                    sikh             47.12099
sec          61.89292   fishing     65.41328                    indian           44.29306
bond         61.39895   vessel      64.25243                    peru             43.00298
junk         61.14784   tanker      62.55056                    hindu            42.79652
milken       58.72266   spill       60.21822                    lima             41.87559
firms        51.26381   exxon       58.35260                    kashmir          40.01138
investors    48.80564   boats       54.92072                    tamilnadu        39.54702
lynch        44.91865   waters      53.55938                    killed           39.47202
insider      44.88536   valdez      51.53405                    india's          39.25983
shearson     43.82692   alaska      48.63269                    punjab           39.22486
boesky       43.74837   ships       46.95736                    delhi            38.70990
lambert      40.77679   port        46.56804                    temple           38.38197
merrill      40.14225   hazelwood   44.81608                    shining          37.62768
brokerage    39.66526   vessels     43.80310                    menem            35.42235
corporate    37.94985   ferry       42.79100                    hindus           34.88001
burnham      36.86570   fishermen   41.65175                    violence         33.87917

                                    (Sample aspect lists from AP data, 100-Aspect Model)
technorati.com/tag/<tag>…:


•   ”hovefestivalen”: 113 bloggposter, 739 bilder
•   ”hovefestivalen08”: 27 bloggposter, 20 bilder
•   ”hove+’08”: 19 bloggposter, 280 bilder
•   ”hovefestival”: 14 bloggposter, 282 bilder
•   ”hove”: 68 norske bloggposter, ? bilder

•   ”haga”: 47 norske (og svenske) bloggposter, 2300 bilder
•   ”hagasaken”: 0 bloggposter, 0 bilder
•   ”Åslaug+Haga”: 26 norske bloggposter, 1 bilde
•   ”Aslaug+Haga”: 4 norske bloggposter, 0 bilder
"semantisk"
Identity
Reference
Expressivity




                                                              RDF /
                                                            Topic Maps




                                    Taxonomies, thesauri


                  Flat list, tags



               No model              Closed model          Open model
Meningsfylte sammenstillinger forutsetter delt
semantikk…
”Nye
          ”Jeg vet
                                                     sammenhenger
           hva jeg
                                                       mellom…”
             ser
          etter…”                        ”Alt nytt
                                          om…”



(Gjen)finne                   Forstå                 Følge med


                ”Oversikten      ”Sammenhengen
                  over…”            mellom…”
Atom/RSS (inkl. podcasts)
                                           SMS/MMS
                                           IM/XMPP            ”Abonnere
                                           Epost
                                           Kalendersynk.      på et søk!”
                                           …


              • Registrering              • “Discovery”
              • Notifikasjon              • “Trust metrics”
              • Indeksering               • Filtrering
              • Aggregering               • Abonnement


   • Dele                                           • Finne
                                 Indeks             • Sammenstille
   • Tilgjengeliggjøre




PRODUSENT                      MEDIATOR                        KONSUMENT
”
   In some sense when people come to
Google, that’s exactly what they’re asking
   for — our editorial judgment. They’re
expressed via algorithms. When someone
   comes to Google, the only way to be
 neutral is either to randomize the links or
 to do it alphabetically – Matt Cutts, Google,
            til Wired 3. mars 2011
Tom Coates, Yahoo: ”The web as it was”…
Tom Coates, Yahoo: ”Web of the future?”
<foaf:Person rdf:ID="me">
<foaf:name>Stian Danenbarger</foaf:name>
<foaf:givenname>Stian</foaf:givenname>
<foaf:family_name>Danenbarger</foaf:family_name>
<foaf:mbox rdf:resource="mailto:stian@bouvet.no"/>
<foaf:homepage rdf:resource="http://twitter.com/stidan"/>
<foaf:workplaceHomepage rdf:resource="http://www.bouvet.no"/>
</foaf:Person>
"Maskiner som leser" (Nordic Research 2011)

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"Maskiner som leser" (Nordic Research 2011)

  • 1.
  • 2. <foaf:Person rdf:ID="me"> <foaf:name>Stian Danenbarger</foaf:name> <foaf:givenname>Stian</foaf:givenname> <foaf:family_name>Danenbarger</foaf:family_name> <foaf:mbox rdf:resource="mailto:stian@bouvet.no"/> <foaf:homepage rdf:resource="http://twitter.com/stidan"/> <foaf:workplaceHomepage rdf:resource="http://www.bouvet.no"/> </foaf:Person> "MASKINER SOM LESER"
  • 3.
  • 4.
  • 5.
  • 6.
  • 7.
  • 9.
  • 10.
  • 11.
  • 12.
  • 13.
  • 14.
  • 15.
  • 18.
  • 19. Securities Ship India securities 94.96324 ship 109.41212 india 91.74842 firm 88.74591 coast 93.70902 singh 50.34063 drexel 78.33697 guard 82.11109 militants 49.21986 investment 75.51504 sea 77.45868 gandhi 48.86809 bonds 64.23486 boat 75.97172 sikh 47.12099 sec 61.89292 fishing 65.41328 indian 44.29306 bond 61.39895 vessel 64.25243 peru 43.00298 junk 61.14784 tanker 62.55056 hindu 42.79652 milken 58.72266 spill 60.21822 lima 41.87559 firms 51.26381 exxon 58.35260 kashmir 40.01138 investors 48.80564 boats 54.92072 tamilnadu 39.54702 lynch 44.91865 waters 53.55938 killed 39.47202 insider 44.88536 valdez 51.53405 india's 39.25983 shearson 43.82692 alaska 48.63269 punjab 39.22486 boesky 43.74837 ships 46.95736 delhi 38.70990 lambert 40.77679 port 46.56804 temple 38.38197 merrill 40.14225 hazelwood 44.81608 shining 37.62768 brokerage 39.66526 vessels 43.80310 menem 35.42235 corporate 37.94985 ferry 42.79100 hindus 34.88001 burnham 36.86570 fishermen 41.65175 violence 33.87917 (Sample aspect lists from AP data, 100-Aspect Model)
  • 20. technorati.com/tag/<tag>…: • ”hovefestivalen”: 113 bloggposter, 739 bilder • ”hovefestivalen08”: 27 bloggposter, 20 bilder • ”hove+’08”: 19 bloggposter, 280 bilder • ”hovefestival”: 14 bloggposter, 282 bilder • ”hove”: 68 norske bloggposter, ? bilder • ”haga”: 47 norske (og svenske) bloggposter, 2300 bilder • ”hagasaken”: 0 bloggposter, 0 bilder • ”Åslaug+Haga”: 26 norske bloggposter, 1 bilde • ”Aslaug+Haga”: 4 norske bloggposter, 0 bilder
  • 21.
  • 22.
  • 23.
  • 27. Expressivity RDF / Topic Maps Taxonomies, thesauri Flat list, tags No model Closed model Open model
  • 28.
  • 30.
  • 31.
  • 32.
  • 33.
  • 34.
  • 35. ”Nye ”Jeg vet sammenhenger hva jeg mellom…” ser etter…” ”Alt nytt om…” (Gjen)finne Forstå Følge med ”Oversikten ”Sammenhengen over…” mellom…”
  • 36.
  • 37.
  • 38.
  • 39.
  • 40.
  • 41.
  • 42. Atom/RSS (inkl. podcasts) SMS/MMS IM/XMPP ”Abonnere Epost Kalendersynk. på et søk!” … • Registrering • “Discovery” • Notifikasjon • “Trust metrics” • Indeksering • Filtrering • Aggregering • Abonnement • Dele • Finne Indeks • Sammenstille • Tilgjengeliggjøre PRODUSENT MEDIATOR KONSUMENT
  • 43. In some sense when people come to Google, that’s exactly what they’re asking for — our editorial judgment. They’re expressed via algorithms. When someone comes to Google, the only way to be neutral is either to randomize the links or to do it alphabetically – Matt Cutts, Google, til Wired 3. mars 2011
  • 44.
  • 45. Tom Coates, Yahoo: ”The web as it was”…
  • 46. Tom Coates, Yahoo: ”Web of the future?”
  • 47.
  • 48. <foaf:Person rdf:ID="me"> <foaf:name>Stian Danenbarger</foaf:name> <foaf:givenname>Stian</foaf:givenname> <foaf:family_name>Danenbarger</foaf:family_name> <foaf:mbox rdf:resource="mailto:stian@bouvet.no"/> <foaf:homepage rdf:resource="http://twitter.com/stidan"/> <foaf:workplaceHomepage rdf:resource="http://www.bouvet.no"/> </foaf:Person>

Notas del editor

  1. «Worldwide Information Growth Ticker» fra http://www.emc.com/leadership/programs/digital-universe.htm
  2. Lisens: http://creativecommons.org/licenses/by-sa/3.0/no/ (for alle deler av presentasjonen der jeg selv er opphavsmann. Alle skjermbilder, foto og video har tredjepartsopphav, og omfattes derved ikke av dette)
  3. Eksempel 1a: LMK.com (Hearst Corp), temasentriske nyheter og personalisering for mobil og ipad
  4. Eksempel 1b: Kategoriseringsteknologien bak LMK.com (evri.com)
  5. Eksempel 2a: «Menneskelesbar» HTML-artistside på BBC Music (BBC)http://www.bbc.co.uk/music/artists/e20747e7-55a4-452e-8766-7b985585082d
  6. Eksempel 2a: «Maskinlesbar» utgave av samme artistside på BBC Music (BBC)http://www.bbc.co.uk/music/artists/e20747e7-55a4-452e-8766-7b985585082d.rdf
  7. Eksempel 3: Åpent tilgjengelig tjeneste som gjenkjenner entiteter (personer, organisasjoner, steder) og relasjoner i tekst (Thomson Reuters)http://www.opencalais.com/http://semanticproxy.com/
  8. «semantisk» i denne sammenhengen = hva en tekst handler om
  9. De foregående eksemplene koster mye penger…
  10. Hvorfor investerer disse organisasjonene så mye penger i teknologi «ingen» har hørt om?
  11. Kanskjenoen hint her…?2 Exabytes: volume of information generated in 1999. 30 % progression per year -&gt; doubling time 2,3 yrs (100*ln2/30)-&gt; Exponensiellvekst….!Exabyte (EB) 1 000 000 000 000 000 000 (10^18) bytes
  12. Kanskjenoen hint her…?Video: http://www.youtube.com/watch?v=iIKPjOuwqHoBasertpå:Martin Hilbert &amp; PriscilaLópez (2011). The World&apos;s Technological Capacity to Store, Communicate, and Compute Information. Science, 332(6025), 60-65.http://http://www.sciencemag.org/content/332/6025/60 (accessible throughhttp://www.martinhilbert.net/WorldInfoCapacity.html)It was conceptualized, produced, and animated by Martin Hilbert, Gavin Wright, Stuart Allan, and Dan St. Pierre (www.proof-inc.com), with the support from The Economist and USC&apos;s Annenberg School of Communication.
  13. MerbakgrunnWashington Post: “Rise of the digital information age”http://www.washingtonpost.com/wp-dyn/content/graphic/2011/02/11/GR2011021100614.htmlMengdenanalogtlagretinformasjonavtar10 TB: The print collections of the U.S. Library of Congress. Terabyte (TB) 1 000 000 000 000 (10^12) bytes 200 Petabytes: All printed material. Petabyte (PB) 1 000 000 000 000 000 (10^15) bytes
  14. Konsekvens: Bruksmønstre i endring!Mer og mertidpåmedier og informasjonhttp://www.thewrap.com/media/column-post/people-spend-more-12-day-consuming-media-study-finds-21005…men relativt lite avdettepånyheter, og mindre og mindrepåtradisjonelleplattformer…! http://www.people-press.org/2010/09/12/americans-spending-more-time-following-the-news/
  15. I en oppmerksomhetsøkonomi -&gt;Forretningsmodeller i endring!Fylle behov =&gt; god økonomi
  16. Konsekvens for researchere: Overveldendeomfang!30 Megabytes: 30 cm of shelved books memorized information/year by an expert Megabyte (MB) 1 000 000 (10^6) bytes 2 Kilobyte (KB) 2 000 bytes: A Typewritten page. 10 TB: The print collections of the U.S. Library of Congress. Terabyte (TB) 1 000 000 000 000 (10^12) bytes 200 Petabytes: All printed material. Petabyte (PB) 1 000 000 000 000 000 (10^15) bytes 2 Exabytes: volume of information generated in 1999. 30 % progression per year Exabyte (EB) 1 000 000 000 000 000 000 (10^18) byteshttp://www.sxc.hu/photo/324541
  17. Verktøy for research«Anvendt datalingvistikk»(Jigsaw, Georgia Tech, USA)
  18. EntitetsgjenkjenningKonsepter og relasjonerAutokategorisering/-autoklassifikasjon (modelldrevet)textUrgy (Norge)
  19. Autokategorisering/-klassifikasjon (datadrevet)(Recommind, USA/Tyskland)
  20. DisambigueringProblemet med tagging (fra technorati.com)
  21. Visualisering (personer, organisasjoner, steder, tema)Silobreaker (Sverige)
  22. TrendanalyseVisualiseringSilobreaker (Sverige)
  23. «Lytte» på sosiale medier:Løpende analyse av positive og negative holdninger (sentiment)Eksempel fra Twitter under TV-debattene ifb valget i StorbritanniaLinguamatics (UK)
  24. Tilbake til «semantikk» – litt mer inngående
  25. Semantisk teknologi: Du slipper å lete etter «nåler i høystakken» – får entitetene (personer, organisasjoner, steder, tema, …) «servert» i strukturert (maskinprosesserbar) formhttp://www.stockxpert.com/browse_image/view/404665
  26. …Ikke bare entiteter, men også sammenhengene («relasjonene») mellom dem…http://www.stockxpert.com/browse_image/view/33052791
  27. Med åpne modeller kan mange små strukturer sammenstilles til store, sammenhengende strukturer
  28. Det finnes allerede et omfattende «økosystem» av åpne, semantiske modeller («Linked Open Data») som referer til hverandre.«Kulene» i diagrammet over representerer modeller, og størrelsen antyder omfanget av publiserte data.«DBPedia» (midten) er en tjeneste som tilgjengelliggjør strukturert informasjon som er ekstrahert fra Wikipedia – et naturlig nav.Wikipedia-artikler består for det meste av tekst, men de inneholder også strukturert informasjon, for eksempel informasjonsbokser, tabeller, kategorier, geografiske koordinater og eksterne henvisninger til informasjon utenfor Wikipedia (eksterne lenker). Disse informationene kan ekstraheres og benyttes som datagrunnlag for mer kompliserte forespørsler. I november 2010 hadde DBpedia 3,4 millioner data-sett med mer enn en milliard enkeltdata
  29. For de mer teknisk orienterte:Teknologien og standardene for sammenstilling («informasjonsintegrasjon») og utvekslinghar blitt etablert på under 20 år, helt opp til og med semantikklaget, som er temaet i dag.Vi har ennå en jobb å gjøre rundt nivåene over («pragmatikk» knytter seg for eksempel til vår evne til å tolke mening ut fra kontekst, dvs. ikke ordrett)…Videoen jeg nå skal vise, illustrerer likevel godt hvor langt vi er kommet.(fig.: Michael Bergman)
  30. Watson er etdatasystem utviklet som et ledd i IBMs såkalte DeepQA prosjekt.I 2011konkurrerte Watson på quiz-showetJeopardy!I en tre-episoders match slo Watson mestrene Brad Rutter, som har vunnet mest penger noensinne i Jeopardy!, og Ken Jennings, som har innehatt mestertittelen lengst.Video:http://www.youtube.com/watch?v=WFR3lOm_xhEWatson hadde problemer med å svare på noen få kategorier, særlig de som bare inneholdt noen få ord. For hver ledetråd vises Watsons tre mest sannsynlige svar på TV-skjermen. Watson hadde tilgang til 200 millioner sider strukturert og ustrukturert innhold (blant annet gjennom det nevnte «økosystemet» av åpne, semantiske modeller) gjennom sine fire terabyte med diskplass, inkludert den fullstendige teksten i Wikipedia, og var ikke koblet til Internett i løpet av spillet.
  31. Watson-teknologi er ennå ikke tilgjengelig for «folk flest», menVåren 2010 kjøpte Apple Siri (siri.com), som kan gi en liten pekepinn om liknende muligheter framover.En tidlig versjon er tilgjengelig via iPhone 4S.
  32. CTO i Siri, Tom Gruber, er kjent i sematikkmiljøet, bl.a. for sin definisjon av begrepet «ontologi».
  33. Nevnte endrede brukerbehov i starten…Brukerbehov – overordnet
  34. Silobreaker forsøker å besvare behovet automatisk, menstøter på et fundamentalt problem med nyheter i dag…
  35. Rapport for Associated Press i 2008: http://www.ap.org/newmodel.pdfIntervjuobjektene savner utdypinger, forklaringer, helhet og sammenhengNyhetene settes for lite i kontekst, framstår fragmentertLiten eller ingen lenking til mer infoBrukerne søker rundt etter mer utdypende info, finner bare mer av det samme
  36. Nettstedslogger viser at brukere beveger seg raskt rundt, og skanner myeHar gitt opphav til myten om brukerne som «gullfisk» med null konsentrasjonsevne…Men mye nyhetstekst er bakgrunnsstoff eller gjentakelse, og dette kunne vært bedre tilpasset web’ens premisser…Fig.:Jonathan Stray (AP) http://jonathanstray.com/short-doesnt-mean-shallow
  37. HistoriefortellingPå web’ens premisserFig.:Jonathan Stray (AP) http://jonathanstray.com/short-doesnt-mean-shallow
  38. Ikke grunt – «uendelig» dypt – med lenking til relevant info på andre nettstederFig.:Jonathan Stray (AP) http://jonathanstray.com/short-doesnt-mean-shallow
  39. Hele systemet endrer segFra industrisamfunnets «push» til informasjonssamfunnets «pull» - og bruksmønstrene på web’en er et godt eksempelhttp://www.johnhagel.com/paper_pushpull.pdfhttp://www.edgeperspectives.com/pop.htmlOppsummert: http://www.edgeperspectives.typepad.com/edge_perspectives/2005/10/from_push_to_pu.html
  40. Algoritmer som redaktører?http://jonathanstray.com/the-editorial-search-engine
  41. Om redaktørens (og researcherens) rolle:Kan nyhetene suppleres med research-materiale ogkunnskapen i et nyhetsrom,som skaper helhet og sammenheng?(-&gt; Fokusere mindre på forside, layout?)Semantiske modeller er skapt for dette (men krever gode verktøy)!(figur: AP)
  42. Digitaliseringen av informasjon har endret verden mye på kort tid-&gt; krever systemiske endringer som allerede er i gang
  43. …Og min påstand er at semantisk teknologi er en del av nøkkelen…! 
  44. Mer om Watson