linked data twitter mining architecture semantic web applications

1. @twitter Mining #MicroblogsUsing #SemanticTechnologies Selver Softic, Martin Ebner, Herbert Mühlburger , Thomas Altmann, Behnam Taraghi

2. Web 2.0 - well knownstory Web 2.0 technologiesbroughtuserscloserto Web … Wikis, Blogs, Forums … Podcasts, RSS, XML … … thenusersstarted togeneratecontent … Source: http:mediabistro.com

3. From Web toSocial Web Result = a vastofinformation Text, Pictures, Audio, Videos …. Communication, networking, exchangeofdata Web becamemore personal Cultural, geographicalandsocialbordersdisappeared Source: http://www.ignitesocialmedia.com

4. Social Media Boom!

6. Socialsitesaredatasilos source: www.pidgintech.com

7. But still disconnected ? source: www.pidgintech.com

8. Data is still captured in Walled Garden!

9. Statements Social Web relies on usersandcommunicationamongthem Whilecommunicatingusersproduceorconsumecontent Socialsitesaredatasilosrich on varietyofinformation Thisinformationcouldbeinterestingfor: monitoring of trends, advertising, statistics, reputation, news broadcasting , tagging … Thisdataiscaptured in Walledgarden !!!

10. Questions Howtousethisdatatogainmoreusefulinsights Whataretheadvantagesof online (offline) search on such dataandhowtoreachit in an uniform way Is itpossibletostructurize, connectandexposethedata in order tobeusedbyhumansandmachinesmoreefficiently Whatwould an architecturelooklikeforthisissue

11. Social Web Trends Microblogging SocialBookmarking Social Networking Social Marketing Sharing Photos, Videos … Source: http://socialwebresearch.com

12. Microblogs Microblogs Usedforcommunication,publishingandinformationexchange Simple forprocessing Information generatedbymany different users Socialuserrelations Tripartitecommunicationstructure Varietyofinformations Noboundariesbyculture,locationortechnology (mobile users) Twitter Most Popular Large amountoddata But limited According: http://an.kaist.ac.kr/traces/WWW2010.html 41.7 million user profiles, 1.47 billion social relations, 4,262 trending topics, and 106 million tweets

13. SemanticaspectsandTwitter Twitter User realtions Tweetsasshortinformationartefacts Communication withtripartitepattern Time relatedinformation Vocabularies SIOC, FOAF, Dublin Core

14. Linked Data andTwitter Twittercontainsinfos on: People, Organisations, Locations, Trends … LOD Cloudcontains Billionsoftriplesabout: Geolocations , dataaboutscience, government, commonknowledge, persons, news … Vocabularies MOAT, CommmonTag

15. Architecture model

16. Acquisition - Grabeeter

17. Grabeeter Search in your Tweets Filter your Tweets by date Search in your Tweets offline using the Grabeeter Client Filter your tweets offline using the Grabeeter Client Grabeeter provides an API

18. Triplification Module Author Date Content Reciever <tweet url="http://grabeeter.tugraz.at/tweet/199272" text="Sitting in Prater #vienna, launch party. Nice" screen_name="selvers" created="2010-08-19" twitterUrl="http://twitter.com/selvers/status/21606926237"/> RDF Store Triplifier

19. Triplification Module @prefix foaf: <http://xmlns.com/foaf/0.1/> . @prefix rdfs: <http://www.w3.org/2000/01/rdf-schema#> . @prefix rdf: <http://www.w3.org/1999/02/22-rdf-syntax-ns#> . @prefix sioc: <http://rdfs.org/sioc/ns#> . @prefix sioct: <http://rdfs.org/sioc/types#> . @prefix dcterms: <http://purl.org/dc/terms/#> . <http://twitter.com/selvers/status/21606926237> rdf:typesioct:MicroblogPost ; sioc:content "Sitting in Prater #vienna, launch party. Nice" ; sioc:has_creator <http://twitter.com/selvers/> ; foaf:maker <http://grabeteer.tugraz.at/foaf/selvers/> ; dcterms:created “2010-08-19” ; rdfs:sameAs <http://grabeeter.tugraz.at/tweet/199272> . <http://twitter.com/selvers/> rdf:typefoaf:Person ; foaf:name "SelverSoftic" ; foaf:depiction <http://a0.twimg.com/profile_images/905118560/f9e4b6eba.13070201_3_normal.jpg> ; foaf:knows <http://twitter.com/hmuehlburger/> ; foaf:knows <http://twitter.com/mhausenblas/> ; foaf:knows <http://twitter.com/mebner/> . …

20. Interlinking Module Hashtags (People, Organisation, Locations) MOAT, CommonTag Later NLP processedcontent, SILK Framework SELECT ?post ?content ?maker ?name WHERE { ?post rdf:typesioct:MicroblogPost; foaf:maker ?maker; ?makerfoaf:name ?name; sioc:content ?content. FILTER(regex(?content,#vienna)) } Classifier tag: tagName "vienna" ; moat: tagMeaning <http://dbpedia .org/resource/Vienna> tag: taggedResource <http://twitter.com/selvers/status/2160692623>

21. Analysis

22. Conclusions & Outlook Currentstateofthearttechnologiessufficetorealisetheproposedarchitectureparadigm Interlinkingwith LOD Cloud (Tweet-O-Sphere) Involving NLP Methods Sentiment classification (Re)TaggingofTweets Providing SPARQL Endpoint + Lookup Serviceasresearchinterface SocialSemantic Web Apps

23. Questions?