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Twarql Tapping Into the Wisdom of the Crowd Pablo N. Mendes, Pavan Kapanipathi, Alexandre Passant I-SEMANTICS Graz, Austria September 2 nd, 2010
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Outline Introduction – Motivation – Contributions Use Cases – IPad Scenario – Location, Sentiment, Recommendations, Competitors System – Demo – Architecture – Activity Flow – Annotation Pipeline Conclusion
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Tap into the Wisdom of the Crowd? “taking into account the collective opinion of a group of individuals rather than a single expert to answer a question” (Wikipedia) Has been done successfully – box-office revenue prediction for movies (CoRR’10) – earthquake detection (WWW’10) Can be useful in many scenarios
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Social Media Information Overload!
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Twitter 140 characters Users can “follow” updates of other users Hashtags – Category markers Short URLs
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Twarql Contributions Expressive description of an information need – Beyond keywords (uses SPARQL) Flexibility on the point of view – Ability to “slice and dice” data in several dimensions: thematic, spatial, temporal, sentiment, etc. Streaming data + background knowledge – Enables automatic evolution and serendipity Scalable real time delivery – Using sparqlPuSH (SFSW’10)
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Use Cases (IPad Scenario) 1.Location – Retrieve stream of locations where my product is being mentioned right now. 2.Consumer sentiment – Retrieve all people that have said negative things about my product. 3.Content suggestion – Retrieve all URLs that people recommend with relation to my product. 4.Related entities – What competitors are being mentioned with my product?
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Use Case 1: Location (query) Retrieve a stream of locations where my product is being mentioned right now. SELECT ? location WHERE { ?tweet moat:taggedWith dbpedia:IPad. ?presence opo:currentLocation ?location. ?presence opo:customMessage ?tweet. } SELECT ? location WHERE { ?tweet moat:taggedWith dbpedia:IPad. ?presence opo:currentLocation ?location. ?presence opo:customMessage ?tweet. }
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Use Case 1: Location SELECT ? location WHERE { ? tweet moat : taggedWith dbpedia : IPad. ? presence opo: currentLocation ? location. ? presence opo: customMessage ? tweet. } ?presence ?location ?tweet dbpedia: IPad moat:taggedWith opo:customMessage opo:currentLocation @anonymized Lorem ipsum bla bla this is an example tweet @anonymized Lorem ipsum bla bla this is an example tweet @anonymized Lorem ipsum bla bla this is an example tweet
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Use Case 2: Consumer Sentiment Retrieve all people that have said negative things about my product. SELECT ? user WHERE { ? tweet sioc:has_creator ? user. ? tweet moat:taggedWith dbpedia:IPad. ? tweet twarql:sentiment twarql:Negative. } SELECT ? user WHERE { ? tweet sioc:has_creator ? user. ? tweet moat:taggedWith dbpedia:IPad. ? tweet twarql:sentiment twarql:Negative. }
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Use Case 2: Consumer sentiment ?user :Negative ?tweet dbpedia: IPad moat:taggedWith sioc:has_creator twarql:sentiment @anonymized Lorem ipsum bla bla this is an example tweet Invite users for testing our new launch: @pablomendes @terraces @pavankaps @anotheruser
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Use Case 3: Content suggestion Retrieve all URLs that people recommend with relation to my product SELECT ?url WHERE { ? tweet moat:taggedWith dbpedia:IPad. ? tweet sioc:links_to ?url. } SELECT ?url WHERE { ? tweet moat:taggedWith dbpedia:IPad. ? tweet sioc:links_to ?url. }
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Use Case 3: Content Suggestion SELECT ? user WHERE { ? tweet sioc : has_creator ? user. ? tweet moat : taggedWith dbpedia : IPad. ? tweet twarql : sentiment twarql : Negative. } ?url ?tweet dbpedia: IPad moat:taggedWith sioc:links_to @anonymized Lorem ipsum bla bla this is an example tweet
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Use Case 4: Competitors ?competitor ?category ?tweet dbpedia: IPad moat:taggedWith skos:subject category:Wi-Fi category:Touchscreen skos:subject Background Knowledge (e.g. DBpedia) @anonymized Lorem ipsum bla bla this is an example tweet HPTabletPC IPhone
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Use Case 4: Competitors (contd.) Highlights – When a new competitor “appears” in the KB, no change is needed in the query => Automatic Evolution – We found interesting products that we didn’t initially consider as competitors of IPad (e.g. IPhone) => Serendipity
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Use Case 4: Competitors (query) What competitors of my product are being mentioned? SELECT ? competitor WHERE { dbpedia:IPad skos:subject ?category. ?competitor skos:subject ?category. ?tweet moat:taggedWith ?competitor. } SELECT ? competitor WHERE { dbpedia:IPad skos:subject ?category. ?competitor skos:subject ?category. ?tweet moat:taggedWith ?competitor. } ?tweet moat:taggedWith dbpedia:Ipad. - …are being mentioned with my product?
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Demonstration Cuebee – query formulation Twarql – information extraction – stream querying sparqlPuSH – real time delivery Demo link: http://bit.ly/twarql
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Architecture Mendes, Passant, Kapanipathi, Sheth. Linked Open Social Signals, Web Intelligence 2010
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Twarql Streaming Activity Diagram Web Client Web Client APP SERVER APP SERVER DIST. HUB (SEMANTIC) PUBLISHER (SEMANTIC) PUBLISHER SOCIAL SENSOR SOCIAL SENSOR Twitter API /register query, #id REGISTER (query, new hubURL()) LISTEN(tweet) ANNOTATE(tweet) STREAM(tweet) keywords FILTER(tweet, for all query) STORE(tweet) PUBLISH(tweet) SETUP REQUEST(#id)PULL(hubURL, req) hubURL UPDATE INTERFACE PUSH(tweet, subscriber) UPDATE(hubURL, rssTweet) POLL QUERY RELAY QUERY(#id, query) /feed feed update STREAM(query, #id) FORMULATE QUERY cache UPDATE(tweet) CACHE(tweet) /publish RDF store /subscribe /sparql topic idHub URL #id1http://hub1 #id2http://hub2 /sparql #id
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Annotation URL extraction – Regex based, short URL resolution via http redirects Hashtag extraction – Regex based, “resolution” via TagDef and Tagal.us Entity mention extraction – “Spotting” via string matching (prefix tree) based on a dictionary (Dbpedia) – Disambiguation on the way! (est. October) Conversion to RDF triples – using SIOC, FOAF, MOAT, etc.
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Conclusion Flexibility and expressiveness in managing real time streams of information! Triples generated for the IPad scenario – From June 3 rd to June 8 th – 511,147 tweets – 4,479,631 triples … and counting! You can generate triples too: http://twarql.sf.nethttp://twarql.sf.net 53,237 positive; 6,739 negative; 451,171 neutral
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Thank you Connect with us: @pablomendes @terraces @pavankaps Collaborate: – http://twarql.sf.net http://twarql.sf.net – http://wiki.knoesis.org/index.php/Twarql http://wiki.knoesis.org/index.php/Twarql
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