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Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
Twarql (Presentation at I-SEMANTICS 2010)
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Twarql (Presentation at I-SEMANTICS 2010)

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This was the presentation I gave at I-SEMANTICS 2010 for our first-prize winner at the Triplification Challenge 2010. …

This was the presentation I gave at I-SEMANTICS 2010 for our first-prize winner at the Triplification Challenge 2010.

It describes scenarios that motivated our development of Twarql. The paper can be found at:
http://blog.semantic-web.at/wp-content/uploads/2010/09/a45_mendes.pdf

Citation:
Pablo N. Mendes, Alexandre Passant, and Pavan Kapanipathi. 2010. Twarql: tapping into the wisdom of the crowd. In Proceedings of the 6th International Conference on Semantic Systems (I-SEMANTICS '10), Adrian Paschke, Nicola Henze, and Tassilo Pellegrini (Eds.). ACM, New York, NY, USA, , Article 45 , 3 pages. DOI=10.1145/1839707.1839762 http://doi.acm.org/10.1145/1839707.1839762

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  • Sentiment: 53,237 positive; 6,739 negative; 451,171 neutral
  • Sentiment: 53,237 positive; 6,739 negative; 451,171 neutral
  • This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  • This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  • This use case requires merging streaming data with background knowledge information (e.g. from DBpedia). Examples of ?category include category:Wi-Fi devices and category:Touchscreen portable media players amongst others. As a result, without having to elicit all products of interest as keywords to lter a stream, a user is able to leverage relationships in background knowledge to more effectively narrow down the stream of tweets to a subset of interest.
  • Use case: get me all the tweets related to sports from all the friends of a user.The Formulate query will make a query for this assigning an ID for this, and will configure the social sensor for the query, id and hubURL.The hubURL will be given back to the client so the client knows where to collect information in future.
  • Sentiment: 53,237 positive; 6,739 negative; 451,171 neutral
  • Transcript

    • 1. TwarqlTapping Into the Wisdom of the Crowd
      Pablo N. Mendes, PavanKapanipathi, Alexandre Passant
      I-SEMANTICS
      Graz, Austria
      September 2nd, 2010
    • 2. Outline
      Introduction
      Motivation
      Contributions
      Use Cases
      IPad Scenario
      Location, Sentiment, Recommendations, Competitors
      System
      Demo
      Architecture
      Activity Flow
      Annotation Pipeline
      Conclusion
    • 3. 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
    • 4. Social Media: Motivation
    • 5. Social Media: Motivation (contd.)
      Information Overload!
    • 6. 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)
    • 7. Use Cases (IPad Scenario)
      Location
      Retrieve stream of locations where my product is being mentioned right now.
      Consumer sentiment
      Retrieve all people that have said negative things about my product.
      Content suggestion
      Retrieve all URLs that people recommend with relation to my product.
      Related entities
      What competitors are being mentioned with my product?
    • 8. Use Case 1: Location (query)
      Retrieve a stream of locations where my product is being mentioned right now.
      SELECT ? location
      WHERE {
      ?tweet moat:taggedWithdbpedia:IPad .
      ?presence opo:currentLocation ?location .
      ?presence opo:customMessage ?tweet .
      }
    • 9. Use Case 1: Location
      Incoming microposts…
      @anonymized
      @anonymized
      @anonymized
      Loremipsumblabla this is an example tweet
      Loremipsumblabla this is an example tweet
      Loremipsumblabla this is an example tweet
      opo:currentLocation
      ?presence
      ?location
      SELECT ? location
      WHERE {
      ? tweet moat : taggedWithdbpedia : IPad .
      ? presence opo: currentLocation ?
      location .
      ? presence opo: customMessage ? tweet .
      }
      opo:customMessage
      moat:taggedWith
      dbpedia:IPad
      ?tweet
    • 10. Use Case 1: Location
      @anonymized
      @anonymized
      @anonymized
      Loremipsumblabla this is an example tweet
      Loremipsumblabla this is an example tweet
      Loremipsumblabla this is an example tweet
      Update view if micropost matches contraints
      opo:currentLocation
      ?presence
      ?location
      SELECT ? location
      WHERE {
      ? tweet moat : taggedWithdbpedia : IPad .
      ? presence opo: currentLocation ?
      location .
      ? presence opo: customMessage ? tweet .
      }
      opo:customMessage
      moat:taggedWith
      dbpedia:IPad
      ?tweet
    • 11. 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:taggedWithdbpedia:IPad .
      ? tweet twarql:sentimenttwarql:Negative .
      }
    • 12. Use Case 2: Consumer sentiment
      Incoming microposts…
      @anonymized
      Loremipsumblabla this is an example tweet
      twarql:sentiment
      ?user
      :Negative
      sioc:has_creator
      moat:taggedWith
      dbpedia:IPad
      ?tweet
    • 13. Use Case 2: Consumer sentiment
      Invite users for testing our new launch:
      @pablomendes
      @terraces
      @anonymized
      Loremipsumblabla this is an example tweet
      @pavankaps
      Trigger action if micropost matches contraints
      @anotheruser
      twarql:sentiment
      ?user
      :Negative
      sioc:has_creator
      Update view
      moat:taggedWith
      dbpedia:IPad
      ?tweet
    • 14. Use Case 3: Content suggestion
      Retrieve all URLs that people recommend with relation to my product
      SELECT ?url
      WHERE {
      ? tweet moat:taggedWithdbpedia:IPad .
      ? tweet sioc:links_to ?url .
      }
      Note: Twarql extracts links and resolves shortened URIs before annotating the tweet
    • 15. Use Case 3: Content Suggestion
      Incoming microposts…
      @anonymized
      Loremipsumblabla this is an example tweet
      ?url
      sioc:links_to
      moat:taggedWith
      dbpedia:IPad
      ?tweet
    • 16. Use Case 3: Content Suggestion
      My IPad Journal
      @anonymized
      Loremipsumblabla this is an example tweet
      If micropost matches contraints,
      accumulate information and update view
      ?url
      sioc:links_to
      moat:taggedWith
      dbpedia:IPad
      ?tweet
    • 17. Use Case 4: Competitors (query)
      What competitors of my product are being mentioned?
      SELECT ? competitor
      WHERE {
      dbpedia:IPadskos:subject ?category .
      ?competitor skos:subject ?category .
      ?tweet moat:taggedWith ?competitor .
      }
    • 18. Use Case 4: Competitors (query)
      What competitors of my product are being mentioned with my product?
      - comparative opinion!
      SELECT ? competitor
      WHERE {
      dbpedia:IPadskos:subject ?category .
      ?competitor skos:subject ?category .
      ?tweet moat:taggedWith ?competitor .
      }
      ?tweet moat:taggedWithdbpedia:Ipad .
    • 19. Use Case 4: Competitors
      Incoming microposts…
      Background Knowledge (e.g. DBpedia)
      @anonymized
      Loremipsumblabla this is an example tweet
      dbpedia:IPad
      skos:subject
      ?category
      ?category
      ?competitor
      skos:subject
      skos:subject
      moat:taggedWith
      ?tweet
    • 20. Use Case 4: Competitors
      Incoming microposts…
      Background Knowledge (e.g. DBpedia)
      @anonymized
      Loremipsumblabla this is an example tweet
      category:Wi-Fi
      dbpedia:IPad
      category:Touchscreen
      skos:subject
      ?category
      ?category
      ?competitor
      skos:subject
      skos:subject
      moat:taggedWith
      Background knowledge is dynamically “brought into” microposts.
      ?tweet
    • 21. Use Case 4: Competitors
      Background Knowledge (e.g. DBpedia)
      @anonymized
      Loremipsumblabla this is an example tweet
      category:Wi-Fi
      dbpedia:IPad
      category:Touchscreen
      skos:subject
      ?category
      ?category
      ?competitor
      skos:subject
      skos:subject
      moat:taggedWith
      ?tweet
      Trigger action if micropost matches constraints.
    • 22. 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
    • 23. Demonstration
      Cuebee
      query formulation
      Twarql
      information extraction
      stream querying
      sparqlPuSH
      real time delivery
      Demo link: http://bit.ly/twarql
    • 24. Architecture
      Mendes, Passant, Kapanipathi, Sheth. Linked Open Social Signals, Web Intelligence 2010
    • 25. Twarql Streaming Activity Diagram
      DIST. HUB
      Web Client
      APP SERVER
      (SEMANTIC)
      PUBLISHER
      SOCIAL SENSOR
      Twitter API
      SETUP
      keywords
      FORMULATE QUERY
      LISTEN(tweet)
      STREAM(tweet)
      query, #id
      /register
      /subscribe
      STREAM(query, #id)
      ANNOTATE(tweet)
      REGISTER(query,
      new hubURL())
      PUBLISH(tweet)
      /publish
      hubURL
      FILTER(tweet, for all query)
      #id
      REQUEST(#id)
      PULL(hubURL, req)
      /feed
      STORE(tweet)
      feed update
      UPDATE INTERFACE
      RDF store
      UPDATE(hubURL,rssTweet)
      UPDATE(tweet)
      #id
      POLL
      cache
      CACHE(tweet)
      QUERY(#id, query)
      PUSH(tweet, subscriber)
      RELAY
      QUERY
      /sparql
      /sparql
      http://www.slideshare.net/pablomendes/streaming-annotatedtweets
    • 26. 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! (via DBpedia Spotlight)
      Conversion to RDF triples
      using SIOC, FOAF, MOAT, etc.
    • 27. Conclusion
      Flexibility and expressiveness in managing real time streams of information!
      Triples generated for the IPad scenario
      From June 3rd to June 8th
      511,147 tweets
      4,479,631 triples
      … and counting!
      You can generate triples too: http://twarql.sf.net
      53,237 positive;
      6,739 negative;
      451,171 neutral
    • 28. Thank you
      Connect with us:
      @pablomendes
      @terraces
      @pavankaps
      Collaborate:
      http://twarql.sf.net
      http://wiki.knoesis.org/index.php/Twarql

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