Twarql Tapping Into the Wisdom of the Crowd

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 Information Overload!

5. 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)

6. 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?

7. 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 . }

8. 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 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

9. Use Case 2: Consumer sentiment Invite users for testing our new launch: @pablomendes @terraces @anonymized Loremipsumblabla this is an example tweet @pavankaps @anotheruser twarql:sentiment ?user :Negative sioc:has_creator moat:taggedWith dbpedia:IPad ?tweet

10. Use Case 4: Competitors Background Knowledge (e.g. DBpedia) IPhone HPTabletPC category:Wi-Fi @anonymized Loremipsumblabla this is an example tweet category:Touchscreen ?category skos:subject skos:subject ?competitor skos:subject moat:taggedWith dbpedia:IPad ?tweet

11. 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

12. Demonstration • Cuebee • query formulation • Twarql • information extraction • stream querying • sparqlPuSH • real time delivery Demo link: http://bit.ly/twarql

13. Architecture Mendes, Passant, Kapanipathi, Sheth. Linked Open Social Signals, Web Intelligence 2010

14. 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

15. Thank you • Connect with us: @pablomendes @terraces @pavankaps • Collaborate: • http://twarql.sf.net • http://wiki.knoesis.org/index.php/Twarql