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BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
BOTTARI: Location based Social Media Analysis with Semantic Web
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BOTTARI: Location based Social Media Analysis with Semantic Web

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Bottari is a LarKC application http://www.larkc.eu/. It offers a real-time personalized recommendation service for restaurants in Insa-dong(Seoul) listening to the reputation of the restaurants on …

Bottari is a LarKC application http://www.larkc.eu/. It offers a real-time personalized recommendation service for restaurants in Insa-dong(Seoul) listening to the reputation of the restaurants on social media. Social media anlytics is powered by LarKC inductive and deductive stream reasoning solution. Learn more at http://larkc.cefriel.it/lbsma/bottari/ .

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    • 1. BOTTARI: Location based Social Media Analysis with Semantic Web Emanuele Della Valle Joint work with: CEFRIEL : Irene Celino, Daniele Dell ’ Aglio, Marco Balduini SALTLUX : Tony Lee, Seonho Kim S IEMENS : Volker Tresp, Yi Huang
    • 2. Watch this first :-) 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany http://www.youtube.com/watch?v=c1FmZUz5BOo
    • 3.
      • An augmented reality application for personalized recommendation of restaurants in Seoul
      What have you seen? 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany
    • 4.
      • Yes and no!
      • Same use case, more “democratic”
      • We do “reality mining” by listening to the social media
      Yet another ? 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany
    • 5. Architecture 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany out Query Rewriter Query Evaluator RDF2Matrix Plug-in Streaming Linked Data Server SOR Invoker SOR geo-spatial KB Social Media Crawler and Sentiment Miner HTTP PULL: Query Initiated PUSH: Data Initiated SPARQL androjena
    • 6. Sentiment Mining
      • Precision tests:
        • Auto-generated rules ≈ 70%
        • Manually-coded rules ≈ 90%
        • Syllable kernel ≈ 50~60%
      • Our target > 85%
      26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany Micropost message Morphologically Analyzable? Rule based Analysis Auto generated rules Learned documents SVMs Syllable Kernel Sentiment of the tweet Yes No
    • 7. SOR - Geo-Spatial KB 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany
    • 8. C-SPARQL and Streaming Linked Data Server 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany
    • 9.
      • A machine learning framework for inductive materialization
        • Detects interesting data patterns
        • Predics RDF-triples
          • i.e., which restaurant a user will tweet positively about
      • Caractheristics
        • Capability to deal with sparse, high-dimensional and incomplete data
        • Multivariate latent space based approach
        • Modularized approach for easily integrating contextual information
      SUNS (Statistical Unit Node Sets) 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany
    • 10.
      • SELECT DISTINCT ?poi ?name ?lat ?long ?numPos ?prob
      • WHERE {
      • ?poi a ns:NamedPlace ;
      • ns:name ?name ;
      • geo:lat ?lat ;
      • geo:long ?long .
      • FILTER (f:within_distance( 37.5 , 126.9 , ?lat, ?long, 200 ))
      • FILTER (f:dest_point_viewing( 37.5 , 126.9 , ?lat, ?long, 90 , 200 ))
      • { :someUser sioc:creator_of ?tweet .
      • ?tweet twd:talksAboutPositively ?poi .
      • WITH PROBABILITY ?prob
      • ENSURE PROBABILITY [0.5..1) }
      • ?poi twd:numberOfPositiveTweets ?numPos .
      • }
      • ORDER BY DESC(?numPos), ?prob, f:distance( 37.5 , 126.9 , ?lat, ?long)
      • LIMIT 10
      Query Processing 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany GEO-SPATIAL PROBABILISTIC STREAMING
    • 11. LarKC At Work 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany PULL: Query Initiated PUSH: Data Initiated SPARQL androjena Probabilistic part of the query to get personalized recommendations (the “ for me ” button in BOTTARI) Geo-Spatial part of the query to get POIs closer to user location Streaming part of the query to get trends in users' sentiment (the “ emerging ” button in BOTTARI) Input user query is split Results of the different computations are joined out Query Rewriter Query Evaluator RDF2Matrix Plug-in Streaming Linked Data Server SOR Invoker SOR geo-spatial KB Social Media Crawler and Sentiment Miner HTTP
    • 12. Evaluation - Efficacy 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany 5 10 15 20 25 30 0,7 random knnItem emerging (C-SPARQL) for me (SUNS) SUNS + C-SPARQL 0,6 0,5 0,4 0,3 0,2 0,1
    • 13. Evaluation - Efficiency 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany Hardware: 2.66 GHz Intel Core 2 Duo with 8 GB RAM
    • 14. Evaluation – Scalability 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany Number of concurrent users Query Latency (sec)
    • 15.
      • End-user application
      • Attractive and functional interface
      • Real-world dynamic data
      • Fully based on Semantic Web technologogies
        • RDF as common data format between heterogenous components
        • SPARQL as query language
      • Rigorously evaluated
        • Effective
        • High throughput for handling dynamic data
        • Scalable in number of concurrent users
      • Commercial Potential
      Conclusions 26.10.2011 - SW Challenge 2011, ISWC 2011, Bonn, Germany
    • 16. Emanuele Della Valle Joint work with: CEFRIEL : Irene Celino, Daniele Dell ’ Aglio, Marco Balduini SALTLUX : Tony Lee, Seonho Kim S IEMENS : Volker Tresp, Yi Huang Any question?

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