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Knowledge Acquisition from Social Awareness Streams
 

Knowledge Acquisition from Social Awareness Streams

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Slides from PhD symposiumat ESWC2010 (http://www.eswc2010.org/program-menu/phd-symposium)

Slides from PhD symposiumat ESWC2010 (http://www.eswc2010.org/program-menu/phd-symposium)

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    Knowledge Acquisition from Social Awareness Streams Knowledge Acquisition from Social Awareness Streams Presentation Transcript

    •  
    • Social Awareness Streams (SAS)
      • Short, natural-language messages created by users
      • Broadcasted
      • Information consumption is driven by social networks
      • Applications such as Twitter or Facebook
      • [Naaman, 2010]
      31 May 2010
    • 31 May 2010 What‘s the knowledge of SAS? Users Web Resources Real-World Happenings Natural Language Constructs http://www.flickr.com/photos/matthewfield/2306001896/ What‘s the knowledge of SAS? Users Web Resources Natural Language Constructs Real-World Happenings http://www.flickr.com/photos/waldoj/722508166/
    • Proposed Approach
      • Aim
        • What kind of knowledge is contained in SAS
        • How we can acquire knowledge from SAS
        • Which factors influence knowledge acquisition results
      • Method
        • Develop a SAS Analyzer system
        • Controlled experiments
      31 May 2010
    • Research Questions
      • Do ontological structures emerge from SAS?
      • Which factors influence their emergence?
        • Stream aggregation/sampling strategies?
        • Semantic enrichment strategies?
        • Knowledge acquisition methods and algorithms?
      31 May 2010
    • SAS Analyzer 31 May 2010
    • Controlled Experiments (1) 31 May 2010 Do ontological structures emerge from SAS? ground truth ontology randomly sample Input labels Compare (e.g., via Precision, Recall, RLA [Maedche et al, 2000] )
    • Controlled Experiments (2) 31 May 2010 Which factors influence emerging semantics? vary variables Compare (e.g., via Precision, Recall, RLA [Maedche et al, 2000] ) ground truth ontology randomly sample Input labels
    • Preliminary Results
      • Network-theoretic Model of SAS
      • Structural Stream Measures
      • First Experiment on acquiring latent conceptual structures from SAS
      31 May 2010
    • A network-theoretic model of SAS
      • A Social Awareness Stream is a tupel
      • U, M and R are finite sets whose elements are called users, messages and resources
      • q1, q2, q3 are qualifiers
      • Y is a ternary relation
      • ft is a function
      • fl is a function
      31 May 2010
    • Example 31 May 2010
    • Structural Stream Measures (1) 31 May 2010
    • Structural Stream Measures (2)
      • Social Diversity
        • How many different users participate in a stream?
        • Social variety:
        • How balanced are their participations?
        • Social balance:
      31 May 2010
    • Experiment
      • Aim
        • Can we observe emerging semantics from SAS?
      • Method
        • Input: topic of interest, in our case „semanticweb“
        • 4 different stream aggregations
        • 3-mode networks (users, resources and messages)
        • Network transformations (projections) to obtain lower-order networks of resources
        • Output: weighted resource networks
        • Manual evaluation
      31 May 2010
    • Dataset
      • 4 different stream aggregations from Twitter
      • Same topic
        • Hashtag stream: #semanticweb
        • Keyword stream: semanticweb and semweb
        • User list stream: semweb user list from twitter user sclopit
        • User directory stream: wefollow semanticweb directory
      • Same time interval
        • 2 time intervals: 16th of Dec 2009 - 20th of Dec 2009 and 29th of Dec 2009 - 1st of Jan 2010
      31 May 2010
    • Network Transformations 31 May 2010 co-occurence context [ Harris , 1954] [Mika, 2007] communities
    • First Results (1)
      • Type of stream aggregations influence emerging semantics
        • Hashtag stream aggregations are more robust against external disturbances than user list streams
      31 May 2010 Hashtag Stream O R (RU a )S(R h ) User List Stream O R (RU a )S(R UL )
    • First Results (2)
      • Type of network transformation influence emerging semantics
        • Hashtags seem to be good context indicators
        • Resource-hashtag networks reveal good latent conceptual structures
      31 May 2010
    • Limitations
      • Small Dataset
      • Only one topic/domain
      • Manual Evaluation
      31 May 2010
    • References
      • Z. Harris. Distributional structure. The Structure of Language: Readings in the philosophy of language,10:146-162, 1954.
      • A. Maedche and S. Staab. Discovering Conceptual Relations from Text. In: W.Horn (ed.): ECAI 2000. In Proceedings of the 14th European Conference on Artificial Intelligence, Berlin, Amsterdam, 2000.
      • P. Mika. Ontologies are us: A unified model of social networks and semantics. Web Semantics, 5(1):5-15, 2007.
      • M. Naaman, J. Boase, and C.-H. Lai. Is it all about me? user content in social awareness streams. In Proceedings of the ACM 2010 conference on Computer supported cooperative work, 2010.
      31 May 2010
    • Thank you! 31 May 2010 http://clauwa.info/me [email_address] http://twitter.com/clauwa