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An Exploratory Study on Using Social Information Networks for Flexible Literature Access
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An Exploratory Study on Using Social Information Networks for Flexible Literature Access

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It is well known that the fundamental intellectual problems of information access are the production and consumption of information. In this paper, we investigate the use of social network of ...

It is well known that the fundamental intellectual problems of information access are the production and consumption of information. In this paper, we investigate the use of social network of information producers (authors) within relations in data (co-authorship and citation) in order to improve the relevance of information access. Relevance is derived from the network by levraging the usual topical similarity between the query and the document with the target author’s authority. We explore various social network based measures for computing social information importance and show how this kind of contextual information can be incorporated within an information access model. We experiment with a collection issued from SIGIR proceedings and show that combining topical, author and citation based evidences can significantly improve retrieval access precision, measured in terms of mean reciprocal rank.

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An Exploratory Study on Using Social Information Networks for Flexible Literature Access An Exploratory Study on Using Social Information Networks for Flexible Literature Access Presentation Transcript

  • An exploratory study on using social informationnetworks for flexible literature access
    Lynda Tamine, Amjed Ben Jabeur and WahibaBahsoun
    University Paul Sabatier Toulouse III, France
    IRIT SIG-RI
    {lechani, jabeur,wbahsoun}@irit.fr
    FQAS 2009
  • An exploratory study on using social information networks for flexible literature access
    Outline:
    Social Information Retrieval : Background and motivation
    A social based model for literature access
    Experimental evaluation
    Conclusion and outlook
    2
  • Towards Social Information Retrieval
    IRS
    Query
    User
    Profile
    Tag
    Comment
    3
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Towards Social Information Retrieval
    Information Producer
    Documents
    Social Information Retrieval
    Queries
    Information consumer
    Tags, comments ..etc
    4
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • About Social Information Retrieval
    Incorporating information from social network in the information retrieval process
    Access to relevant information in the social neighborhood
    Spread information through the social network
    Take account of the social activity
    Crossing two domains [KIRSCHet al , 2003]
    Information Retrieval
    Represent and compare document /query
    Social Network Analysis [WESSERMANN & FAUST]
    Represent social entities and relationships
    Estimate individual’s centrality
    5
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • About Social Information Retrieval
    Social relevance features [AMER & al, 07]
    Topical relevance
    Social distance
    Incoming links and bookmarks
    Timeliness and freshness
    Social importance of individuals
    Relevant documents are published by important people
    6
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • About Social Information Retrieval
    Domain application of Social Information Retrieval
    Knowledge and experience sharing , Collaborative production [KORFAITIS & al, 2006]
    Wiki, SourgeForge
    Opinion retrieval [ZANG & YE, 2008]
    Blogs
    Social media and bookmarking [HEYMANN & al, 2008] [BUDURA & al, 2008]
    Facebook, YouTube, Del.ici.us
    Information finding and social network exploring [ZANG & al , 2008]
    Social ranking of document, Expert searching
    Literature access [KIRSCH & al , 2006]
    CiteULike.org
    7
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Literature Access
    Indexing scientific publication
    Access to content and metadata
    Integrate heterogeneous bibliographic resources
    Explore citation
    Ranking publication
    Content feature
    Citation feature [EDGAR & RIJIKE, 07]
    • Citation analysis in information retrieval
    Extracting terms form citing documents
    Considering citation as hyperlinks
    8
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Related works
    Extract social network form bibliographic resources [KIRSCH, 2003] [MUTSCHKE, 2001] [KIRCHNOFF & al, 2008]
    Actors : documents and authors
    Edges : co-author relationships
    Multiplicative relevance score [KIRSCH, 2003]
    Query document similarity
    Author’s authority
    9
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Contribution
    Relationships extracted from bibliographic resources :
    Co-authorship
    Citation link
    Linear combination
    Topical relevance
    Social importance of documents
    Study the impact of centrality measures
    10
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Social Content Graph
    a1
    a3
    a2
    a4
    a6
    a1
    a3
    a4
    a2
    a5
    Co-author
    d1
    d2
    d3
    d4
    Citation
    Social network graph
    V: Authors E: Relationships between authors
    An edge express:
    Co-auteur relationship (implicit direct)
    Citation relationship (implicit indirect)
    11
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Estimate document relevance
    Step 1. Social Importance of authors
    a1
    a3
    a4
    a2
    Ranked List
    Social
    Importance
    Algorithms
    Of authors
    Degree, Closeness, Betweeness
    PageRank and Hits
    {author, score}
    Social Network
    Social Network Analysis
    Social Scores
    12
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Social Importance measures [KIRCHNOFF & al, 08] [LANGVILLE & MEYR, 08]
    Rank nodes according to their position in the network
    Estimate document relevance
    Degree
    • Social activity
    • Popularity
    • Gregariousness
    Closeness
    • Reachability|independence
    • Influence
    Betweenss
    • Interdisciplinary
    PageRank
    • Authority
    Hits
    • Hub and authority
    13
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Step 2. From author score to document social importance
    Estimate document relevance
    C(v2)
    C(v1)
    • Sum of the authors social scores
    • Implicitly taking account of the number of co-authors
    • Other proposition:
    • Weighted Sum, Min, Max, AVG …etc
    Imp(d)
    C(v4)
    C(v3)
    14
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Estimate document relevance
    Step 3. Retrieving documents
    Step 4. Combining topical relevance and social importance
    Result set
    IRS
    Query
    {d, RSV (q,d)}
    Topical relevance
    Social importance
    15
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • ACM SIGIR 1978-2008
    Metadata gathered from ACM Portal
    Author
    Citations links
    Downloads ( Mars 2008 – Mars 2009)
    Experimentation
    Degree distribution
    Verticeswithdegreeδ(ν)
    Vertex degreeδ(ν)
    16
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Experimentation
    Choosing queries
    Experimentation measure
    SIGIR
    Collection
    50 Queries
    Top 50 Docs
    Most cited assumption
    • -------------
    • -------------
    • -------------
    • -------------
    • -------------
    • -------------
    • -------------
    • -------------
    • -------------
    • -------------
    1
    {titleterms}
    Most downloaded assumption
    2
    Known item retrieval
    { d,q }
    50 x{ d, q, rank }
    50 x { document, query}
    17
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Comparing importance measures
    Most cited assumption
    Most downloaded assumption
    Co-author
    Citation
    Co-author & Citation
    18
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Parameter Tuning
    Most cited assumption
    PageRank
    PageRank
    HITS
    Closeness
    Most downloaded assumption
    Co-author & Citation
    Co-author
    Co-author
    Citation
    Co-author & Citation
    Citation
    19
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Comparative evaluation
    Co-author
    27%
    0.212 0.270
    26% 59%
    Most cited
    27% 61%
    Most cited
    Citation
    59%
    59%
    Co-author & Citation
    61%
    61%
    20
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Conclusion
    A social network based information access model
    Co-author and citation relationships
    Linear combination of scores
    Study effectiveness of the model using several social importance measure
    Ensure the soundness of our results using two social relevance assumptions
    21
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Outlook
    Extend the social content graph
    Additional relationships
    Weighting social relationships
    Include tag entity
    Test our retrieval model on a large web collection
    Study recall and precision of the proposed approach
    22
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • Thanks for your attention !
  • References
    [KIRSH & al, 06] Sebastian Kirsch, Melanie Gnasa, Armin Cremers,
    “Beyond the Web : Retrieval in Social Information Spaces” ,
    Proceedings of the 28th European Conference on Information Retrieval, ECIR 2006, Imperial College, London, 2006
    [AMER & al, 07] SihemAmer-Yahia, Michael Benedikt,Philip Bohannon,
    “Challenges in Searching Online Communities” ,
    IEEE Data Eng. Bull., 2007
    [KIRCHNOFF & al, 08] Lars Kirchhoff, Katarina Stanoevska-Slabeva, Thomas Nicolai and Matthes Fleck ,
    “Using social network analysis to enhance information retrieval systems , Applications of Social Network Analysis”,
    ASNA, Zurich, 2008
    [LANGVILLE & MEYR, 08] Lars Kirchhoff, Katarina Stanoevska-Slabeva,
    “Using social network analysis to enhance information retrieval systems , Applications of Social Network Analysis”,
    ASNA, Zurich, 2008
    24
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • References
    [MUTSCHKE, 01] Peter Mutschke,
    “Enhancing Information Retrieval in Federated Bibliographic Data Sources Using Author Network Based Stra-tagems”,
    Reserach and Advanced Technology for Digital Libraries : 5th European Conference, ECDL 2001, Darmstadt, Germany, September 4-9, 2001
    [EDGAR & RIJIKE, 07] Edgar Meij and Maarten de Rijke,
    “Using Prior In-formation Derived from Citations in Literature Search”
    Proceedings of RIAO 2007 : Recherched’InformationAssistée par Ordinateur 2007, 2007
    [KORFIATIS & al., 2006] Korfiatis, N.; Poulos, M. & Bokos, G.
    “EvaluatingAuthoritative Sources using Social Networks: An Insight fromWikipedia”,
    Online Information Review, 2006, 30, 252-262
    [HEYMANN & al, 2008] Paul Heymann, Daniel Ramage, Hector G. Molina
    "Social tag prediction Export"
    In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008), pp. 531-538.
    25
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • References
    [BUDURA et al.] Adriana Budura, Sebastian Michel, Philippe C. Mauroux, Karl Aberer
    “ To tag or not to tag -: harvesting adjacent metadata in large-scaletaggingsystemsExpor ”,  
    In SIGIR '08: Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval (2008), pp. 733-734.
    [ZHANG et al.] Jing Zhang, Jie Tang and Juanzi Li
    “ Expert Finding in a Social Network ”,
    Advances in Databases: Concepts, Systems and Applications, Volume 4443/2008, 1066-1069, Springer Berlin / Heidelberg, 2008
    [ZANG & YE, 2008 ] Min Zhang and XingyoaYe
    “ A generation model to unifytopic relevance and lexicon-based sentiment for opinion retrieval ”,
    In Proceedings of the 31st Annual international ACM SIGIR Conference on Research and Development in information Retrieval (Singapore, Singapore, July 20 - 24, 2008). SIGIR '08. ACM, New York, NY, 411-418
    26
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook
  • References
    [WASSERMAN & KHATERINE] Stanley Wasserman and Katherine Faust
    “ Social network analysis: methods and applications”,
    Cambridge Uni. Press
    [KIRSCH, 2003] Sebastian Marius Kirsch,
    “ Social information retrieval ”,
    PhDThesis in Computer Science, Computer science department III, Bonn, 14 March 2003
    [KIRSCH & al. , 2006] Sebastian Kirsch, MelanieGnasa, and Armin Cremers
    “ Beyondthe web: Retrieval in social information spaces”,
    EuropeanConference on IR Research No28, London , ROYAUME-UNI (2006) 2006
    [RITCHIE & TEUFEL, 2007] Anna Ritchie, Simone Teufel and Stephen Robertson,
    “ Usingtermsfrom citations for IR: some first results”,
    Proceedings of the EuropeanConference for Information Retrieval ECIR, pp 211-221, 2007
    [SMALL, 1973] Henry Small
    “ Co-citation in the scientificliterature: A new measurement of the relationshipbetweentwodocuments”,
    Journal of the American Society of Information
    27
    Background and motivation | Social based model for literature access | Experimental evaluation | Conclusion and outlook