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About the Social Semantic Web
 

About the Social Semantic Web

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Talk given at the Semantic Web SIKS course 2011: why we need semantics on the Social Web. Three examples: social tagging, user profiling based on Twitter streams and cross-system user profiling ...

Talk given at the Semantic Web SIKS course 2011: why we need semantics on the Social Web. Three examples: social tagging, user profiling based on Twitter streams and cross-system user profiling (linking user profiles).

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  • Characteristics:Overlap is small; still one gets significantly more informationPerformance: cross-system UM leads to very high improvements for cold-start recommendations (some personal information is better than nothing) to optimize: we need to know the characteristics of the system (we can be stupid and simply aggregate what we can get  this is fine as we will get improvements anyhow; but we can massively optimize if we carefully select the different building blocks of the cross-system UM strategy with respect to the given application (e.g. Recommending bookmarks in Delicious  select tags from personal Twitter profile, but weigh them according to the global Delicious tag frequencies.
  • 1. Translate between “information need” and Twitter vocabulary and 2. compose answer out of several tweets

About the Social Semantic Web About the Social Semantic Web Presentation Transcript

  • Social Semantic WebWhy we need semantics on the Social Web
    Somewhere, Netherlands, September 27, 2011
    Fabian Abel
    Web Information Systems, TU Delft
  • The Social Web
    Social Web stands for the culture of participation on the Web.
  • Power-law of participation by Ross Mayfield 2006
  • The Social Web
    “Problem”
    The Social Web is made by people for people
  • Why do we need semantics on the Social Web? (from an engineering point of view)
    Applications
    …that understand and leverage Social Web data
    Semantic Enrichment, Linkage and Alignment
    user/usage data
    Social Web
  • About this talk
    Applications
    …that understand and leverage Social Web data
    User Modeling and Personalization
    Mapping words to
    ontological concepts
    Semantic Enrichment, Linkage and Alignment
    Social tagging
    Micro-blogging
    user/usage data
    Social Web
  • Social Tagging
    Semantics in Social Tagging Systems
  • Social Tagging Systems
    jazzmusic
    armstrong
    trumpet
    baker, trumpet
    Users
    trumpet
    Tags
    armstrong, baker, dizzy,
    jazzmusic, jazz, trumpet
    dizzy, jazz
    armstrong
    Resources
    tag
    user
    resource
    Folksonomy:
    • set of tag assignments
    • Formal model [Hotho et al. ‘07]:
    F = (U, T, R, Y)
    tag assignment
  • Folksonomy Graph
    A folksonomy (tag assignments) can be represented via an undirected weighted tripartite graph GF = (VF, EF) where:
    VF = U U T U R is the set of nodes
    EF = {(u,t), (t,r), (u,r) | (u,t,r) in Y} is the set of edges
  • How to weigh the edges of a folksonomy graph?
    w(t1, r1) = ?
    w(t1, r1) = 2
    tag assignments: (u1, t1, r1), (u2, t1, r1), (u2, t2, r2)
    w(u1, t1) = ?
    w(u1, t1) = 1
    u1
    w(u2,r2) = ?
    w(u2,r2) = 1
    t1
    r1
    w(t1, r1)
    w(u,t) = ?
    w(u,r) = ?
    w(t,r) = ?
    w(u1, t1)
    t2
    r2
    u2
    w(u2, r2)
    For example:
    w(t,r) = {u in U| (u, t, r) in Y} = count the number of users who assigned tag t to resource r
  • Search & Ranking in Folksonomies
    FolkRank[Hotho et al. 2006] is an application of PageRank[Page et al. 98] for folksonomies:
    FolkRank-based rankings:
    users tags resources
    1.
    2.
    preference
    vector
    FolkRank vector
    adjacency matrix models the folksonomy graph
    influence of preferences
    t1
    u2
    r1
    r2
    t2
    u1
    u1 u2 t1 t2 r1 r2
    u1 0.5 0.5
    u2 0.25 0.25 0.25 0.25
    t1 0.25 0.25 0.5
    t2 0.5 0.5
    r1 0.25 0.25 0.5
    r2 0.5 0.5
    u1
    u2
    t1
    t2
    r1
    r2
    u1
    u2
    t1
    t2
    r1
    r2
    0
    0
    1
    0
    0
    0
    0.1
    0.2
    0.3
    0.1
    0.3
    0.1
    u1
    u1
    t1
    r1
    t1
    r1
    t2
    A. Hotho, R. Jäschke, C. Schmitz, and G. Stumme. Information retrieval in folksonomies: Search and ranking. In Proc. ESWC, volume 4011 of LNCS, pages 411–426, Budva, Montenegro, 2006. Springer.
    r2
    u2
  • Problems of traditional folksonomies
    no tags
    jazzmusic
    armstrong
    trumpet
    baker, trumpet
    trumpet
    Tags
    armstrong, baker, dizzy,
    jazzmusic, jazz, trumpet
    dizzy, jazz
    armstrong
    ambiguity
    of tags
    synonyms
  • “Metadata” in Folksonomies
    Resource Y
    created: 1979-12-06
    creator: …
    metadata
    metadata
    metadata
    metadata
    User X
    Age: 30 years
    Education: …
    music
    jazz
    Jazz (noun) is a
    style of music that…
    jazz
    tag
    User X
    user
    resource
    TAS XY
    created: 2011-04-19
    meaning: dbpedia:Jazz
    Metadata-enabled folksonomy:
    Fc = (U, T, R, Y, M, Z)
    • M is the actual metadata information
    • Z Y xM is the set of metadata assignments
    tag assignment
  • Exploiting Metadata in Folksonomies
    DBpedia-based FolkRank can improve search performance, e.g. for Flickr images ESWC ‘10
    r1
    meaning:
    dbpedia:Jazz
    jazz
    r2
    meaning:
    dbpedia:Jazz
    jazzmusic
    Using FolkRank to search for resources related to jazz:
    … dbpedia:Jazz...
    ...
    r1 1
    r2 1
    ...
    … jazz jazzmusic ...
    ...
    r1 1
    r2 1
    ...
    FolkRank’s adjacency matrix:
  • Representing Tagging Activities in RDF
    & MOAT extension
    armstrong
    fabian
    http://example.org/23.png
    moat:tagMeaning <http://dbpedia.org/resource/Louis_Armstrong>
    Representation of tag assignment via Tag Ontology:
    <http://example.org/tas/1>
    a tag:RestrictedTagging;
    tag:taggedResource <http://example.org/23.png>;
    foaf:maker <http://fabianabel.myopenid.com>;
    tag:associatedTag <http://example.org/tag/armstrong>;
    .
    Tag ontology:http://www.holygoat.co.uk/projects/tags/
    MOAT: http://moat-project.org/
  • Pointers
    RDF vocabularies:
    Tag ontology: http://www.holygoat.co.uk/projects/tags/
    MOAT: http://moat-project.org/
    SCOT: http://www.scot-project.org/
    Tagging datasets: http://kmi.tugraz.at/staff/markus/datasets/
    ICWSM ‘10 Tutorial on Social Semantic Web: http://www.slideshare.net/Cloud/the-social-semantic-web
    NER tools: DBpedia spotlight, Alchemey, OpenCalais, Zemanta,…
    Papers:
    Folksonomy Model and FolkRank: Hotho et al.: Information retrieval in folksonomies: Search and ranking. ESWC 2006.
    MOAT framework: A. Passant: Meaning Of A Tag: A collaborative approach to bridge the gap between tagging and Linked Data. LDOW 2008.
    Learning semantics from social tagging:
    Marinho et al.: Folksonomy-based Collabulary Learning. ISWC 2008.
    Hotho et al.: Emergent Semantics in BibSonomy. LNI vol. 94, 2006.
    P. Mika: Ontologies are us: A unified model of social networks and semantics. Web Semantics vol. 5(1), 2007.
  • Micro-blogging
    Making sense of micro-blogging data
  • Challenge: inferring interests from tweets
    Personalized News Recommender
    I want my personalized news recommendations!
    Profile
    Analysis and
    User Modeling
    ?
    (How) can we infer a Twitter-based user profile that supports the news recommender?
    Semantic Enrichment, Linkage and Alignment
  • 1. Profile Type
    1. What type of concepts should represent “interests”?
    Francesca
    Schiavone
    T
    Sport
    Francesca Schiavone French Open
    #fo2010
    Profile?
    concept weight
    Francesca Schiavone won French Open #fo2010
    #
    hashtag-based
    ?
    entity-based
    French
    Open
    T
    topic-based
    #
    fo2010
    time
    June 27
    July 4
    July 11
  • Performance of User Modeling strategies
    Profile Type
    Topic-based strategy improves S@10 significantly
    #
    Entity-based strategy improves the recommendation quality significantly (MRR & S@10)
    T
  • 2. Temporal Constraints
    2. Which tweets of the user should be analyzed?
    (a) time period
    ?
    (b) temporal patterns
    Profile?
    concept weight
    end
    start
    weekends
    Morning:
    Afternoon:
    Night:
    time
    June 27
    July 4
    July 11
  • Temporal patterns of user profiles
    Temporal Constraints
    2
    1. Weekend profiles differ significantly from weekday profiles
    2. the difference is stronger than between day and night profiles
    weekday vs. weekend profiles
    d1(pweekday, pweekend)
    day vs. night profiles
    d1(pday, pnight)
    topic-based user profiles
  • Impact of temporal constraints
    Temporal Constraints
    startcomplete
    startfresh
    end
    Adapting to temporal context helps?
    Selection of temporal constraints depends on type of user profile.
    • Topic-based profiles:
    adapting to temporal
    context is beneficial
    • Entity-based profiles:
    long-term profiles
    perform better
    Recommendations = ?
    yes
    T
    time
    no
    complete: 2 months
    fresh: 2 weeks
    end
    start
    yes
    weekends
    T
    Recommendations = ?
    no
    time
  • 3. Semantic Enrichment
    3. Further enrich the semantics of tweets?
    Francesca
    Schiavone
    Francesca wins French Open
    Thirty in women's
    tennis is primordially
    old, an age when
    agility and desire
    recedes as the …
    (a) tweet-based
    Profile?
    concept weight
    Francesca Schiavone
    French Open
    Francesca Schiavone won! http://bit.ly/2f4t7a
    Tennis
    French
    Open
    (b) further enrichment
    Tennis
  • 3. Semantic Enrichment
    More distinct topics per profile
    further enrichment
    (e.g. exploiting links)
    further enrichment
    (e.g. exploiting links)
    More distinct entities per profile
    Exploiting external resources allows for significantly richer user profiles (quantitatively)
    Tweet-based
    Tweet-based
    entity-based user profiles
    topic-based user profiles
    Impact of Semantic Enrichment
  • Impact of Semantic Enrichment
    3. Semantic Enrichment
    T
    Tweet-based
    Further enrichment
    Further semantic enrichment (exploiting links) improves the quality of the Twitter-based profiles!
  • How to weights the concepts?
    4. Weighting Scheme
    Based on concept occurrence frequency (CF)
    ?
    Francesca Schiavone
    4
    Profile?
    concept weight
    3
    French Open
    6
    Tennis
    CF
    Time Sensitive
    weight(FrancescaSchiavone)
    weight(French Open)
    CF*IDF
    weight(Tennis)
    time
    June 27
    July 4
    July 11
  • Impact of weighting scheme
    4. Weighting Scheme
    Time-sensitive weighting functions perform best (for news recommendations)
    time sensitive
    not time sensitive
  • Observations
    Profile type:
    Semantic profiles (entity-based and topic-based) perform better than hashtag-based profiles
    Temporal Constraints:
    Adapting to temporal context (e.g. weekend patterns) makes sense if it does not cause sparsity problems
    Semantic Enrichment:
    Further semantic enrichment improves profile/recommendation quality
    Weighting Scheme:
    Time-sensitive weighting functions allow for best news recommendation performance
  • Pointers
    Related papers, datasets & code: http://wis.ewi.tudelft.nl/tweetum/
    ESWC 2011 workshop on “Making Sense of Microposts”: http://research.hypios.com/msm2011/
    Special Issue at Semantic Web Journal: http://www.semantic-web-journal.net/blog/special-issue-semantics-microposts (deadline: Nov 15)
  • Linking Social Data
    Cross-system User Modeling
  • profile
    ?
    Hi, I have a
    new-user problem!
    profile
    Hi, I’m back and
    I have new
    interests.
    Hi, I don’t know
    that your
    interests changed!
    Pitfalls of today’s Web Systems
    Hi, I’m your new
    user. Give me
    personalization!
    System A
    System D
    System C
    System B
    How can we tackle these problems?
    profile
    profile
    profile
    time
  • Cross-system user modeling on the Social Web
    User data on the Social Web
  • SocialGraph API
    1. get other accounts
    of user
    Account Mapping
    2. aggregate
    public profile
    data
    Social Web Aggregator
    Blog posts:
    Semantic Enhancement
    Profile Alignment
    Bookmarks:
    3. Map profiles to
    target user model
    4. enrich data with
    semantics
    Other media:
    WordNet®
    Social networking profiles:
    FOAF
    vCard
    Interweaving public user data
    Aggregated,
    enriched profile
    (e.g., in RDF or vCard)
    Google Profile URI
    http://google.com/profile/XY
    Analysis and user modeling
    5. generate user profiles
  • Analysis: form-based profiles
    338 users with filled form-based profiles at the five different services.
    2. Benefits of Profile Aggregation:
    a. more profile attributes
    b. more complete profiles
  • Overlap of tag-based profiles
    Overlap of tag-based profiles is less than 10% for more than 90% of the users
  • Cold-start: Recommending tags / bookmarks
    Hi, I’m your new
    user. Give me
    personalization!
    delicious
    profile
    profile
    ?
    user’s tags and bookmarks
    profile
    Ground truth:
    leave-n-out evaluation
    tags to explore
    Cosine-based
    recommender
    Web sites to
    bookmark
    Cross-system
    user modeling
    actual tags and bookmarks of the user
    How does cross-system user modeling impact the recommendation quality (in cold-start situations)?
  • Bookmark Recommendations
    Cross-system user modeling achieves significant improvements for cold-start bookmark recommendations
    Twitter is a more appropriate source than Flickr
    baseline
    Cross UM
    Cross UM
  • Tag Recommendations over time
    Consideration of external
    profile information (Mypes)
    also leads to significant
    improvement when the
    profiles in the target service
    are growing.
    Baseline
    (target profile)
  • Observations
    Aggregating Social Profile Data leads to tremendous (and significant) improvements of tag and bookmark recommendation quality in cold-start situations and beyond
    To optimize the performance one has to adapt the cross-system strategies to the concrete application setting
  • Pointers
    Workshop series on “Social Data on the Web”: http://sdow.semanticweb.org/
    RDF vocabularies:
    SIOC: http://rdfs.org/sioc/spec/
    FOAF: http://xmlns.com/foaf/spec/
    Weighted Interest Vocabulary: http://purl.org/ontology/wi/core#
    Papers:
    Abel et al.: Cross-system User Modeling and Personalization on the Social Web. UMUAI (to appear 2011) http://wis.ewi.tudelft.nl/papers/2011-umuai-cross-system-um.pdf
    B. Mehta. Cross System Personalization: Enabling personalization across multiple systems. PhD thesis, 2009.
  • 2 Take-away Questions
    Possible Future Work
  • What kind of knowledge can we learn from users’ tagging and micro-blogging activities?
    u1
    t1
    r1
    t2
    r2
    u2
  • Question
    compose
    answer
    Answer
    translate between query and Twitter vocabulary
    How can we find “information” in social (micro-)streams?
    see also TREC Microblogging Task: http://trec.nist.gov/data/tweets/
  • Thank you!
    Twitter: @fabianabel
    http://persweb.org/