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Interlinking semantics, web2.0, and the real-world
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Interlinking semantics, web2.0, and the real-world

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Invited talk at APRESW Workshop, Extended Semantic Web Conference, Crete, 2010...

Invited talk at APRESW Workshop, Extended Semantic Web Conference, Crete, 2010

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  • Pain to control Dislikes and distrusted because we don’t know what they listen to, and who they talk to, and what they do with that data.
  • Publishing web (publishing) – blogging, tweeting, updating their status, etc. Sharing web (sharing) – want others to see what we publish, which groups we’re in, what we like and dislike, our opinion on things, what we’ve been up to, places we’re visitingSocial networks (networking) – becoming part of online communities, of friends, colleagues, or complete strangers And as you know, the social web is increasingly becoming the new renewable energy of RS – it’s cheap, exists in abundance all around us, but largely untamed.Spyware is not the answer, where you develop apps that sit inside computers to monitor and analyze what we browse. Much research went into that direction. Problem is that people lose control on when and what to share and what not to share. Of course they could always switch off/on the spyware but it’s a headache and people worry that they might forget, or don’t trust the tool to behave like it should.
  • Disconnection of knowledge and social networkWant the SNS to talk to each other so they can give me a better serviceOvertime, the cumulative frequencies of the tags you use canbe represented with a tag-cloud. This gives a visual snapshot of the terms that you use most frequently.When we began this work, the first thing we did was develop a toolFor viewing tag clouds from multiple domains. We noticed thatmany tags represented concepts that could be considered Interests of the users.Hence, the motivation for our work is to exploit this tagging
  • Facebook is heading the move towards globalising how you learn about what your users like, by allowing them to tell you what they like wherever they are whenever they like. Problem – you don’t know who they will sell this info to, info locked within Facebook
  • What u like: from browsing, purchasingWhom u know  what they like  what you might like
  • The DBpedia knowledge base currently describes more than 3.4 million things, out of which 1.5 million are classified in a consistent Ontology, including 312,000 persons, 413,000 places, 94,000 music albums, 49,000 films, 15,000 video games, 140,000 organizations, 146,000 species and 4,600 diseases. The DBpedia data set features labels and abstracts for these 3.2 million things in up to 92 different languages; 841,000 links to images and 5,081,000 links to external web pages; 9,393,000 external links into other RDF datasets, 565,000 Wikipedia categories, and 75,000 YAGO categories. The DBpedia knowledge base altogether consists of over 1 billion pieces of information (RDF triples) out of which 257 million were extracted from the English edition of Wikipedia and 766 million were extracted from other language editions
  • Sense here refers to adding meaning to tags, structure, modeling users
  • Disambiguation based on similarity of term vectors of Dbpedia pages and tag terms based on their frequency.

Transcript

  • 1. Interlinking semantics, web2.0, and the real-world
    HarithAlani
    Knowledge Media institute, OU
    APRESW Workshop, Extended Semantic Web Conference, Crete, 2010
  • 2. Learning about YOU!
    Sites learn about what you like from your browsing/purchasing history
    Cold start problem
    New user
    New site
    New product, product range
    Sparse knowledge
    Limited to interactions within the site
    Can’t learn if you are using other sites
    No to Spyware!
    Tools that sit inside computers and monitor browsing behaviour and content
    Much research went into that direction for building RSs
    Pain to control what they should/shouldn’t access and when
    2
  • 3. New info sources for recommendation systems
    Micro publishing
    blogging
    tweeting
    updating status
    messaging
    Posting
    3
    publishing
    • Sharing
    • 4. wish for others to see what we publish
    • 5. know which groups we are in
    • 6. what we like
    • 7. where we’re going
    networking
    sharing
    • Online networking
    • 8. Becoming part of an online community
    • 9. Connecting with friends, colleagues, family
    • 10. Participating in groups and discussions
  • Can you tell what my interests are?
    4
  • 11. Facebook’s Open Graph
    Collects “like” information from anywhere about anything!
    “Based on the structured data you provide via the Open Graph protocol, your pages show up richly across Facebook: in user profiles, within search results and in News Feed.”
    5
  • 12. Personal interests and the social web
    WHAT YOU LIKE
    WHOM YOU KNOW
    6
  • 13. What about the semantics?
    7
  • 14. Un-Semantic Recommender Systems
    8
    Collaborative filtering is scalable, relatively cheap, and requires little background knowledge
    But can semantics help improve recommendation accuracy? Could it be cost effective?
  • 15. Semantics from Linked Open Data Cloud
    2007
    millions of objects
    Billions of triples
    9
  • 16. DBpedia – a Linked Data hub
    10
    Status: No Relation found
  • 17. 11
    Social content
    Recommender Systems
    Social networks
    Semantic web
    YES … BUT!!
  • 18. Challenges
    Tag ambiguity, misspellings, redundancy
    No semantic structure
    Distributed and disintegrated personal tag clouds
    Disconnected social network islands
    Limited accessibility to data on SNSs
    12
    publishing
    networking
    sharing
    Live Social Semantics platform aims at solving these problems!
  • 19. Social+Semantics+RFID: Live Social Semantics
    Integration of physical presence and online information
    Semantic user profile generation
    Interest identification from distributed tagging activities
    Large-scale, real-world social gatherings
    Logging of face-to-face contact
    Social network browsing
    On-site and post-event support for social networking
    13
  • 20. Making Sense of Folksonomies
    Semantic User Profiles
    FOAF
    DBpedia + Wordnet
    Identity Integration
    Tag Integration
    Delicious
    Last.fm
    Flickr
    Facebook

  • 21. Live Social Semantics: architecture
    15
  • 22. Live Social Semantics: architecture
    16
  • 23. From social to semantics
    Cleaning up the tag
    Associating tags with semantics
    Integrating tagging information
    Collecting and merging social networks
    17
  • 24. From social to semantics
    Cleaning up the tag
    Associating tags with semantics
    Integrating tagging information
    Collecting and merging social networks
    18
  • 25. Tag Filtering Service
    Semantic modeling
    Semantic analysis
    Collective intelligence
    Statistical analysis
    Syntactical analysis
  • 26. Tag Filtering Service
    http://tagora.ecs.soton.ac.uk/tsr/tag_filtering.html
  • 27. From social to semantics
    Cleaning up the tag
    Associating tags with semantics
    Integrating tagging information
    Collecting and merging social networks
    21
  • 28. From Tags to Semantics
    22
  • 29. Tag Disambiguation
    Term vector similarity
    Term vector from tag co-occurrence
    Term vector for each suggested Dbpedia disambiguation page
    23
    apple, film, 1980, ..
    apple, inc, computer, ..
    apple, iphone, computer, ..
    apple, tree, fruit, ..
  • 30. Tags to User Interests
    Based on 72 POIs verified by users
    24
  • 31. From social to semantics
    Cleaning up the tag
    Associating tags with semantics
    Integrating tagging information
    Collecting and merging social networks
    25
  • 32. Connecting it all
    26
  • 33. Tag structuring
    27
  • 34. From social to semantics
    Cleaning up the tag
    Associating tags with semantics
    Integrating tagging information
    Collecting and merging social networks
    28
  • 35. Merging social networks
    29
  • 36. From raw tags and social relations to Linked Data
    Collective intelligence
    User raw data
    Semantic data
    Linked data
    ontologies
  • 37. Live Social Semantics: architecture
    31
  • 38. SocioPatterns platform: motivation
    fundamental knowledge on human contact
    epidemiological relevance for airborne pathogens
    communication in mobile scenarios
    organizational investigation
    ubiquitous social networking
    augmented (social) reality
    32
  • 39. Convergence with online social networks
    33
    leverage social context
  • 40. SocioPatternsRFIDs and data collection
    34
  • 41. Live Social Semantics: architecture
    35
  • 42.
  • 43. 37
    http://www.vimeo.com/6590604
  • 44. web-based user-centered interface
    38
  • 45. 39
  • 46. Deployed at:
    Live Social Semantics
  • 47. Statistics
    ESWC 2009
    attended by over 300 people
    187 collected an RFID
    139 created accounts on LSS site
    HyperText 2009
    attended by around 150 people
    113 collected an RFID
    97 registered on LSS site
    41
  • 48. Survey of users who didn’t provide LSS with any SNS accounts
    84 registered with no SNS accounts
    36 responded to our survey
    Some used LinkedIn or xing
    This survey does not include conf attendees who did not participate in LSS
    42
  • 49. Recommendation Services for LSS
    Recommending talks and sessions
    If speakers are in your online social network
    If speakers are in your community of practice network
    If you have met the speakers during the conference or in past events
    Recommending people for your online social network
    If you spent time talking to someone not in your online social network
    If you met someone who is influential, active
    If you have strong indirect connections to a person you met
    Recommending people you should meet
    If you have strong overlap of interests
    If your community of practice is very similar
    If you have an overlapping social network
    Recommending popular topics/sessions to organisers
    If a talk/session is heavily attended
    If a talk/speaker generated much attention
    43
  • 50. Acknowledgement
    CiroCattuto - ISI Turin
    Wouter van Den Broeck - ISI Turin
    Martin Szomszor - CeRC, City University, UK
    Alain Barrat - CPT Marseille & ISI
    GianlucaCorrendo – Uni Southampton, UK
    Organizers of ESWC 2009, HT 2009, and ESWC 2010
    Users of LSS!
    Live Social Semantics references:
    Szomszor, M., et al. (2010) Semantics, Sensors, and the Social Web: The Live Social Semantics experiments. Extended Semantic Web Conference (ESWC), Crete.
    Broeck, W., et al. (2010) The Live Social Semantics application: a platform for integrating face-to-face presence with on-line social networking, Workshop on Communication, Collaboration and Social Networking in Pervasive Computing Environments (PerCol), IEEE PerCom, Mannheim.
    Alani, H., et al. (2009) Live Social Semantics. In: 8th International Semantic Web Conference (ISWC, US.
    44
  • 51. THANKS!
    please consider participating in LSS
    http://tagora.ecs.soton.ac.uk
    45