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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

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