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Interlinking semantics, web2.0, and the real-world,[object Object],HarithAlani,[object Object],Knowledge Media institute, OU ,[object Object],APRESW Workshop, Extended Semantic Web Conference, Crete, 2010,[object Object]
Learning about YOU!,[object Object],Sites learn about what you 					       like from your browsing/purchasing history,[object Object],Cold start problem,[object Object],New user,[object Object],New site,[object Object],New product, product range,[object Object],Sparse knowledge,[object Object],Limited to interactions within the site,[object Object],Can’t learn if you are using other sites,[object Object],No to Spyware!,[object Object],Tools that sit inside computers and monitor browsing behaviour and content,[object Object],Much research went into that direction for building RSs,[object Object],Pain to control what they should/shouldn’t access and when,[object Object],2,[object Object]
New info sources for recommendation systems,[object Object],Micro publishing,[object Object],blogging,[object Object],tweeting,[object Object],updating status,[object Object],messaging,[object Object],Posting,[object Object],3,[object Object],publishing,[object Object],[object Object]
wish for others to see what we publish
know which groups we are in
what we like
where we’re goingnetworking,[object Object],sharing,[object Object],[object Object]
Becoming part of an online community
Connecting with friends, colleagues, family
Participating in groups and discussions,[object Object]
Facebook’s Open Graph,[object Object],Collects “like” information from anywhere about anything!,[object Object],“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.”,[object Object],5,[object Object]
Personal interests and the social web,[object Object],WHAT YOU LIKE,[object Object],WHOM YOU KNOW,[object Object],6,[object Object]
What about the semantics?,[object Object],7,[object Object]
Un-Semantic Recommender Systems,[object Object],8,[object Object],Collaborative filtering is scalable, relatively cheap, and requires little background knowledge,[object Object],But can semantics help improve recommendation accuracy? Could it be cost effective?  ,[object Object]
Semantics from Linked Open Data Cloud,[object Object],2007,[object Object],millions of objects,[object Object],Billions of triples,[object Object],9,[object Object]
DBpedia – a Linked Data hub ,[object Object],10,[object Object],Status: No Relation found,[object Object]
11,[object Object],Social content,[object Object],Recommender Systems,[object Object],Social networks,[object Object],Semantic web,[object Object],YES … BUT!!,[object Object]
Challenges,[object Object],Tag ambiguity, misspellings, redundancy,[object Object],No semantic structure,[object Object],Distributed and disintegrated personal tag clouds ,[object Object],Disconnected social network islands,[object Object],Limited accessibility to data on SNSs,[object Object],12,[object Object],publishing,[object Object],networking,[object Object],sharing,[object Object],Live Social Semantics platform aims at solving these problems!,[object Object]
Social+Semantics+RFID: Live Social Semantics,[object Object],Integration of physical presence and online information,[object Object],Semantic user profile generation,[object Object],Interest identification from distributed tagging activities ,[object Object],Large-scale, real-world social gatherings,[object Object],Logging of face-to-face contact,[object Object],Social network browsing,[object Object],On-site and post-event support for social networking,[object Object],13,[object Object]
Making Sense of Folksonomies,[object Object],Semantic User Profiles,[object Object],FOAF,[object Object],DBpedia + Wordnet,[object Object],Identity Integration,[object Object],Tag Integration,[object Object],Delicious,[object Object],Last.fm,[object Object],Flickr,[object Object],Facebook,[object Object],…,[object Object]
Live Social Semantics: architecture,[object Object],15,[object Object]
Live Social Semantics: architecture,[object Object],16,[object Object]
From social to semantics,[object Object],Cleaning up the tag ,[object Object],Associating tags with semantics,[object Object],Integrating tagging information,[object Object],Collecting and merging social networks,[object Object],17,[object Object]
From social to semantics,[object Object],Cleaning up the tag ,[object Object],Associating tags with semantics,[object Object],Integrating tagging information,[object Object],Collecting and merging social networks,[object Object],18,[object Object]
Tag Filtering Service,[object Object],Semantic modeling,[object Object],Semantic analysis,[object Object],Collective intelligence,[object Object],Statistical analysis,[object Object],Syntactical analysis,[object Object]
Tag Filtering Service,[object Object],http://tagora.ecs.soton.ac.uk/tsr/tag_filtering.html,[object Object]
From social to semantics,[object Object],Cleaning up the tag ,[object Object],Associating tags with semantics,[object Object],Integrating tagging information,[object Object],Collecting and merging social networks,[object Object],21,[object Object]
From Tags to Semantics,[object Object],22,[object Object]
Tag Disambiguation,[object Object],Term vector similarity,[object Object],Term vector from tag co-occurrence  ,[object Object],Term vector for each suggested Dbpedia disambiguation page ,[object Object],23,[object Object],apple, film, 1980, ..,[object Object],apple, inc, computer, ..,[object Object],apple, iphone, computer, ..,[object Object],apple, tree, fruit, ..,[object Object]
Tags to User Interests,[object Object],Based on 72 POIs verified by users,[object Object],24,[object Object]
From social to semantics,[object Object],Cleaning up the tag ,[object Object],Associating tags with semantics,[object Object],Integrating tagging information,[object Object],Collecting and merging social networks,[object Object],25,[object Object]
Connecting it all ,[object Object],26,[object Object]
Tag structuring ,[object Object],27,[object Object]
From social to semantics,[object Object],Cleaning up the tag ,[object Object],Associating tags with semantics,[object Object],Integrating tagging information,[object Object],Collecting and merging social networks,[object Object],28,[object Object]
Merging social networks,[object Object],29,[object Object]
From raw tags and social relations to Linked Data,[object Object],Collective intelligence,[object Object],User raw data ,[object Object],Semantic data,[object Object],Linked data ,[object Object],ontologies,[object Object]
Live Social Semantics: architecture,[object Object],31,[object Object]
SocioPatterns platform: motivation,[object Object],fundamental knowledge on human contact,[object Object],epidemiological relevance for airborne pathogens,[object Object],communication in mobile scenarios,[object Object],organizational investigation,[object Object],ubiquitous social networking,[object Object],augmented (social) reality,[object Object],32,[object Object]
Convergence with online social networks,[object Object],33,[object Object],leverage social context,[object Object]
SocioPatternsRFIDs and data collection,[object Object],34,[object Object]
Live Social Semantics: architecture,[object Object],35,[object Object]
Interlinking semantics, web2.0, and the real-world
37,[object Object],http://www.vimeo.com/6590604,[object Object]
web-based user-centered interface,[object Object],38,[object Object]

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

Editor's Notes

  1. 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.
  2. 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.
  3. 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
  4. 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
  5. What u like: from browsing, purchasingWhom u know  what they like  what you might like
  6. 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
  7. Sense here refers to adding meaning to tags, structure, modeling users
  8. Disambiguation based on similarity of term vectors of Dbpedia pages and tag terms based on their frequency.