The document summarizes the Live Social Semantics (LSS) platform, which aims to integrate users' physical interactions at events with their online social networking and interests. LSS collects data on face-to-face interactions at conferences using RFID badges and links it to users' social media profiles and tagging activities to generate semantic user profiles. It addresses challenges like ambiguous tags by cleaning data and linking tags to semantic concepts. The platform has been deployed at several conferences to test merging online and offline social graphs and providing personalized recommendations.
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.