** Social Web and Web Science Track **

The success of Social Web applications (often referred to as “Web2.0” applications) is manifest through the fast growth of social networks and sites with user-generated content, like Facebook, Twitter, YouTube, Wikipedia, Flickr, and many more. Many Social Web applications have simplified the data publishing process using user-friendly and interactive tools and practices (such as Wikis, tagging, microblogging, etc.) and have decreased the cost and increased the incentive to contribute data. In addition, some trends such as ubiquitous computing lead to new ways and means to share content in real-time within social communities.

The combination of Social Web principles and Semantic Web technologies allows end-users to massively produce and use semantic data through social applications, which in turn enables smarter Web-based applications in various domains. This includes the Social Web itself, where it becomes possible to mine Semantic Web data and discover relationships that were not obvious, whether it is in social network identification or for information retrieval purposes. These can be exploited for various purposes: to personalise applications, recommend content, generate new knowledge, and more. But besides the technical aspect, there is also a need to understand the behaviours and patterns of users on the Web, and in particular on the Social Web. Web science aims to address these issues, also considering other aspects that are important to realise a Social Semantic Web, such as governance, law, policies and decision-making, etc.

This track on “Social Web and Web Science” aims at bringing together researchers from these communities to address various challenges from improving Social Web user experiences with Semantic Web technologies in order to build novel semantic applications using Social Web data, as well as understanding the various patterns of the Web. Successful submissions will address at least some aspect of both areas.

Topics of interest include, but are not limited to:
- Collaborative and collective semantic data generation and publishing
- Social and semantic bookmarking, tagging and annotation
- Enriching Social Web with semantic data: RDFa, micro formats and other approaches
- Linked data on the Social Web
- Semantically-enabled social platforms and applications: semantic wikis, semantic desktops, semantic portals, semantic blogs, semantic calendars, semantic email, semantic news, etc.
- Querying, mining and analysis of social semantic data
- User profile construction based on tagging and annotations
- Reasoning and personalisation based on semantics: recommendations, social navigation, social search, etc.
- Privacy, policy and access control on Social Semantic Web
- Provenance, reputation and trust on Social Semantic Web
- Formation, management and understanding of semantically interlinked online communities
- Citizen sensing and ubiquitous Social Semantics
- Social Semantic Web and Internet of Things

** PC members (preliminary) **

- Aidan Hogan, DERI, National University of Ireland, Galway, Ireland
- Akshay Java, Microsoft, USA
- Anna Fensel, FTW, Austria
- Antonina Dattolo, University of Udine, Italy
- Chris Brooks, San Francisco University, USA
- Christopher Thomas, Wright State University, USA
- Cristina Gena, University of Turin, Italy
- Dae-Ki Kang, Dongseo University, Korea
- Daniel Olmedilla, Telefonica R&D, Spain
- Darina Dicheva, Winston-Salem State University, USA
- Dave Beckett, Digg, USA
- Dave Braines, IBM, UK
- Edward Benson, MIT, USA
- Enrico Motta, The Open University, UK
- Federica Cena, University of Turin, Italy
- Francesca Carmagnola, University of Turin, Italy
- Georgia Koutrika, Stanford University, USA
- Guilin Qi, Southeast University, China
- Haizheng Zhang, Microsoft, USA
- Haofen Wang, Apex Data & Knowledge Management Lab, Shanghai Jiao Tong University, China
- Jie Zhang, University of Waterloo
- John Breslin, DERI, National University of Ireland, Galway, Ireland
- Jon Dron, Athabasca University, Canada
- Juan F. Sequeda, The University of Texas at Austin
- Juanzi Li, Tsinghua University, China
- Jyotishman Pathak, Mayo Clinic, USA
- Lalana Kagal, MIT, USA
- Lina Zhou, University of Maryland Baltimore County, USA
- Markus Krötzsch, Karlsruhe Institute of Technology (KIT), Institute AIFB, Germany
- Mary-Anne Williams, University of Technology, Sydney, Australia
- Meenakshi Nagarajan, Wright State University, USA
- Mike Dean, BBN Technologies, USA
- Milos Kravcik, RWTH Aachen, Germany
- Paul Smart, University of Southampton, UK
- Pranam Kolari, Yahoo! Labs, USA
- Richard Cyganiak, DERI, National University of Ireland, Galway, Ireland
- Robert Jaschke, University of Kassel, Germany
- Scott Bateman, University of Saskatchewan, Canada
- Sergey Sosnovsky, DFKI, Germany
- Shlomo Berkovsky, CSIRO, Australia
- Sören Auer, University of Leipzig, Germany
- Steffen Staab, University of Koblenz-Landau, Germany
- Steve Harris, Garlik, UK
- Tom Heath, Talis Information Ltd, UK
- Tsvi Kuflik, The University of Haifa, Israel
- Yihong Ding, Fujifilm Medical Systems, USA
- Ying Ding, Indiana University, USA
- Yuzhong Qu, Nanjing University, China