Mar 15, 2011

Semantic Web Research

Invitations for conferences and newsletters about upcoming journals usually provide clear descriptions of current research in the field. The text below is from the User Modeling [um] newsletter, and describes current research on the Personal and Social Semantic Web:

Social Web sites, such as Facebook, YouTube, Delicious, Flickr and Wikipedia, and numerous other Web applications, such as Google and Amazon, rely on implicitly or explicitly collected data about their users and their activities to provide personalized content and services. As these applications become more and more connected on the Semantic Web, a major challenge is to allow various applications to exchange, reuse, and integrate user data from different sources. Such data comes in different flavors: user data such as user profiles, social networking/tagging/blogging data, etc. as well as usage data like clickthrough data or query logs. The amount of people's data available on the Web is tremendously growing so that sharing and mining these heterogeneous data corpora distributed on the Web is a non-trivial problem that poses several challenges to the Semantic Web community.

Semantic interoperability between Social Web applications is becoming increasingly important as users leave a plethora of traces at diverse services on the Web. Semantic Web and Social Web technologies and paradigms provide means to facilitate integration of user and usage data, for example, with the principles of Linked Data and Microformats, vocabulary standards such as FOAF and SIOC, standardized APIs such as OpenSocial, or support for schema matching as provided by the Silk framework. Further, mechanisms like WebID, OpenId, OAuth and FOAF+SSL allow for identification and authorization on the Social Web. Hence, the time is right to exploit and improve such technologies for connecting user and usage data traces on the Social Semantic Web.

Linking distributed traces of user data provides new possibilities for inferring and modeling user preferences and personalizing Web systems to individual needs. Novel models, techniques, frameworks and systems have to be developed to leverage Social Web semantics. While linkage of user and usage data promises advantages for recommendation and personalization, it also raises questions related to provenance, trust and privacy: how does one know that the data gathered from several sources can be trusted, and how can one avoid that sensitive personal data is disclosed to certain services or used to infer and expose sensitive information? Trust and privacy, and associated policies, may therefore impact mining and reasoning on the people's data.

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1 comment:

  1. Excellent pieces. Keep posting such kind of information on your blog. I really impressed by your blog.