Using Data Mining For Facilitating User Contributions In The Social Semantic Web

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

Using Data Mining for Facilitating User Contributions in the Social Semantic Web

Doctoral Thesis / Dissertation from the year 2011 in the subject Computer Science - Internet, New Technologies, grade: 1,0, Karlsruhe Institute of Technology (KIT), language: English, abstract: Social Web applications have emerged as powerful applications for Internet users allowing them to freely contribute to the Web content, organize and share information, and utilize the collective knowledge of others for discovering new topics, resources and new friends. While social Web applications such as social tagging systems have many benefits, they also present several challenges due to their open and adaptive nature. The amount of user generated data can be extremely large and since there is not any controlled vocabulary or hierarchy, it can be very difficult for users to find the information that is of their interest. In addition, attackers may attempt to distort the system's adaptive behavior by inserting erroneous or misleading annotations, thus altering the way in which information is presented to legitimate users. This thesis utilizes data mining and machine learning techniques to address these problems. In particular, we design and develop recommender systems to aid the user in contributing to the Social Semantic Web. In addition, we study intelligent techniques to combat attacks against social tagging systems. In our work, we first propose a framework that maps domain properties to recommendation technologies. This framework provides a systematic approach to find the appropriate recommendation technology for addressing a given problem in a specific domain. Second, we improve existing graph-based approaches for personalized tag recommendation in folksonomies. Third, we develop machine learning algorithms for recommendation of semantic relations to support continuous ontology development in a social semanticWeb environment. Finally, we introduce a framework to analyze different types of potential attacks against social tagging systems and evaluate their impact on t

Redesigning Worldwide Connections

In the next twenty years, the convergence of robotics, informatics, nano-bio-technologies, genetics, information technologies, and cognitive sciences will have a significant impact on society. This convergence will lead to a revolution in the way that science, health, energy, resources, production, consumption and environment are conceptualised. However, these technologies will also pose new and specific challenges in terms of sustainability, ethics, and even expectations of the future. Indeed, today, the word “future” is often associated with pessimism and fear, much more than it was in the past. In order to face all these technological, ethical and cultural challenges, governments, industries and societies will need a robust cognitive framework, in order to grasp the complex dimensions of the technological convergence in progress, and must rapidly develop effective strategies to face the situations that will, unavoidably, take place. This book provides, through systemic and complexity theories, some of the theoretical tools necessary to tackle the opportunities and risks of the future.