Publication
The large-scale adoption of the Web 2.0 paradigm has revolutionized the way we interact with the Web today. End-users, so far mainly passive consumers of information are now becoming active information producers, creating, uploading, and commenting on all types of digital content. As a consequence, the Web has evolved from a collection of static HTML pages to a highly interactive system, where information is being published and consumed at high rates. This has tremendously increased the amount of data available on the Web today, which brings about new challenges in terms of information management. At the same time, the increased user participation represents a new and extremely valuable source of data. While interacting with different Web 2.0 portals, users freely provide all types of information, such as annotations describing the shared resources, friendship links connecting similar users, etc., which can be exploited in order to improve the methods designed to manage online content. A particularly interesting example of user-generated data are the so-called social annotations, that users attach to resources in the context of collaborative tagging systems. This kind of meta-information opens up new opportunities for improved content search, new means to organize personal data, and ways of mining user profiles based on their annotations. Virtual friendship connections between users, as we can observe in social networks, are another rich source of information as they often group users with similar interests together, give means to study information diffusion and open ground to enhanced expert finding tasks. In this thesis, we leverage information extracted from user-generated data, in order to solve current information management problems, such as data retrieval, mining and integration. We explore different scenarios, where online content is enriched with user-defined meta-information and we identify specific problems, which we solve by leveraging this information. We start by addressing the problem of context-based information discovery in collaborative tagging systems, where we take advantage of user-defined entity graphs – such as a citation graph of publications or a friendship graph of users. In this setting, effective search solutions require a certain amount of annotations, however, content is often poorly annotated. We therefore propose a method that exploits the context-related information embedded in the graph structure, in order to automatically infer new annotations. Our approach propagates tags along the edges of the graph, based on the assumption that the neighborhood of a resource holds additional information about the resource itself. We see a similar graph structure in social communities, where users are connected via friendship links and where the neighborhood of a user reflects her community of interest. We adopt the hypothesis that users mainly annotate resources of interest to them and interpret the annotations (i.e., tags) a