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Exploiting metadata for context creation and ranking on the desktop

With the ever increasing size of the number of resources we store on our computers, there is an obvious need for better tools for managing our personal information. First, there is a need of keeping resources connected beyond the simple folder hierarchies, in order to reflect the user working contexts and tasks. The main problem is that as soon as we store something on our computers, for example a file, then the connection to the email that it was sent with is immediately lost, and also the whole context around it. And second, while faced with this vast amount of data, even if we are able to search and exploit these connections, an ordering is very much needed. We need this not only for retrieving our own data from our PCs, but we also need to be able to give more importance to some resources coming from more trusted persons. In this thesis we propose several solutions not only for enhancing the current data with semantic connections in order to create contexts, but also for ranking resources both on the desktop, and in a collaborative environment where users exchange resources and need to take trust and privacy into account. Several experiments support the ideas proposed and also detail on various situations on which method is best to be applied. We first focus on the enhancement of resources with context metadata, by recreating lost connections among them. We propose several modules fully integrated within the Beagle system and also show via experiments that these metadata generators are useful in finding resources. Also, time connections are exploited and we show that such connections are valuable, since they simulate the normal user behaviour when working on a task several resources are accessed in a sequence rather frequently. Finally, we show how annotations can be a step further for extending the desktop to the Web we automatically extract personalized annotations from within the desktop documents and use them for the annotation of visited web pages. Then, we concentrate upon the benefits that a ranking mechanism can bring. We build on top of the PageRank algorithm a semantic ranking mechanism applied to the desktop, which fully exploits the time connections previously created. We also extend to the collaborative environment and show how recommendations coming from within the user’s working group can be ranked, by taking into account the trust that he has in the persons that sent him those resources. Also, more trust and privacy issues are further explored on how we can share our resources but not disclosing the structure of our resources. A world node solution is proposed and we prove that it is a good trade-off between quality and privacy, given also various amounts of data that are fully shared between users.