Semantically Enhanced Searching and Ranking on the Desktop

Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, offer an incomplete solution for the information retrieval. In this paper we describe our desktop search prototype, which enhances conventional full-text search with semantics and ranking modules. In this prototype we extract and store activity-based metadata explicitly as RDF annotations. Our main contributions are represented by the extensions we integrate into the Beagle desktop search infrastructure to exploit this additional contextual information for searching and ranking the resources on the desktop. Contextual information plus ranking brings desktop search much closer to the performance of web search engines. The initially disconnected sets of resources on the desktop are connected by our contextual metadata, and then a PageRank derived algorithm allows us to rank these resources appropriately. Finally, we use a detailed working scenario to discuss the advantages of this approach, as well as the user interfaces of our search