Semantically enhanced searching and ranking on the desktop

Abstract

Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, o#er 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