Beagle~(++): Semantically Enhanced Searching and Ranking on the Desktop

摘要

Existing desktop search applications, trying to keep up with the rapidly increasing storage capacities of our hard disks, offer an incomplete solution for information retrieval. In this paper we describe our Beagle~(++) desktop search prototype, which enhances conventional full-text search with semantics and ranking modules. This prototype extracts and stores activity-based metadata explicitly as RDF annotations. Our main contributions are 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. Initially disconnected sets of resources on the desktop are connected by our contextual metadata, PageRank derived algorithms allow us to rank these resources appropriately. First experiments investigating precision and recall quality of our search prototype show encouraging improvements over standard search.