DRESS: Data-Repository Enhancer through Semantic Sources

Abstract

In recent years, there has been a huge research effort in the field of Knowledge Base Population (KBP). General approaches based on statistical techniques have been applied to popular resources on the Web (e.g., Wikipedia) with successful results. However, when it comes to small and private digital libraries, where the stored data is scarce and the existing entities might not be so popular, such approaches are not usually enough: many of the common techniques may lack specific tools to disambiguate entities that operate in a local environment. In this paper, we propose an approach to deal with private and isolated digital collections. Our proposed system (named DRESS, with the idea of “dressing” the digital library) builds a domain Knowledge Base (KB) from scratch, leveraging the available local knowledge. Then, DRESS enriches the KB integrating the local data with external knowledge obtained from the Semantic Web. Enhancing the digital repository in this way allows to build high value services for the user, recommending contents and improving the presentation of results (i.e., via infoboxes). Preliminary evaluations of the system have been carried out with promising results.