Desktop Content BibTex Metadata annotations Content RDF Candidates Documents content Client Expert Search Application Space Fig 1. An overview of how the desktop content is extracted and given in input to the expert search component for indexing. A client application is providing a user interface to the expert search service. references to the same entity are misspellings, the use of abbreviations, initials might change). Again, we exploit a component of the Beagle++ search berson or the actual change of the entity over time(e. g, the e-mail address of a person for producing information about the linkage At this point, we obtained a repository describing desktop items content and metadata. In the next section we explain how we can exploit this data and metadata for finding experts in the semantic desktop content 2.2 Leveraging Metadata for People Search In the Nepomuk system, the service of Expert Recommendation aims at pro- viding the user with a list of experts(i.e, people)on a given topic. The experts are selected among a list of persons referral to in the desktop. In order to do so, the component needs to extract, out of the RDF repository, some information about the content of documents and e-mails and also a list of expert candidates Thanks to the Beagle++ system, relations between people and documents e identified and stored in the repository. Entity Linkage identifies references pointing to the same entity by gathering clues as, for example, a person in e- mails described by an e-mail address, whereas in a publication by the authors name. In Beagle++, searching using a person's surname retrieves publications http://dev.nepomuksemanticdesktop.org/wiki/expertrecommenderFig. 1. An overview of how the desktop content is extracted and given in input to the expert search component for indexing. A client application is providing a user interface to the expert search service. references to the same entity are misspellings, the use of abbreviations, initials, or the actual change of the entity over time (e.g., the e-mail address of a person might change). Again, we exploit a component of the Beagle++ search system for producing information about the linkage. At this point, we obtained a repository describing desktop items content and metadata. In the next section we explain how we can exploit this data and metadata for finding experts in the semantic desktop content. 2.2 Leveraging Metadata for People Search In the Nepomuk system, the service of Expert Recommendation5 aims at pro￾viding the user with a list of experts (i.e., people) on a given topic. The experts are selected among a list of persons referral to in the desktop. In order to do so, the component needs to extract, out of the RDF repository, some information about the content of documents and e-mails and also a list of expert candidates (see Figure 1). Thanks to the Beagle++ system, relations between people and documents are identified and stored in the repository. Entity Linkage identifies references pointing to the same entity by gathering clues as, for example, a person in e￾mails described by an e-mail address, whereas in a publication by the author’s name. In Beagle++, searching using a person’s surname retrieves publications 5 http://dev.nepomuk.semanticdesktop.org/wiki/ExpertRecommender