focuses on using the user's desktop content as expertise evidence allowing the system to focus on the user's topics of interest thus providing high quality results The system we propose is first indexing the desktop content also using meta- data annotation that are produced by the Social Semantic Desktop system Nepo- muk [19. Our expert search system creates a vector space that includes the documents and the people that are present in the desktop content. After this step, when the desktop user issues a query of the type "Find erperts on the topic. +keywords the system shows a ranked list of people that the user can contact for getting help. Preliminary experiments show the high precision of the expert search results on topics which are covered by the desktop content. A lim itation of our system is that it can return only people that are present on the user's desktop. Therefore, the performances are poor when the desktop content (i.e, number of items and people)is limited, as for example for new employ ees, or when the queries are different from the main topics represented in the desktop. The main contributions of the paper are the description of how the beagle++ system creates metadata regarding documents and people(Section 2.1) a new system for finding experts on a semantic desktop(Section 2.2 he description of possible test datasets: one composed of fictitious data and one containing real desktop content( Section 3) preliminary experimental results showing how a focused dataset leads to high-quality expert search results( Section 4) a review of the previous systems and formal models presented in the field of expert search and Personal Information Management(PIM)(Section 5) 2 System Architecture 2.1 Generating Metadata about People In order to identify possible expert candidates and link them to desktop items we used extractors from the Beagle++ Dekstop Search Engine[13, 8. These extractors identify documents and e-mails authors by analysing the structure and the content of each file. For storing the produced metadata(see Figure 1) we employ the RDF repository developed in the Nepomuk project [19 based on Sesame for storing, querying, and reasoning about RDF and RDF Schema as well as on Lucene, which is integrated with the Sesame framework via the Lucene Sail [27, for full-text search An additional step is the entity linkage applied to the identified candidates For example, a person in e-mails is described by an e-mail address, whereas in a publication by the author' s name. Other causes for the appearance of different Ihttp://beagle2.kbs.uni-hannover.de http://www.youtubecom/watch?v=u14gdkcr7-1 http://www.openrdf.orgfocuses on using the user’s desktop content as expertise evidence allowing the system to focus on the user’s topics of interest thus providing high quality results for queries about such topics. The system we propose is first indexing the desktop content also using meta￾data annotation that are produced by the Social Semantic Desktop system Nepo￾muk [19]. Our expert search system creates a vector space that includes the documents and the people that are present in the desktop content. After this step, when the desktop user issues a query of the type “Find experts on the topic...”+keywords the system shows a ranked list of people that the user can contact for getting help. Preliminary experiments show the high precision of the expert search results on topics which are covered by the desktop content. A lim￾itation of our system is that it can return only people that are present on the user’s desktop. Therefore, the performances are poor when the desktop content (i.e., number of items and people) is limited, as for example for new employ￾ees, or when the queries are different from the main topics represented in the desktop. The main contributions of the paper are: – the description of how the Beagle++ system creates metadata regarding documents and people (Section 2.1). – a new system for finding experts on a semantic desktop (Section 2.2). – the description of possible test datasets: one composed of fictitious data and one containing real desktop content (Section 3). – preliminary experimental results showing how a focused dataset leads to high-quality expert search results (Section 4). – a review of the previous systems and formal models presented in the field of expert search and Personal Information Management (PIM) (Section 5). 2 System Architecture 2.1 Generating Metadata about People In order to identify possible expert candidates and link them to desktop items, we used extractors from the Beagle++ Dekstop Search Engine1 2 [13, 8]. These extractors identify documents and e-mails authors by analysing the structure and the content of each file. For storing the produced metadata (see Figure 1) we employ the RDF repository developed in the Nepomuk project [19] based on Sesame3 for storing, querying, and reasoning about RDF and RDF Schema, as well as on Lucene4 , which is integrated with the Sesame framework via the LuceneSail [27], for full-text search. An additional step is the entity linkage applied to the identified candidates. For example, a person in e-mails is described by an e-mail address, whereas in a publication by the author’s name. Other causes for the appearance of different 1 http://beagle2.kbs.uni-hannover.de 2 http://www.youtube.com/watch?v=Ui4GDkcR7-U 3 http://www.openrdf.org 4 http://lucene.apache.org