a client application can then use the Nepomuk Expert Recommendation service(which implements the system described in this paper) by providing a keyword query taken from the user. A screenshot of a possible client application is shown in Figure 2. In the top-left corner the user can provide a keyword query and the choice of looking for experts. In the central panel a ranked list of people is presented as result of the query. In the right pane, resources related to the selected expert are shown 3 Desktop Search Evaluation Datasets valuation of desktop search algorithms effectiveness is a difficult task because of the lack of standard test collections. The main problem of building such test collection is the privacy concerns that data providers might have while sharing personal data. The privacy issue is major as it impedes the diffusion of personal desktop data among researches. Some solutions for overcoming these problems have been presented in previous work [11, 12 ness of finding experts using desktop content as evidence of ng t In this section we describe two possible datasets for evaluating the effective- fictitious desktop dataset representing two hypothetical personas. This dataset has been manually created in the context of the Nepomuk project with the goal of providing a publicly available desktop dataset with no privacy concerns. As at present, the access to the actual data is still restricted. The second one is a set of real desktop data provided by 14 employees of a research center 3.1 Fictitious data In order to obtain reproducible and comparable experimental results there is a need for a common test collection. That is, a set of resources, queries, and relevance assessments that are publicly available. In the case of Pim the privacy issue of sharing personal data has to be faced. For solving this issue the team working on the Nepomuk project has created a collection of desktop items(i.e. documents, e-mails, contacts, calendar items, ...)for some imaginary personas representing hypothetical desktop users. In this paper we describe two desktop collections built in this context The first persona is called Claudia Stern. She is a project manager and her interests are mainly about ontologies, know ledge management, and information retrieval. Her desktop contains 56 publications about her interests, 36 e-mails 19 Word documents about project meetings and deliverables, 12 slides presenta- tions, 17 calendar items, 2 contacts, and an activity log collected while a travel was being arranged (i.e, flight booking, hotel reservation, search for shopping places)containing 122 actions. These resources have been indexed using the Bea- gle++ system obtaining a total of 22588 RDf triples which have been stored in the RDF repositor The second persona is called Dirk Hagemann. He works for the project that Claudia manages and his interests are similar to those of Claudia. His desktop http://dev.nepomuksemanticdesktop.org/wiki/claudia 7http://dev.nepomuksemanticdesktop.org/wiki/dirkA client application can then use the Nepomuk Expert Recommendation service (which implements the system described in this paper) by providing a keyword query taken from the user. A screenshot of a possible client application is shown in Figure 2. In the top-left corner the user can provide a keyword query and the choice of looking for experts. In the central panel a ranked list of people is presented as result of the query. In the right pane, resources related to the selected expert are shown. 3 Desktop Search Evaluation Datasets Evaluation of desktop search algorithms effectiveness is a difficult task because of the lack of standard test collections. The main problem of building such test collection is the privacy concerns that data providers might have while sharing personal data. The privacy issue is major as it impedes the diffusion of personal desktop data among researches. Some solutions for overcoming these problems have been presented in previous work [11, 12]. In this section we describe two possible datasets for evaluating the effective￾ness of finding experts using desktop content as evidence of expertise. One is a fictitious desktop dataset representing two hypothetical personas. This dataset has been manually created in the context of the Nepomuk project with the goal of providing a publicly available desktop dataset with no privacy concerns. As at present, the access to the actual data is still restricted. The second one is a set of real desktop data provided by 14 employees of a research center. 3.1 Fictitious Data In order to obtain reproducible and comparable experimental results there is a need for a common test collection. That is, a set of resources, queries, and relevance assessments that are publicly available. In the case of PIM the privacy issue of sharing personal data has to be faced. For solving this issue the team working on the Nepomuk project has created a collection of desktop items (i.e., documents, e-mails, contacts, calendar items, . . . ) for some imaginary personas representing hypothetical desktop users. In this paper we describe two desktop collections built in this context. The first persona is called Claudia Stern6 . She is a project manager and her interests are mainly about ontologies, knowledge management, and information retrieval. Her desktop contains 56 publications about her interests, 36 e-mails, 19 Word documents about project meetings and deliverables, 12 slides presenta￾tions, 17 calendar items, 2 contacts, and an activity log collected while a travel was being arranged (i.e., flight booking, hotel reservation, search for shopping places) containing 122 actions. These resources have been indexed using the Bea￾gle++ system obtaining a total of 22588 RDF triples which have been stored in the RDF repository. The second persona is called Dirk Hagemann7 . He works for the project that Claudia manages and his interests are similar to those of Claudia . His desktop 6 http://dev.nepomuk.semanticdesktop.org/wiki/Claudia 7 http://dev.nepomuk.semanticdesktop.org/wiki/Dirk