similarity measure. We used two artificial datasets for performing preliminary experiments. The results show that search results are good for topics that are well represented in the desktop content and poor for others. Effectiveness might be improved by exploiting external evidence of expertise as, for example, web pages. The Beagle++ system indexes visited web pages and, therefore, it could include information from the web also leveraging on semantic technologies such as microforomats or RDFa. Moreover, evidence of expertise contained in both the enterprise intranet and the desktop could be combined in order to generate better results. As future work, we aim at performing a user study using the collection made of data from real user desktops(see Section 3. 2 )with the goal of evaluating the effectiveness of the expert finding system presented in this paper Acknowledgements. We thank the anonymous reviewers for their valuable comments. This work is partially supported by the eU Large-scale Integrating Project OKKAM-Enabling a Web of Entities(contract no. ICT-215032)and y the nePomuK project funded by the European Commission under the 6th Framework Programme(IST Contract No. 027705) References L. Azzopardi, K. Balog, and M. de Rijke. Language modeling approaches for enterprise tasks. The Fourteenth Tert REtrieval Conference(TREC 2005 ) 2006 2. K. Balog, L. Azzopardi, and M. de rijke. Formal models for expert finding in enterprise corpora. Proceedings of the 29th SIGIR conference, pages 43-50, 2006 K. Balog and M. de rijke. Finding experts and their Details in e-mail corpora Proceedings of the 15th intenational conference on World wide Web, pages 1035- 036.2006 4. K. Balog and M. de rijke. Searching for people in the personal work space. Inter. ational Workshop on Intelligent Information Access(IIIA-2006), 2006 5. K. Balog and M. de Rijke. Determining Expert Profiles(With an Application to Expert Finding). Proceedings of IJCA1-2007, pages 2657-2662, 2007 6. K. Balog and M. de rijke. Associating people and documents. In ECIR, pages 296-308.2008 7. S. Brin and L Page. The anatomy of a large-scale hypertextual Web search engine Computer Networks and ISDN Systems, 30(1-7): 107-117, 1998 8. I Brunkhorst, P. A. Chirita, S Costache, J. Gaugaz, E Ioannou, T. lofciu, E. Mi- ack, W. Nejdl, and R. Paiu. The beagle++ toolbox: Towards an extendable desktop search architecture. Proceedings of the Semantic Desktop and Social Se. antic Collaboration Workshop(Sem Desk 2006), November 2006 9. C. Campbell, P. Maglio, A. Cozzi, and B. Dom. Expertise identification using email communications. Proceedings of the 12th ACM Conference on Information d Knowledge Management(CIKM'03, pages 528-531 10. V. Carvalho and w. Cohen. Learning to Extract Signature and Reply Lines from Email. Proceedings of the Conference on Email and Anti-Spam, 2004 11. S. Chernov, G. Demartini, E. Herder, M. Kopycki, and w. Nejdl. Evaluating personal information management using an activity logs enriched desktop dataset In Proceedings of 3rd Personal Information Management Workshop(PIM 2008) 2008 http://fp7.okkam.org http://www.nepomuk.semanticsimilarity measure. We used two artificial datasets for performing preliminary experiments. The results show that search results are good for topics that are well represented in the desktop content and poor for others. Effectiveness might be improved by exploiting external evidence of expertise as, for example, web pages. The Beagle++ system indexes visited web pages and, therefore, it could include information from the web also leveraging on semantic technologies such as microforomats or RDFa. Moreover, evidence of expertise contained in both the enterprise intranet and the desktop could be combined in order to generate better results. As future work, we aim at performing a user study using the collection made of data from real user desktops (see Section 3.2) with the goal of evaluating the effectiveness of the expert finding system presented in this paper. Acknowledgements. We thank the anonymous reviewers for their valuable comments. This work is partially supported by the EU Large-scale Integrating Project OKKAM11 - Enabling a Web of Entities (contract no. ICT-215032) and by the NEPOMUK12 project funded by the European Commission under the 6th Framework Programme (IST Contract No. 027705). References 1. L. Azzopardi, K. Balog, and M. de Rijke. Language modeling approaches for enterprise tasks. The Fourteenth Text REtrieval Conference (TREC 2005), 2006. 2. K. Balog, L. Azzopardi, and M. de Rijke. Formal models for expert finding in enterprise corpora. Proceedings of the 29th SIGIR conference, pages 43–50, 2006. 3. K. Balog and M. de Rijke. Finding experts and their Details in e-mail corpora. Proceedings of the 15th international conference on World Wide Web, pages 1035– 1036, 2006. 4. K. Balog and M. de Rijke. Searching for people in the personal work space. Inter￾national Workshop on Intelligent Information Access (IIIA-2006), 2006. 5. K. Balog and M. de Rijke. Determining Expert Profiles (With an Application to Expert Finding). Proceedings of IJCAI-2007, pages 2657–2662, 2007. 6. K. Balog and M. de Rijke. Associating people and documents. In ECIR, pages 296–308, 2008. 7. S. Brin and L. Page. The anatomy of a large-scale hypertextual Web search engine. Computer Networks and ISDN Systems, 30(1-7):107–117, 1998. 8. I. Brunkhorst, P. A. Chirita, S. Costache, J. Gaugaz, E. Ioannou, T. Iofciu, E. Mi￾nack, W. Nejdl, and R. Paiu. The beagle++ toolbox: Towards an extendable desktop search architecture. Proceedings of the Semantic Desktop and Social Se￾mantic Collaboration Workshop (SemDesk 2006), November 2006. 9. C. Campbell, P. Maglio, A. Cozzi, and B. Dom. Expertise identification using email communications. Proceedings of the 12th ACM Conference on Information and Knowledge Management (CIKM’03), pages 528–531, 2003. 10. V. Carvalho and W. Cohen. Learning to Extract Signature and Reply Lines from Email. Proceedings of the Conference on Email and Anti-Spam, 2004. 11. S. Chernov, G. Demartini, E. Herder, M. Kopycki, and W. Nejdl. Evaluating personal information management using an activity logs enriched desktop dataset. In Proceedings of 3rd Personal Information Management Workshop (PIM 2008), 2008. 11 http://fp7.okkam.org 12 http://www.nepomuk.semanticdesktop.org