Finding Experts on the Semantic Desktop Gianluca demartini and Claudia Niedere Leibniz universitat hannover Appelstrasse 9a, 30167 Hannover, Germany Abstract. Expert retrieval has attracted deep attention because of the huge economical impact it can have on enterprises. The classical dataset on which to perform this task is company intranet (i. e, personal pages, e-mails, documents). We propose a new system for finding experts in the users desktop content. Looking at private documents and e-mails of the user, the system builds expert profiles for all the people named in the desktop. This allows the search system to focus on the user's topics of interest thus generating satisfactory results on topics well represented on le desktop. We show, with an artificial test collection, how the desk- top content is appropriate for finding experts on the topic the user is interested in 1 Introduction Finding people who are expert on certain topics is a search task which has been mainly investigated in the enterprise context. Especially in big enterprises, topic areas can range very much also because of diverse and distributed data sources This peculiarity of enterprise datasets can highly affect the quality of the results of the expert finding task 15, 16 It is important to provide the enterprise managers with high recommendation. The managers need to build new project teams and to find people who can solve problems. Therefore, a high-precision tool for finding ex- perts is needed. Moreover, not only managers need to find experts. In a highly collaborative environment where the willingness of sharing and helping other team members is present, all the employees should be able to find out to which colleague to ask for help in solving issues If we want to achieve high-quality results while searching for experts, con- sidering the user's desktop content makes the search much more focused on the user's interests also because the desktop dataset will contain much more exper- tise evidence(on such topics) than the rest of the public enterprise intranet Classic expert search systems 9, 30, 21, 25, 26, 17 work on the entire enterprise knowledge available. This means that they use shared repository, e-mails his- tory, forums, wikis, databases, personal home pages, and all the data that an enterprise creates and stores. This makes the system to consider a huge variety of topics, for example, from accountability to IT specific issues. Our soliFinding Experts on the Semantic Desktop Gianluca Demartini and Claudia Nieder´ee L3S Research Center Leibniz Universit¨at Hannover Appelstrasse 9a, 30167 Hannover, Germany {demartini,niederee}@L3S.de Abstract. Expert retrieval has attracted deep attention because of the huge economical impact it can have on enterprises. The classical dataset on which to perform this task is company intranet (i.e., personal pages, e-mails, documents). We propose a new system for finding experts in the user’s desktop content. Looking at private documents and e-mails of the user, the system builds expert profiles for all the people named in the desktop. This allows the search system to focus on the user’s topics of interest thus generating satisfactory results on topics well represented on the desktop. We show, with an artificial test collection, how the desk￾top content is appropriate for finding experts on the topic the user is interested in. 1 Introduction Finding people who are expert on certain topics is a search task which has been mainly investigated in the enterprise context. Especially in big enterprises, topic areas can range very much also because of diverse and distributed data sources. This peculiarity of enterprise datasets can highly affect the quality of the results of the expert finding task [15, 16]. It is important to provide the enterprise managers with high quality expert recommendation. The managers need to build new project teams and to find people who can solve problems. Therefore, a high-precision tool for finding ex￾perts is needed. Moreover, not only managers need to find experts. In a highly collaborative environment where the willingness of sharing and helping other team members is present, all the employees should be able to find out to which colleague to ask for help in solving issues. If we want to achieve high-quality results while searching for experts, con￾sidering the user’s desktop content makes the search much more focused on the user’s interests also because the desktop dataset will contain much more exper￾tise evidence (on such topics) than the rest of the public enterprise intranet. Classic expert search systems [9, 30, 21, 25, 26, 17] work on the entire enterprise knowledge available. This means that they use shared repository, e-mails his￾tory, forums, wikis, databases, personal home pages, and all the data that an enterprise creates and stores. This makes the system to consider a huge variety of topics, for example, from accountability to IT specific issues. Our solution