Finding experts on the semantic desktop

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 desktop content is appropriate for finding experts on the topic the user is interested in.

[1]  Leif AzzopardiKrisztian.  Language Modeling Approaches for Enterprise Tasks , 2005 .

[2]  Djoerd Hiemstra,et al.  Modeling Documents as Mixtures of Persons for Expert Finding , 2008, ECIR.

[3]  Maarten de Rijke,et al.  Finding experts and their eetails in e-mail corpora , 2006, WWW '06.

[4]  Claudia Niederée,et al.  Probabilistic Entity Linkage for Heterogeneous Information Spaces , 2008, CAiSE.

[5]  Maarten de Rijke,et al.  Searching for People in the Personal Work Space , 2006 .

[6]  Nick Craswell,et al.  Overview of the TREC 2006 Enterprise Track , 2006, TREC.

[7]  Craig MacDonald,et al.  Using Relevance Feedback in Expert Search , 2007, ECIR.

[8]  Anne-Marie Vercoustre,et al.  Enterprise PeopleFinder: Combining Evidences from Web Pages and Corporate Data , 2003 .

[9]  Wolfgang Nejdl,et al.  Semantically Enhanced Searching and Ranking on the Desktop , 2005, Semantic Desktop Workshop.

[10]  William W. Cohen,et al.  Learning to Extract Signature and Reply Lines from Email , 2004, CEAS.

[11]  Anne-Marie Vercoustre,et al.  Combining Structured Corporate Data and Document Content to Improve Expertise Finding , 2005, ArXiv.

[12]  M. de Rijke,et al.  Formal models for expert finding in enterprise corpora , 2006, SIGIR.

[13]  ChengXiang Zhai,et al.  Probabilistic Models for Expert Finding , 2007, ECIR.

[14]  Craig MacDonald,et al.  Voting for candidates: adapting data fusion techniques for an expert search task , 2006, CIKM '06.

[15]  Nick Craswell,et al.  Overview of the TREC 2005 Enterprise Track , 2005, TREC.

[16]  Siegfried Handschuh,et al.  The NEPOMUK Project - On the way to the Social Semantic Desktop , 2007 .

[17]  Wolfgang Nejdl,et al.  The Beagle++ Toolbox: Towards an Extendable Desktop Search Architecture , 2006, SemDesk.

[18]  Leo Sauermann,et al.  The Sesame LuceneSail : RDF Queries with Full-text Search NEPOMUK Technical Report 2008-1 , 2008 .

[19]  Pradeep Ravikumar,et al.  A Comparison of String Distance Metrics for Name-Matching Tasks , 2003, IIWeb.

[20]  Enrico Motta,et al.  Mining Web data for competency management , 2005, The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05).

[21]  Wolfgang Nejdl,et al.  Evaluating Personal Information Management Using an Activity Logs Enriched Desktop Dataset , 2008 .

[22]  Jie Li,et al.  Expert Finding for eCollaboration Using FOAF with RuleML Rules , 2006 .

[23]  Wolfgang Nejdl,et al.  Building a Desktop Search Test-Bed , 2007, ECIR.

[24]  Mark S. Ackerman,et al.  Expertise networks in online communities: structure and algorithms , 2007, WWW '07.

[25]  David Hawking,et al.  Panoptic Expert: Searching for experts not just for documents , 2001 .

[26]  Sergey Brin,et al.  The Anatomy of a Large-Scale Hypertextual Web Search Engine , 1998, Comput. Networks.

[27]  Maarten de Rijke,et al.  Associating People and Documents , 2008, ECIR.

[28]  Craig MacDonald,et al.  High Quality Expertise Evidence for Expert Search , 2008, ECIR.

[29]  M. de Rijke,et al.  Determining Expert Profiles (With an Application to Expert Finding) , 2007, IJCAI.

[30]  Paul P. Maglio,et al.  Expertise identification using email communications , 2003, CIKM '03.