PeopleFinder: Searching for People, not just for Documents Technologies for Knowledge Sharing ICT-Centre CSIRO Alistair McLean, Anne-Marie Vercoustre, Mingfang Wu To find out experts in a company, on a given subject, and to be able to contact them. To provide evidence that they are appropriate experts. Goal Search Expert Details + Evidence Information Need A simple process of Web search Although documents are a valuable resource, for many questions it is necessary to find the right person rather than the right document.

Example: Searching for Experts in Virtual Environments More evidences Home page Corporate structure Ranked experts for “Virtual Environments”

Benefits People Finder provides automatic and rapid identification of experts: Without manual maintenance of employee expertise list Without any specialised user training With contact details and place in the corporate organisation chart With supporting evidence so the degree of expertise can be judged Decisions are based on the documents that staff load onto the corporate Intranet and projects they are involved in; this automatically tracks expertise as staff develop. Approach People Finder leverages on structured and unstructured information: Unstructured information: Intranet and extranet documents Structured information: Corporate data (groups, projects description and memberships) Expertise evidence is based on: Documents that contain people’ names Documents that are linked to project pages (not too far down) weight on Home pages, project pages and related pages (weight decreases with distance)

Evaluation Test Collection (April 2003): - CSIRO-CMIS Intranet/Extranet (html) - List of staff, Home Pages - Groups, Projects, Project descriptions (XML) - Publications (XML) - Business development contact database (mails) Average precision for base system without structured data and new system over the Web collection Comparison between two collections (with and without XML documents) by using the new system. Leveraging on structure increases significantly the precision Adding more structured documents does not always increase precision Preliminary Finding Base New (%) (93.8)(68.4)(61.6)(37.6) P< (3.2E-5)(5.3E-6)(4.6E-7)(5.7E-5) Web Web + XML data (1.7%)(6.3%)(6.2%)(20.3%) P< (0.84)(0.31)(0.23)(0.0003)