<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Boster, James S.</style></author><author><style face="normal" font="default" size="100%">Lutters, Wayne G.</style></author><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">Pipek, Volkmar</style></author><author><style face="normal" font="default" size="100%">Wulf, Volker</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Who's There?  The Knowledge Mapping Approximation Project</style></title><secondary-title><style face="normal" font="default" size="100%">Sharing Expertise: Beyond Knowledge Management</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2002</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.eecs.umich.edu/~ackerm/pub/02e04/sharing-expertise.chapter.final.pdf</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">MIT Press</style></publisher><pub-location><style face="normal" font="default" size="100%">Cambridge, MA</style></pub-location><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;em&gt;&lt;strong&gt;Expertise finders&lt;/strong&gt;&lt;/em&gt;, or expertise recommenders, are a form of recommendation system.&amp;nbsp;&amp;nbsp;For these expertise finder systems to be of significant assistance, however, they must effectively point at the relevant people for any given problem.&amp;nbsp;&lt;/p&gt;
&lt;p&gt;Trying to provide these data is directly analogous to the well-established problem of knowledge elicitation for the development of expert systems.&amp;nbsp;We call our problem &lt;em&gt;&lt;strong&gt;expertise mapping&lt;/strong&gt;&lt;/em&gt;, and the required effort to be expertise or knowledge elicitation.&amp;nbsp;In expertise mapping, one needs to inventory the organization&amp;rsquo;s knowledge as well as to map the information flow within the organization.&amp;nbsp;&lt;/p&gt;
&lt;p&gt;In this chapter, we report on the &lt;strong&gt;&lt;em&gt;Knowledge Mapping Approximation (KMA) Project&lt;/em&gt;&lt;/strong&gt;, which concentrates on this&amp;nbsp; providing systems with the type of data needed to adequately determine the people most likely to be able to answer a given question. While it is relatively common to consider system prototypes to find others, less research has been pointed towards finding adequate data for these expertise finders.&amp;nbsp;We focus here only on the first steps in finding adequate data; this chapter discusses the initial steps in the KMA Project.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Collaborative Support for Informal Information in Collective Memory Systems</style></title><secondary-title><style face="normal" font="default" size="100%">Information Systems Frontiers</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Answer Garden 2 system</style></keyword><keyword><style  face="normal" font="default" size="100%">collective help applications</style></keyword><keyword><style  face="normal" font="default" size="100%">information access</style></keyword><keyword><style  face="normal" font="default" size="100%">organizational memory</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><volume><style face="normal" font="default" size="100%">2</style></volume><pages><style face="normal" font="default" size="100%">333-347</style></pages><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;span style=&quot;font-size: 10pt; layout-grid-mode: line&quot;&gt;Informal information, such as the expertise of an organization or the workarounds practiced by a community, is a critical part of organizational or collective memory systems.&amp;nbsp;From a user-centered perspective, a user merely wishes to get his work done, and to do this, he must solve his immediate problems.&amp;nbsp;We have examined how to incorporate this problem solving into a collective memory, as well as how to incorporate the learning that accrues to it or from it.&amp;nbsp;We report here on two systems, the Cafe ConstructionKit and the Collaborative Refinery, as well as an application, Answer Garden 2, built using these two systems.&amp;nbsp;The Cafe ConstructionKit provides toolkit mechanisms for incorporating communication flows among people (as well as agents) into an organizational memory framework, and the Collaborative Refinery system provides mechanisms for distilling and refining the informal information obtained through these communication flows.&amp;nbsp;The Answer Garden 2 application demonstrates the utility of these two underlying systems.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3-4</style></issue></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Expertise Recommender:  A Flexible Recommendation System Architecture</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Conference on Computer Supported Cooperative Work (CSCW'2000)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ER</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">Expertise Recommender</style></keyword><keyword><style  face="normal" font="default" size="100%">organizational memory</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.eecs.umich.edu/~ackerm/pub/00b30/cscw00.er.pdf </style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">231-240</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;div style=&quot;margin: 0in 0in 4pt&quot;&gt;&lt;font size=&quot;2&quot;&gt;Locating the expertise necessary to solve difficult problems is a nuanced social and collaborative problem. In organizations, some people assist others in locating expertise by making referrals. People who make referrals fill key organizational roles that have been identified by CSCW and affiliated research. Expertise locating systems are not designed to replace people who fill these key organizational roles. Instead, expertise locating systems attempt to decrease workload and support people who have no other options. Recommendation systems are collaborative software that can be applied to expertise locating. This work describes a general recommendation architecture that is grounded in a field study of expertise locating. Our expertise recommendation system details the work necessary to fit expertise recommendation to a work setting. The architecture and implementation begin to tease apart the technical aspects of providing good recommendations from social and collaborative concerns.&lt;/font&gt;&lt;/div&gt;</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>27</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Supporting Nuance in Groupware Design: Moving from Naturalistic Expertise Location to Expertise Recommendation</style></title><secondary-title><style face="normal" font="default" size="100%">University of California, Irvine. Ph.D. thesis</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ER</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">Expertise Recommender system</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2000</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://projects.ischool.washington.edu/mcdonald/papers/McDonald.Dissertation.final.pdf</style></url></web-urls></urls><abstract><style face="normal" font="default" size="100%">No abstract available.  You should contact David McDonald at dwmc_at_u.washington.edu for more information.
 </style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Just Talk to Me:  A Field Study of Expertise Location</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Conference on Computer Supported Cooperative Work (CSCW '98)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">cscw</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise finding</style></keyword><keyword><style  face="normal" font="default" size="100%">expertise location</style></keyword><keyword><style  face="normal" font="default" size="100%">organizational memory</style></keyword><keyword><style  face="normal" font="default" size="100%">recommenders</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.eecs.umich.edu/~ackerm/pub/98b25/cscw98.expertise.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">315-324</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Everyday, people in organizations must solve their problems
to get their work accomplished. To do so, they often
must find others with knowledge and information. Systems
that assist users with finding such expertise are increasingly
interesting to organizations and scientific communities.
But, as we begin to design and construct such systems, it is
important to determine what we are attempting to augment.
Accordingly, we conducted a five-month field study of a
medium-sized software firm. We found the participants use
complex, iterative behaviors to minimize the number of
possible expertise sources, while at the same time, provide
a high possibility of garnering the necessary expertise. We
briefly consider the design implications of the identification,
selection, and escalation behaviors found during our
field study.&lt;/p&gt;</style></abstract></record><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mark S. Ackerman</style></author><author><style face="normal" font="default" size="100%">McDonald, David W.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Answer Garden 2:  Merging Organizational Memory with Collective Help</style></title><secondary-title><style face="normal" font="default" size="100%">ACM Conference on Computer-Supported Cooperative Work (CSCW'96)</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Cafe ConstructionKit system</style></keyword><keyword><style  face="normal" font="default" size="100%">Collaborative Refinery system</style></keyword><keyword><style  face="normal" font="default" size="100%">help</style></keyword><keyword><style  face="normal" font="default" size="100%">information access</style></keyword><keyword><style  face="normal" font="default" size="100%">information distillation</style></keyword><keyword><style  face="normal" font="default" size="100%">organizational memory</style></keyword><keyword><style  face="normal" font="default" size="100%">Q&amp;A</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1996</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.eecs.umich.edu/~ackerm/pub/96b22/cscw96.ag2.pdf</style></url></web-urls></urls><pages><style face="normal" font="default" size="100%">97-105</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;This research examines a collaborative solution to a
common problem, that of providing help to distributed
users. The Answer Garden 2 system provides a secondgeneration
architecture for organizational and community
memory applications. After describing the need for Answer
Garden 2â€™s functionality, we describe the architecture of the
system and two underlying systems, the Cafe
ConstructionKit and Collaborative Refinery. We also
present detailed descriptions of the collaborative help and
collaborative refining facilities in the Answer Garden 2
system.&lt;/p&gt;</style></abstract></record></records></xml>
