Expert-Finding Systems for Organizations: Problem and Domain Analysis and the DEMOIR Approach

Abstract

Computer systems that augment the process of finding the right expert for a given problem in an organization or world-wide are becoming feasible more than ever before, thanks to the prevalence of corporate Intranets and the Internet. This paper investigates such systems in two parts. We first explore the expert finding problem in depth, review and analyze existing systems in this domain, and suggest a domain model that can serve as a framework for design and development decisions. Based on our analyses of the problem and solution spaces, we then bring to light the gaps that remain to be addressed. Finally, we present our approach called DEMOIR, which is a modular architecture for expert finding systems that is based on a centralized expertise modeling server while also incorporating decentralized components for expertise information gathering and exploitation.

The OU Linked Open Data: Production and Consumption

“…Furthermore, the University of Manchester's library catalogue records can now be accessed in RDF format 6 . In addition, other universities are currently working on transforming and linking their data: University of Bristol, 7 Edinburgh (e.g., the university's buildings information is now generated in LOD 8 ), and Oxford 9 . Furthermore the University of Muenster announced a funded project, LODUM, the aim of which is to release the university's research information as Linked Data.…”

“…As discussed by Yimam-Seid and Kobsa [7], developing and manually updating an expert system database is time consuming and hard to maintain. However, valuable information can be identified from documents generated within an organisation [8].…”

The design and evaluation of EKE, a semi-automated email knowledge extraction tool

“…To help fulfil this goal, expert finders require a range of information relating to individuals and their expertise (Yimam-Seid and Kobsa, 2003;Sim and Crowder, 2004). These include:…”

“…The second element is dependent on whether personal web pages are regularly updated by the experts to reflect changes in their skills. Yimam-Seid and Kobsa (2003) question the adequacy of utilising search engines to trace experts, in light of having a large number of hits returned. Such a procedure would entail traversing the results returned, selecting the most appropriate expert, and determining their accessibility; hence it is greatly time consuming.…”

The Role of Information Technology in Building and Sustaining the Relational Base of Communities

“…Several recommender systems have been designed to support the identification of human actors (cf. Yiman-Seid & Kobsa, 2003). Systems like Who Knows (Streeter & Lochman, 1988), the Referral Web (Kautz et al, 1997a(Kautz et al, , 1997b, Yenta (Foner, 1997), MII Expert Finder, and XperNet (Maybury et al, 2002) extract personal data about human interests automatically from documents created by the actors.…”