The role of artificial intelligence technologies in the implementation of People-Finder knowledge management systems

The development of Knowledge Management Systems (KMS) demands that knowledge be obtained, shared, and regulated by individuals and knowledge-sharing organizational systems such as Knowledge Repositories. One kind of Knowledge Repository, known as Knowledge Yellow Pages or People-Finder Systems, are repositories that attempt to manage knowledge by pointing to experts possessing specific knowledge within an organization. This paper presents the insights, challenges and future plans for the development of two People-Finder KMS: the Searchable Answer Generating Environment (SAGE), and the Expert Seeker. Here we also discuss the role that Artificial Intelligence technologies play in the development of PeopleFinder KMS and in automating the profile-maintenance. Introduction to Knowledge Management Systems Knowledge Management Systems (KMS) have been defined as “an emerging line of systems [which] target professional and managerial activities by focusing on creating, gathering, organizing, and disseminating an organization’s ‘knowledge’ as opposed to ‘information’ or ‘data’” (Alavi and Leidner 1999). It has been observed that KMS currently underway at most organizations fall into three categories (Becerra-Fernandez 1999a): 1. Educational KMS: To elicit and catalog tacit knowledge, and at the same time serve as an educational tool. 2. Problem-Solving KMS: Organizations with significant intellectual capital require eliciting and capturing knowledge for reuse in solving new problems as well as recurring old problems. 3. Knowledge Repositories: The majority of the KMS in place. One kind of Knowledge Repository is known as Knowledge Yellow Pages or People-Finder Systems, are repositories that attempt to manage knowledge by holding pointers to experts who possess specific knowledge within an organization. The paper presents insights from the development of two examples of such People-Finder KMS: the Searchable Answer Generating Environment (SAGE), and the Expert Seeker. This paper discusses insights and lessons learned from the development of these two systems. Finally, it presents the role of technology in automating the process of profile-maintenance, as well as future plans for the integration of Artificial Intelligence technologies in the development of People-Finder KMS. The Searchable Answer Generating Environment (SAGE) KMS The NASA/Florida Minority Institution Entrepreneurial Partnership (FMIEP) grant is funding the development of the Searchable Answer Generated Environment (SAGE), which is in the category of People-Finder KMS (BecerraFernandez 1999b). The purpose of this KM System is to create a repository of experts in the State of Florida (FL) State University System (SUS). Previous studies have pointed out that there is a void in the ability to identify the capabilities in the FL SUS (Kotnour 1998). Currently, each State University in Florida keeps a database of funded research, but these databases are disparate and dissimilar. The SAGE KM System creates a single repository by incorporating a distributed database scheme, which can be searched by a variety of fields, including research topic, investigator name, funding agency or university. As NASA-Kennedy Space Center (KSC) looks to develop new technologies necessary for the continuation of their space exploration missions, their need to partner with Florida SUS experts becomes evident. The main interfaces developed on the query engine use text fields to search the processed data for key words, fields of expertise, names, or other applicable search fields. The application processes the end user's query and returns the pertinent information. The purpose of the SAGE KMS is to unify myriad data collections into one database collection that could easily be mined for relevant data. The benefits of SAGE are: 1. SAGE is a repository of Intellectual Capital within the state of FL SUS. 2. SAGE helps locate FL SUS researchers for collaboration with industry and federal agencies, thus increasing the potential for research funding to the SUS. 3. SAGE enhances communication and allows more visibility for FL SUS experts, making universities more marketable. 4. SAGE combines and unifies existing data from multiple sources into one user web-accessible interface. The SAGE system addresses an important KM problem: From: AAAI Technical Report SS-00-03. Compilation copyright © 2000, AAAI (www.aaai.org). All rights reserved.