Providing knowledge workers with access to experts and communities-of-practice is central to sharing expertise and crucial to organizational performance, adaptation, and even survival. This paper covers ongoing research to develop an Expert Locator prototype, a model-based system for detecting experts and broader communities-of-practice. The underlying expertise model is extensible and supports aggregation of evidence across diverse sources. The prototype is being used to locate critical expertise in key project areas, and current evaluation indicates its potential effectiveness.

This article describes automated tools for increasing organizational awareness within a global enterprise. The MITRE Corporation is the context for this work; however, the tools and techniques are general and should apply to a wide variety of distributed, heterogeneous organizations. These tools provide awareness of team members and materials in virtual collaboration environments as well as support for automated discovery of distributed experts. The results are embodied in 3 systems: MITRE's Collaborative Virtual Workspace (CVW), Expert Finder, and XpertNet. CVW is a place-based collaboration environment that enables team members to find one another and work together. Expert Finder is an expert skill finder that exploits the intellectual products created within an organization to support automated expertise identification. XpertNet addresses the problem of detecting extant or emerging classes of expertise without a priori knowledge of their existence. Both Expert Finder and XpertNet combine to detect and track experts and expert communities within a complex work environment. After describing the background of knowledge management at MITRE, this article describes the architecture and use of collaboration and expert finder systems to enhance organizational awareness, provides some principles of expertise, and concludes with an outline of future research directions.

Indexing according to occurrences of selected word fragments, called “n-grams”, offers a significant alternative to keyword indexing and full text scanning methods in the design of information systems based on documents. Finite sets of n-grams can be selected to allow effective fixed indexing of all words, numbers, and special terms in text. The characteristics of such indexing can be modeled statistically and validated over a wide range of text. The model provides a descriptive and predictive tool for controlling precision and recall in searching and for scaling estimates of relevance to an adaptive reference noise distribution for a target collection. Special techniques such as partial inversion of index terms, probabilistic ordering of index terms, and various types of data compression allow n-gram indexing to be competitive in performance with other approaches.