A personal knowledge advantage machine for knowledge workers in data-intensive domains

Abstract

With the ever-increasing amount of data and associated knowledge required to perform many modern-day tasks, quickly finding context-sensitive information can contribute to significant gains in knowledge worker productivity. Here, we describe the design and construction of a system, called a Personal Knowledge Advantage Machine (pKaM), to assist users while they perform knowledge based tasks. In this paper, we illustrate this idea by using the computer programming domain, which undoubtedly is a knowledge-intensive task, as an example. We first describe the architecture of a pKaM followed by a description of how it may be used by a programmer new to the Python language. The pKaM system may be viewed as a collection of agents that discover, mark-up, organize, display and present contextually relevant pieces of knowledge to the worker during the entire life-cycle of the task. The design of pKaM emphasizes the use of the plug-and-play approach so that it can be adapted to any domain by merely plugging-in a new domain knowledge base. As this idea gains currency, it is our hope that open-source knowledge bases organized along the lines described here will become available for many domains.