Beyond Full-text Search: AI-based Technology to Support the Knowledge Cycle

David M. Steier, Scott B. Huffman, and Douglas I. Kalish

From the mounds of raw information available electronically today, what professionais really need are targeted, timely nuggets of knowledge that can guide the solution to business problems. Today’s common information tools -Web full-text search engines and the like - do not fully support this conversion of raw information into knowledge. In examining the common knowledge management problems faced by Price Waterhouse professionals, we have found that converting information to knowledge requires not only finding raw information, but also filtering through it for relevance, formatting it appropriately for the knowledge task at hand, and forwarding it to the right people. A fifth stage, feedback from the users, can allow the effectiveness of each stage to increase with time. In this paper, we describe each stage of this knowledge cycle and discuss the potential role that AI-based technology can play in its automation. We illustrate the possibilities through case studies of deployed knowledge management tools we have built at Price Waterhouse. These tools demonstrate that for targeted business tasks, AI-based technology can potentially facilitate much of the knowledge cycle, providing users with useful business knowledge that provides competitive advantage.

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