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Using AI in Knowledge Management: Knowledge Bases and Ontologies
May/June 1998 (vol. 13 no. 3)
pp. 34-39

Consulting and professional services firms are often among the first organizations to adopt a new technology. The reason is clear: they've got to know the technology before their clients do. Consequently, there appears to be a technology life cycle in consulting firms, where the firms first try the technology for internal use, before selling that same technology to their clients in the form of consulting engagements. We see this process at work with knowledge-management systems and their two main components: knowledge bases and ontologies.

Knowledge-management systems employ a wide range of knowledge bases, especially including best-practices knowledge bases. To use those knowledge bases effectively, the consulting firms must be able to generate ontologies that allow users to pinpoint what resources they need and want. Ontologies and knowledge bases are closely related in knowledge management. Ontologies define the knowledge base's characteristics and views, while also employing models that are helpful in knowledge-base definition and access.

This article looks at the way knowledge bases interact to form effective knowledge-management systems and in particular at the way leading consulting firms such as Price Waterhouse, Ernst & Young, and Arthur Andersen apply those systems to their businesses.

Citation:
Daniel E. O'Leary, "Using AI in Knowledge Management: Knowledge Bases and Ontologies," IEEE Intelligent Systems, vol. 13, no. 3, pp. 34-39, May-June 1998, doi:10.1109/5254.683180
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