document: Knowledge base maintenance using knowledge gap analysis As the web and e-business have proliferated , the practice of using customer facing knowledge bases to augment customer service and support operations has increased . This can be a very efficient , scalable and cost effective way to share knowledge . The effectiveness and cost savings are proportional to the utility of the information within the knowledge base and inversely proportional to the amount of labor required in maintaining the knowledge . To address this issue , we have developed an algorithm and methodology to increase the utility of the information within a knowledge base while greatly reducing the labor required . In this paper , we describe an implementation of an algorithm and methodology for comparing a knowledge base to a set of problem tickets to determine which categories and subcategories are not well addressed within the knowledge base . We utilize text clustering on problem ticket text to determine a set of problem categories . We then compare each knowledge base solution document to each problem category centroid using a cosine distance metric . The distance between the `` closest '' solution document and the corresponding centroid becomes the basis of that problem category 's `` knowledge gap '' . Our claim is that this gap metric serves as a useful method for quickly and automatically determining which problem categories have no relevant solutions in a knowledge base . We have implemented our approach , and we present the results of performing a knowledge gap analysis on a set of support center problem tickets .
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