Abstract

KM (Knowledge Management) systems have recently been adopted within the realm of enterprise management. On the other hand, data mining technology is widely acknowledged within Information systems' R&D Divisions. Specially, acquisition of meaningful information from Web usage data has become one of the most exciting eras. In this paper, we employ a Web based KM system and propose a framework for applying Web Usage Mining technology to KM data. As it turns out, task duration varies according to different user operations such as referencing a table-of-contents page, down-loading a target file, and writing to a bulletin board. This in turn makes it possible to easily predict the purpose of the user's task. By taking these observations into account, we segmented access log data manually. These results were compared with results abstained by applying the constant interval method. Next, we obtained a segmentation rule of Web access logs by applying a machine-learning algorithm to manually segmented access logs as training data. Then, the newly obtained segmentation rule was compared with other known methods including the time interval method by evaluating their segmentation results in terms of recall and precision rates and it was shown that our rule attained the best results in both measures. Furthermore, the segmented data were fed to an association rule miner and the obtained association rules were utilized to modify the Web structure.