Abstract
In order to process huge amount of electronic documents in an organized manner, automatic document categorization is an important research area in the area of explicit knowledge management. In this paper, we propose a new document classification methodology based on neural network technology. We first extract key phrases from the document set by means of text processing and determine the significance of key phrases by frequency. After significant terms are extracted, a keyword correlation analysis model is applied to compute similarity between terms. Then, synonyms are reduced to higher similarity terms. We adopt the back-propagation network model as a classifier. The target output is to identify a document’s proper category based on a hierarchical document classification scheme. In this research, patents related to designs of power hand tools are studied in their IPC classification scheme. Any hand tool patent can be automatically and accurately classified using the pre-trained neural network models. In the prototype system we provide two modules for explicit knowledge management. The automatic classification module helps the user classify patent documents and the search module helps the user identify the correct patent document quickly. The result shows a very significant improvement in document classification and identification in explicit knowledge management.