Modeling contextualized textual knowledge as a Long-Term Working Memory

A knowledge management system is more than an archive of textual documents; it provides context information, allowing to know which documents where used by people with a common goal. In the hypothesis that a set of textual documents with a common context can be assimilated to the long term memory of a human expert executor, we can use on them mining techniques inspired by the mechanic of human comprehension in expert domains. Text mining techniques for KM task can use a model of the long-term memory to extract meaningful keywords from the documents. The model acts as a dynamic and non-stationary dimensionality reduction strategy, allowing the clustering of context documents according to keyword presence, the classification of external documents according to local criteria, and a better understanding of document content and relatedness.