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
Modelling contextualized textual knowledge as a Long-Term Working Memory / M., Mazzieri; S., Topi; Dragoni, Aldo Franco; G., Vallesi. - (2010), pp. 53-58. (Intervento presentato al convegno ESANN 2010. 18th European Symposium On Artificial Neural Networks, Computational Intelligence and Machine Learning tenutosi a BRUGES, BELGIUM nel APRIL 28-29, 2010).
Modelling contextualized textual knowledge as a Long-Term Working Memory
DRAGONI, Aldo Franco;
2010-01-01
Abstract
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 relatednessI documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.