5 CONCLUSIONS
Knowledge based management of administration
and of business provides for enhanced competitive
advantage and quality of services and business
process flow. Practically knowledge doesn’t exist in
explicit form. It’s hidden within data pools of
different format and volume. The task of uncovering
it poses significant challenge to information
technologies and business modelling. Current paper
contributes to the design and architecture of
knowledge generation system with issues concerning
data modelling and implementation for knowledge
discovery. General model for mining knowledge
from digital text stores is presented. The model
framework involves structure model and knowledge
models. The steps for designing them implement
text and data mining techniques. Basic architecture
and algorithms for performing descriptive
(clustering) and predictive (classification) modelling
tasks are presented. The knowledge generation
model is trained on test document corpus for “good”
practices for administration management. The
models are established in WEKA and produce
knowledge results for the clustering and
categorisation tasks. Future work is intended in
extracting associations between terms and
implementation of ontologies.
ACKNOWLEDGEMENTS
The paper presents results of the project “Research
and Education Centre for e-Governance” funded by
the Ministry of Education in Bulgaria.
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