Abstract:
With the daily increase in document flow,
as well as the transition to paperless document
management around the world, the demand
for electronic document management systems
is increasing. This significantly requires
optimization of these systems in terms of quality
document information retrieval and document
management. However, research based on
statistical methods cannot effectively handle
large amounts of data extracted from electronic
documents. In this regard, machine learning
methods can effectively solve this problem. This
paper presents an approach to building a model
of an intelligent document management system
using machine learning techniques to ensure
efficient employee performance in organizations.
The authors have solved a number of problems
to optimize each of the document management
subsystems, resulting in the development of an
intelligent document management system model,
which can be effectively applied to enterprises,
government and corporate institutions. The
feasibility and effectiveness of the proposed
model of intelligent document management
system based on machine learning and multiagent modeling of information retrieval processes
provides maximum reliability and reduced time
of work on documents. The obtained results
show that with the help of the presented model
it is possible to further develop an intelligent
document management system that will allow an
electronic document to qualitatively go through
the whole life cycle of a document, starting
from the moment of document registration and
finishing with its closing, i.e. execution, which
will greatly facilitate the daily work of users with
large volumes of documents. At the same time, the
paper considers the application of topic modeling
methods and algorithms of text analysis based
on a multi-agent approach, which can be used
to build an intelligent document management
system
