  The development of a company often entails the emergence of autonomous data
sources with different structural and technological organization. This can lead
to the inability of data analysis at a high level and a violation of the
integrity and reliability of data within the organization, hindering the
adoption of high-quality decisions and further development of the company. This
problem can be solved by implementing a higher abstraction, representing
heterogeneous organization data in a single space by combining them into a
single knowledge graph. We propose a framework capable of autonomous
construction of an organization's knowledge graph based on semi-structured data
from various sources by finding links between sources based on data with an
arbitrary structure, and combining document collections into single entities.
The results of tests show the applicability of the developed approach for
constructing a knowledge graph based on partially-structured data from various
sources and the high efficiency of the approach based on the metrics of
completeness of data storage subsystems coverage (11 out of 11) and filtering
false connections (there are only 2.5 connections of collections with neighbors
on average in the final graph).
