The paper proposes to review the process of foresight with the presence of
semistructured data as a whole, gradually reducing uncertainty, moving from the start of
the study to the desired future. To implement the proposed concept, a systematic approach
to the support of the foresight process based on textual analytics, which is the most
modern and most powerful tool for the analysis of semistructured data written in natural
language.
The system approach consists of four stages which are continuously repeated
throughout the life cycle of foresight, and its results are reused in all other foresight
sessions. In the first stage, the subject area is studied, the features to the desired future are
analysed, the models, methods and their metadata are determined. The conceptual model
of support of the foresight process is determined. An idea of the process of foresight and
the horizon of foresight is formed. Factors of growth and reduction of uncertainty on the
way to the forecast horizon are determined. An information model of the foresight process
is introduced - the representation of subject areas using the set-theoretic concept of general
systems theory. Restrictions on information model connections are introduced, options for
presenting knowledge in the form of a hierarchical classifier or ontology are considered,
and advantages and disadvantages are outlined. The concept of the existence of knowledge
in time is considered. Integrated time-dependent awareness indicators have been
introduced to measure changes in the knowledge base over time and / or depending on the
amount of new knowledge. New knowledge is registered as classified metadata according
to developed classifiers. Awareness indicators are constantly calculated and analyzed
during the foresight process.
At the second stage of the system approach the model and approach of extraction of
knowledge from texts in natural language is introduced and applied. The work modifies
the general model of extracting facts from texts in natural language to meet the
requirements of extracting metadata information model of foresight, introducing universal
lexical templates-restrictions to compile more powerful rules for extracting metadata.
At the third stage of the system approach the information model of support of the
foresight process is introduced, classes of input data are defined.
At the fourth stage of the system approach, the semistructured data processing
modules are adapted and scaled as a part of the foresight process support system. A
number of cases show the application of a systematic approach to support the foresight
process with the presence of semistructured data using textual analytics.
The developed system approach is applied throughout the life cycle of the foresight
session. Artifacts created at the end of the support process (classifiers, lexical restrictions,
rules, knowledge) can be used in subsequent and new foresight sessions.
Introduced system approach reduces the resources to provide data in the internal
subprocesses of the system and improves the quality of processes, including: speeds up the
processing of input data about foresight process, provides analysts and experts with tools
for rapid analysis of input data, information on the progress in the form of awareness
indicators, provides reuse of acquired knowledge and artifacts at the output of models,
algorithms and approaches in subsequent foresight sessions. Number of practical cases
confirmed the effectiveness, efficiency, scale of the proposed concept, saving the integrity
of the foresight process, during the involvement of the proposed system approach.
Keywords: systems analysis, foresight methodology, text analytics, natural
language processing, data mining, foresight process support, sentiment analysis, foresight
awareness indicators, information model, conceptual model, model of knowledge
extraction from texts in natural language, classifiers, synthesis of classification rules,
foresight process metadata.
