Open AccessForesight of promising scientific fields in scientific social networks
Author(s) -
Yulia Orlova,
С. В. Проничкин,
Igor Mamai,
A V Kholstov
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2060/1/012007
Subject(s) - structuring , computer science , sorting , graph , traverse , data science , process (computing) , futures studies , network science , scientific literature , management science , artificial intelligence , theoretical computer science , geography , engineering , complex network , world wide web , paleontology , geodesy , biology , programming language , operating system , finance , economics
Currently, the problem of developing methods for determining promising directions, trends in scientific research and technology is the most important component of the process of forming a national science policy. The problem of finding priority or breakthrough areas of development is reduced to sorting out in one form or another and assessing possible existing areas of scientific activity. It is proposed to use a semantic network of communicative information space, in which the structuring of the subject area is based on the selection of objects and identifying features, which include not only features-properties, but also features-relationships. A module for traversing elements of a scientific social network by reference graph has been implemented, as well as an algorithm for determining the level of importance of a scientific direction according to multiple criteria.