Vol 13, No 2 (2023)

In modern deductive analysis, the main tasks include the following: finding proof of a given statement using axioms and rules of inference and checking the correctness of a given consequence from certain premises. Little is currently known about inference problems with predetermined properties (problems with interesting consequences). There are no clear answers to the following questions: what properties are inherent in an interesting consequence and how to calculate an interesting consequence? To solve these problems, it is proposed to use the methods of n-tuple algebra (NTA) based on properties of the Cartesian product of sets. The objects of NTA are arbitrary n-ary relations, which can be interpreted as the formulas of mathematical logic. They are matrix-like structures which cells do not contain elements, but subsets of the corresponding attributes. In NTA, operations (addition, generalized intersection and generalized union) in the tuple algebra correspond to logical connectives of mathematical logic (negation, conjunction, disjunction), and the generalized inclusion relation corresponds to the derivability relation. The calculation of quantifier operations is performed using operations on attributes (adding a dummy attribute, which corresponds to the generalization rule in predicate calculus, and eliminating an attribute). For two of the four types of NTA structures, the elimination of attributes corresponds to computing the projection of a relation. To derive interesting consequences in NTA, a structure called the minimal consequence is proposed, which is equal to the generalized intersection of premises. Interesting consequences are calculated as projections of the minimal consequence. As a result of calculations and checks, consequences are obtained with a reduced or a given composition of variables, as well as with a reduced amount of notation.

The paper describes a generalized ontology that makes it possible to form knowledge about various types of treatment (medicamental, restorative, surgical), regardless of the branch of medicine. Ontology is a necessary component for creating an information system focused on solving a class of treatment planning problems. Planning allows you to combine treatments for various diseases and branches of medicine. A method for adapting a generalized ontology to various types of treatment and terminology, which is familiar to the developer of the knowledge base, an expert in the subject area, is described. All specialized ontologies for specific types of treatment are inherited from the generalized ontology, preserving its structure and semantic dependencies. This approach ensures the creation of knowledge bases in decision support systems for the complex prescription of patient treatment, opening up the possibility of creating a single solver, which provides a significant reduction in system development costs. The generalized ontology was developed on the IACPaaS cloud platform and is currently used to create knowledge bases in various fields of medicine.

The growth of the world population forces a more outstanding and more efficient food production, forcing agribusiness into the race for greater productivity. Thus, agrochemicals as a tool for increasing and defending production have become more critical with each harvest. This work presents an ontology that describes the knowledge involved in the need for Agrochemical Pervasive Traceability Model (APTM). This proposed ontology is called ontology for pervasive traceability of agrochemicals (OntoPTA). We present classes and their relationships in a hierarchical way and a visualization from the OWL ontology language. This ontology fills the gap in the understanding and modeling of this type of agribusiness process. This modeling helps farm administrators and software developers to perform better analysis for the development, use and maintenance of systems in agribusiness. 

The possibility of using graph models in project management to solve the problem of forming a project team and distributing project work among potential performers is investigated. The possible areas of application of ontological modeling in project management are highlighted. The ontology of the project is implemented by means of the Protégé ontology editor. The possibility of constructing vector representations of elements of simple graphs and knowledge graphs in the problem of forming a project team is investigated. The main objectives of the research are: the construction of graph models of projects, a simple graph and a knowledge graph; the use of vector representations of vertices and relationships of graph models to solve the problem of distributing project work. The possibility of applying semantic proximity measures in ontology is analyzed. The novelty of the proposed approach lies in the use of data on participants in completed projects when forming a project team, which increases the chance of successful project implementation. The representation of graph elements in the form of numerical vectors makes it possible to apply machine learning methods to the study of the subject area. An example of solving the problem of forming a project working group is given, in which the executors of the project work are selected. All calculations were carried out using the Python language.

The article analyzes the problem of multi-level management of the resources of large enterprises and the associated limitations of the cascade approach in modern automated control systems. It is shown that the cascade approach leads to delays in management processes, an increase in management complexity and significant discrepancies between plans at different management levels, missed deadlines and exceeding order budgets. To solve the problem, a new distributed approach is proposed, which involves the design of autonomous intelligent systems for resource management of units that form strategic plans and run operational management; network interfaced interaction of these systems to coordinate and synchronize plans for events coming in real time. A method for designing distributed autonomous systems for managing enterprise resources has been developed. The possibility of implementing the approach within a digital ecosystem based on a network-centric platform with vertical and horizontal interactions of these systems is shown. The results of the development and implementation of the approach at industrial enterprises for the design, production and operation of high-tech products that require separate order planning and accounting are presented. The prospects for the approach development of the formation of digital twins of industry chains of enterprises cooperation are outlined.

The analysis of the requirements for vacancies in the labor market shows that they are multi-level language constructions of several words with complex semantic relationships. The aim of the research is to develop a method for extracting short texts of knowledge and skills from texts of job requirements that have a complex organizational structure. The method consists in supplementing the structure of complex sentences with new relationships by means of a BERT neural network model trained on the texts of online vacancies and moving from a complex text to a set of simple word combinations. The process of additional training (finetunig) of BERT neural network models from the Sberbank AI laboratory on the texts of online vacancies is shown. Two mechanisms for adding new links between requirements words, taking into account knowledge from the subject area, are implemented: linear and through the addition of the parsing tree. In the course of the experiment, a comparative analysis was carried out for several combinations of the listed tools. The combination that showed the best result was 'the BERT + deeppavlov_syntax_parser model + a linear method of adding links'. The applicability of the method was demonstrated on the text corpus of online job requirements. The proposed method has shown higher efficiency than the rule-based approach, which involves the use of formal rules and grammar rules for natural language analysis. Using the method will allow you to quickly identify the key changes in the needs of the labor market at the level of requirements texts of individual knowledge and skills.