Recognition of spatial data from natural language texts for the purpose of visualization

This paper describes one of the possible implementation options for the recognition of spatial data from natural language texts. The proposed option is based on the lexico-syntactic analysis of texts, which requires the use of special grammars and dictionaries. Spatial data recognition is carried out for their subsequent geocoding and visualization. The practical implementation of spatial data recognition is done using a free, freely distributed software tool. Also, some applications of spatial data are considered in the work and preliminary results of spatial data recognition are given.

System analysis of the natural language modeling problem

System analysis of the problem of modeling a natural language (NL) made it possible to formulate the root cause of the low efficiency of modern means for accumulating and processing knowledge in such languages. This is the complexity of intellectualization for such tools, which are created on the basis of primitive artificial programming languages that practically represent a subset of flectional analytical languages or artificial constructions based on them. To reduce the severity of the identified problem, it is proposed to build NL modeling systems on the basis of technological tools for verbalization and recognition of sense. These tools consist of semiotic models of NL lexical and grammatical means. This approach seems to be especially promising for agglutinative languages; it is supposed to be implemented on the example of the Tatar language.

Methodological foundations of security management information support of regional critical infrastructures

The unified methodological basis of information and analytical support of socio-economic security network-centric control in the region is proposed. The problem of regional security support is discussed at the level of risk-management of critical infrastructure resilience violation of the socio-economic systems. The methodology and tools for its implementation are aimed to information and analytical support of situational centers functioning in the region.

Planning of the open-pit working edge positions by the periods of mining within the constraint programming paradigm

The work is aimed at solving the three-dimensional problem of finding the open-pit working edge positions by the periods of mining, taking into account the a priori specified productivity for the mineral and overburden. The proposed method uses a block model of a pit, where for each block its coordinates, the content of minerals in it, and the conditional initial value of the block are known. Also, a discounting function is set - a change in the total value of a block, depending on the period of its mining. The task is to find the distribution of blocks over mining periods that maximizes the total value of the blocks. Combinatorial search acceleration is achieved by representing a number of technological constraints in the form of global constraints.

Technology of training a neural-network model for ontology population

The paper considers one of the subtasks of ontology learning - the ontology population, which implies the extension of existing ontology by new instances without changing the structure of its classes and relations. A brief overview of existing ontology learning approaches is presented. A highly automated technology for ontology population based on training and application of the neural-network language model to identify and extract potential instances of ontology classes from domain texts is proposed. The main stages of its application, as well as the results of its experimental evaluation and the main directions of its further improvement are considered.

The geoattributed entity extraction technology for visual representation of objects spatial relations based on automated schematic map generation

This paper considers the problem of extracting geoattributed entities from natural language texts to visualize the spatial relations of geographical objects. For visualization we use the technology of automated generation of schematic maps as subject-oriented components of geographic information systems. The paper describes the information technology that allows extracting geoattributed entities from natural language texts by combining several approaches. These are the neural network approach, the rule-based approach and the approach based on the use of lexico-syntactic patterns for the analysis of natural language texts. For data visualization we propose to use automated geocoding tools in conjunction with the capabilities of modern geographic information systems. The result of this technology is a cartogram that displays the spatial relations of the objects mentioned in the text.

Intellectual support for socially-oriented operational decisions making during hospitalization, taking into account limited resources and human behavior

The research was carried out with the financial support of the Russian Foundation for Basic Research within the framework of scientific project No. 20-04-60054 in terms of support for making socially-oriented decisions and budgetary topic No. 0073-2019-0005 in terms of organizing user interaction with the system.

Training set augmentation in training neural- network language model for ontology population

This paper is a continuation of the research focused on solving the problem of ontology population using training on an automatically generated training set and the subsequent use of a neural-network language model for analyzing texts in order to discover new concepts to add to the ontology. The article is devoted to the text data augmentation - increasing the size of the training set by modification of its samples. Along with this, a solution to the problem of clarifying concepts (i.e. adjusting their boundaries in sentences), which were found during the automatic formation of the training set, is considered. A brief overview of existing approaches to text data augmentation, as well as approaches to extracting so-called nested named entities (nested NER), is presented. A procedure is proposed for clarifying the boundaries of the discovered concepts of the training set and its augmentation for subsequent training a neural-network language model in order to identify new concepts of ontology in the domain texts. The results of the experimental evaluation of the trained model and the main directions of further research are considered.

Resilience of critical infrastructure of regional security

The article is devoted to the development of the concept of regional security management based on the principles of ensuring the resilience of critical infrastructures. The article is a problem statement and considers general theoretical issues in the field of resilience of complex systems. The authors identify two main approaches to the model of representation of the regional security system from the perspective of ensuring its resilience.

Two-stage technology of automated terminology extraction based on topic modeling and context analysis

The paper deals with the task of automated terminology extraction. A two-stage technology for its solution is proposed, based on topic modeling and analyzing the context of the use of lexical units. The results of experimental verification of the technology and the prospects for its further development are presented.