Semantic Uncertainty Research Articles

Semantic Segmentation Uncertainty Assessment of Different U-net Architectures for Extracting Building Footprints

Abstract. Automatic extraction of building footprints from aerial and space imageries has been found ever increasing importance in urban planning, disaster management, and environmental monitoring. However, achieving accurate building footprint extraction poses significant challenges due to diverse building characteristics and their similarities to their background elements. While conventional methods in building footprint extraction have mainly relied on image processing techniques, recent advancements in deep learning, particularly semantic segmentation algorithms like U-Net, have shown promise in addressing these challenges through machine learning. This study explores different depths of the U-Net model for building footprint extraction, aiming to identify the optimum architecture while investigating the semantic uncertainty of the building footprint extraction. Utilizing aerial imagery from cities including Berlin, Paris, Chicago, and Zurich, collected from Google Maps and OpenStreetMap (OSM) data, five U-Net models have been compared with varying depths. In addition, the impact of dataset sizes and learning rates on model performance has been investigated. Results confirmed that the U-Net-32-1024 model achieves the highest intersection over union (IoU), Accuracy, and F1-score. Moreover, increasing the training dataset size leads to significant improvements in model performance with IoU, Accuracy and F1-score reaching their values of 73.73%, 88.65% and 88.53%. However, challenges remain in accurately delineating buildings in dense urban areas. Nonetheless, our findings demonstrated the effectiveness of U-Net models in building footprint extraction.

Economical hybrid novelty detection leveraging global aleatoric semantic uncertainty for enhanced MRI-based ACL tear diagnosis

This study presents an innovative hybrid deep learning (DL) framework that reformulates the sagittal MRI-based anterior cruciate ligament (ACL) tear classification task as a novelty detection problem to tackle class imbalance. We introduce a highly discriminative novelty score, which leverages the aleatoric semantic uncertainty as this is modeled in the class scores outputted by the YOLOv5-nano object detection (OD) model. To account for tissue continuity, we propose using the global scores (probability vector) when the model is applied to the entire sagittal sequence. The second module of the proposed pipeline constitutes the MINIROCKET timeseries classification model for determining whether a knee has an ACL tear. To better evaluate the generalization capabilities of our approach, we also carry out cross-database testing involving two public databases (KneeMRI and MRNet) and a validation-only database from University General Hospital of Larissa, Greece. Our method consistently outperformed (p-value<0.05) the state-of-the-art (SOTA) approaches on the KneeMRI dataset and achieved better accuracy and sensitivity on the MRNet dataset. It also generalized remarkably good, especially when the model had been trained on KneeMRI. The presented framework generated at least 2.1 times less carbon emissions and consumed at least 2.6 times less energy, when compared with SOTA. The integration of aleatoric semantic uncertainty-based scores into a novelty detection framework, when combined with the use of lightweight OD and timeseries classification models, have the potential to revolutionize the MRI-based injury detection by setting a new precedent in diagnostic precision, speed and environmental sustainability. Our resource-efficient framework offers potential for widespread application.

An end-to-end image-text matching approach considering semantic uncertainty

We propose a novel end-to-end image-text matching approach considering semantic uncertainty (SU-ITM), aiming to deal with the one-to-many semantic diversity involved in image-text matching in order to capture the associations between them more comprehensively and improve the robustness of the model. Traditional methods map images and texts as definite points in an embedding space to measure cross-modal similarity. However, the point-based embedding cannot capture the semantic uncertainty, leading to a large bias in the matching results. To address this problem, we model the one-to-many associations between image and text in a way that establishes a probability distribution, incorporating the uncertainty information into the final semantic representation of the text. In addition, we optimize the image-text matching loss so that the different text features approximate the image features in a distributed manner while maintaining the discriminative nature of the semantic representation, effectively reducing the matching uncertainty. Notably, our method achieves end-to-end training by not using pre-trained target detection branches throughout the training process. We fully demonstrate the excellent performance of our method in the image-text matching task through experimental validation on Flickr30k and MSCOCO. Excellent performance levels of 546.1 and 545.0 are achieved on the R@SUM metric for Flickr30k and MSCOCO 1k, respectively.

Progressive Decision Boundary Shifting for Unsupervised Domain Adaptation.

Unsupervised domain adaptation (UDA) is attracting more attention from researchers for boosting the task-specific generalization on target domain. It focuses on addressing the domain shift between the labeled source domain and the unlabeled target domain. Recent biclassifier-based UDA models perform category-level alignment to reduce domain shift, and meanwhile, self-training is used for improving the discriminability of target instances. However, the error accumulation problem of instances with high semantic uncertainty may cause discriminability degradation and category-level misalignment. To solve this issue, we design the progressive decision boundary shifting algorithm, where stable category information of target instances is explored for learning a discriminability structure on target domain. Specifically, we first model the semantic uncertainty of instances by progressively shifting decision boundaries of category. Then, we introduce the uncertainty decoupling in a contrastive manner, where the discriminative information is learned from the source domain for instance with low semantic uncertainty. Furthermore, we minimize the predictive entropy of instances with high semantic uncertainty to reduce their prediction confidence. Extensive experiments on three popular datasets show that our model outperforms the current state-of-the-art (SOTA) UDA methods.

Methods of accounting for measurement uncertainty and semantic uncertainty in the classification of the state of objects according to quality indicators

The paper proposes methods of accounting for measurement uncertainty and semantic uncertainty when consistently using measurement and classification procedures to classify the state of objects according to their quality indicators. Methods of accounting for uncertainty are distinguished by two approaches: the first one is based on accounting for measurement uncertainty when constructing a classification scale, and the second one is based on presenting the result of measuring a quality indicator as a fuzzy number, the carrier of which is the expanded measurement uncertainty. Using these methods, the state of the object can be classified according to one or more categories of the classification scale, indicating the degree of belonging to these categories. Recommendations for the application of two approaches to account for measurement uncertainty in the subsequent classification are given. The use of one or more fuzzy classification scales is recommended when classifying the state of an object by a single quality indicator or by several quality indicators that are combined into a group indicator after classification using the fuzzy logic operators, such as arithmetic mean, as well as pairwise volume operators. When using single and group quality indicators, measurement uncertainty may be accounted for when constructing a classification scale, that is, when using a fuzzy scale. At the same time, the components of the measurement uncertainty preceding the classification and the vagueness of the expert information that may be used in the classification can be accounted for. In this way, the following components of the uncertainty can be defined: instrumental uncertainty and semantic uncertainty. The use of a clear classification scale when presenting the measurement result as a fuzzy number is recommended when classifying the state of the object according to a complex quality indicator that is related to individual quality indicators by a certain dependence or by accounting for the weighting factors of individual quality indicators. It is shown that when accounting for the measurement uncertainty, the state of the object may be classified into one or several categories of the classification scale, indicating the degree of belonging to each category.

Semantic Minimalism’s Truth-Evaluability: The Minimal Proposition and Semantic Uncertainty

Semantic Minimalism’s Truth-Evaluability: The Minimal Proposition and Semantic Uncertainty

Teacher’s Agency as an Indicator of Leadership Potential Development

Nonlinearity, multi-contextuality, new normality, constant variability of the modern world are reflected in politics, economics, education and other spheres of human life. Professional and personal self-determination and orientation of a person is difficult in a situation of diversity of various social environments and spaces, functional and semantic uncertainty of educational contexts. In this regard, the importance in the field of education is acquired by the teacher’s transprofessionalism, his ability to continuously develop, independently build interaction with the multifunctional environment, showing his agency as one of the indicators of leadership potential development. The purpose of the study is to operationalize the content of the concept of the teacher’s agency and its manifestation as an indicator of the development of leadership potential, taking into account the ongoing changes in the modern world. Methodology. The understanding of the essence of the terms “agency” and “leadership potential” of the teacher was carried out using the method of semantic analysis of their interpretations available in domestic and foreign sources. The use of activity-based and acmeological approaches is due to the need to determine the key mechanism for implementing the teacher’s agency in the comparison of significant categories (agency, leadership and management). General scientific research methods were used: theoretical analysis and synthesis, systematization and generalization of scientific literature on the research topic. Results. Connectivism (social polyvalence) is proposed as the main mechanism for the manifestation of the teacher’s agency as a teacher’s ability to find and maintain conditions, forms, ways and means of communication with representatives of different professions, social groups and statuses, beliefs, ages for organizing joint actions, events, productive activities. This made it possible to establish the key role of agency in the development of a teacher’s leadership potential – the creation of an atmosphere of cooperation within and outside the educational organization. Conclusion. The conclusion is made that the teacher, as a carrier and subject of public relations, needs to develop his agency and leadership potential, respectively, for orientation and adaptation in a changing world in the interests of sustainable development of regions and the country as a whole.

Discourse factor of semantic uncertainty in reverse machine translation of media texts

The article is devoted to the study of semantic uncertainty in the texts of legal and media discourses using online programs. The object of the study is conflict factors caused by semantic uncertainty in problem areas. An assessment and description of problem areas in machine translation of media texts was carried out, taking into account lexical ambiguity. The prerequisites for possible conflict situations when translating texts on legal and environmental topics are described. In achieving this goal, both traditional linguistic and innovative methods of analysis (use of artificial intelligence technologies) were used. The characteristics of the philosophical category of uncertainty are given according to the following methodology: 1) assessment of the semantic uncertainty of reverse machine translation texts, taking into account the discursive factor; 2) comparison of legislative and media texts for their similarity to the original after machine translation. The comparative analysis took into account the following parameters: the total number of words in the texts; similarity of texts in percentage terms; presence of various fragments. It is shown that discursive and linguistic means of realizing semantic uncertainty have a significant impact on the reader’s perception of the text. A comparison of two discourses of the same thematic focus on environmental management revealed the influence of the context (discourse) and the intermediary language on the degree of translatability of the text. It has been established that semantic uncertainty and tension zones as conflict-generating factors depend on the quality of translation programs. The study took into account possible errors associated with the limited capabilities of the online programs used. Prospects for studying problem areas in reverse machine translation include expanding sources of empirical material, involving other computer-assisted translation systems, and the possibility of applying research results in an expanded context of “potential conflict”.

Text Mining - A Comparative Review of Twitter Sentiments Analysis

Background: Text mining derives information and patterns from textual data. Online social media platforms, which have recently acquired great interest, generate vast text data about human behaviors based on their interactions. This data is generally ambiguous and unstructured. The data includes typing errors and errors in grammar that cause lexical, syntactic, and semantic uncertainties. This results in incorrect pattern detection and analysis. Researchers are employing various text mining techniques that can aid in Topic Modeling, the detection of Trending Topics, the identification of Hate Speeches, and the growth of communities in online social media networks. Objective: This review paper compares the performance of ten machine learning classification techniques on a Twitter data set for analyzing users' sentiments on posts related to airline usage. Methods: Review and comparative analysis of Gaussian Naive Bayes, Random Forest, Multinomial Naive Bayes, Multinomial Naive Bayes with Bagging, Adaptive Boosting (AdaBoost), Optimized AdaBoost, Support Vector Machine (SVM), Optimized SVM, Logistic Regression, and Long-Short Term Memory (LSTM) for sentiment analysis. Results: The results of the experimental study showed that the Optimized SVM performed better than the other classifiers, with a training accuracy of 99.73% and testing accuracy of 89.74% compared to other models. Conclusion: Optimized SVM uses the RBF kernel function and nonlinear hyperplanes to split the dataset into classes, correctly classifying the dataset into distinct polarity. This, together with Feature Engineering utilizing Forward Trigrams and Weighted TF-IDF, has improved Optimized SVM classifier performance regarding train and test accuracy. Therefore, the train and test accuracy of Optimized SVM are 99.73% and 89.74% respectively. When compared to Random Forest, a marginal of 0.09% and 1.73% performance enhancement is observed in terms of train and test accuracy and 1.29% (train accuracy) and 3.63% (test accuracy) of improved performance when compared with LSTM. Likewise, Optimized SVM, gave more than 10% of enhanced performance in terms of train accuracy when compared with Gaussian Naïve Bayes, Multinomial Naïve Bayes, Multinomial Naïve Bayes with Bagging, Logistic Regression and a similar enhancement is observed with AdaBoost and Optimized AdaBoost which are ensemble models during the experimental process. Optimized SVM also has outperformed all the classification models in terms of AUC-ROC train and test scores.

Methods of judicial argumentation in case of semantic uncertainty of a legal text

While in non-legal texts linguistic uncertainty either does not hinder normal linguistic practices or can be removed by clarifications, the elimination of uncertainty in legal texts is complicated since peculiarities of legal language precludes its complete exclusion. Accordingly, the task of overcoming legal uncertainty is delegated to the judge and the question arises as to what methods of dealing with the legal text are applicable. The article examines the approaches distinguished in the theory of legal argumentation to assess whether they can offer a methodology which will be able to overcome the policy consequences of the language “open texture” as a characteristic of uncertainty of the natural language in which legal texts are presented by formalizing the process of choosing one of the possible options of the legal text interpretation in judicial decisions. The author concludes that the methods to deal with an uncertain legal text offered by the topicalrhetorical and dialectical approach are limited to the formulation of requirements for the process of argumentation (which do not guarantee absolute predictability of the decision to be made), as well as criteria to evaluate the justification of the decision retrospectively. In turn, the possibility to critically assess the arguments limits the arbitrariness, forcing the judge to follow standards of acceptability and sufficiency of the reasoning to justify their decision.

Geometric Matching for Cross-Modal Retrieval.

Despite its significant progress, cross-modal retrieval still suffers from one-to-many matching cases, where the multiplicity of semantic instances in another modality could be acquired by a given query. However, existing approaches usually map heterogeneous data into the learned space as deterministic point vectors. In spite of their remarkable performance in matching the most similar instance, such deterministic point embedding suffers from the insufficient representation of rich semantics in one-to-many correspondence. To address the limitations, we intuitively extend a deterministic point into a closed geometry and develop geometric representation learning methods for cross-modal retrieval. Thus, a set of points inside such a geometry could be semantically related to many candidates, and we could effectively capture the semantic uncertainty. We then introduce two types of geometric matching for one-to-many correspondence, i.e., point-to-rectangle matching (dubbed P2RM) and rectangle-to-rectangle matching (termed R2RM). The former treats all retrieved candidates as rectangles with zero volume (equivalent to points) and the query as a box, while the latter encodes all heterogeneous data into rectangles. Therefore, we could evaluate semantic similarity among heterogeneous data by the Euclidean distance from a point to a rectangle or the volume of intersection between two rectangles. Additionally, both strategies could be easily employed for off-the-self approaches and further improve the retrieval performance of baselines. Under various evaluation metrics, extensive experiments and ablation studies on several commonly used datasets, two for image-text matching and two for video-text retrieval, demonstrate our effectiveness and superiority.

NARRATIVE AS A MEANS OF SOLVING MEANINGFUL PROBLEMS IN A SITUATION OF UNCERTAINTY

The purpose of the article: is to show that the personality life competence is determined by the development of his ability to solve problems that arise in unpredictable life situations by means of constructing a narrative text. Life competence is interpreted as a significant prerequisite for the successful resolution of various life and personal problems that a person faces, especially in situations of uncertainty. It is the analysis, interpretation, and understanding of such situations that motivates a person to independently set and successfully solve problems, first of all, problems of searching and finding sense. Lack of information about the situation, its inconsistency, newness, and complexity of the situation are among the most common factors that cause the uncertain situation and make it difficult to adequately perceive and understand it. The most significant factor that complicates human activity in a situation of uncertainty is semantic uncertainty. It creates in the personality an anxious state associated with future uncertain events, for which it is difficult to prepare since there are no corresponding equivalents in the individual experience that would allow overcoming the semantic chaos. An important role in the interpretation of an uncertain situation is played by interpretation, which is a cognitive process of giving concrete meaning to the multidimensional components of an individual's experience. Narrative is the leading interpretative scheme that determines the transformation of personal experience and its adaptation to a new situation. It is expedient to interpret it as a means of solving semantic problems faced by an individual in a situation of uncertainty. A leading role in the process of generating a narrative is played by reflection, which is an important resource for creating meaning, adapting to life changes, and overcoming difficult life situations.

Semantic uncertainty of the general theory of systems and problems of its interpretation and formalization

The subject of research in this article is the question of the possibility of formalizing the general theory of systems, that is, turning it into a language for describing systems of any nature with unambiguously defined lexical units and rules. To answer this question, the author considers the phenomenon of semantic indeterminacy of languages, which ensures the flexibility of formed lexical constructions due to the multivalence of lexical units. Also the subject of the research is the practice of quoting out of context – a phenomenon, the admissibility of which is conditioned by the isomorphism of lexical constructions, as well as by the creative nature of the cognitive process, in which the result of cognition is unknown in advance, and thus the change of interpretation of the used concepts is inevitable. The conducted research allows us to state that the general theory of systems cannot be exclusively an applied theory, but should be supplemented with an ontological component. In this case, it becomes a philosophical theory, for which full formalization is impossible without loss of functionality. As a result, general systems theory must inevitably retain a certain semantic uncertainty. This uncertainty, however, can be reduced by translation into universality and generalization of concepts. Besides, it is possible and in some cases necessary to formalize separate components of the general theory of systems, in particular, its ontological component - the metaphysics of material existence.

Semi-supervised knowledge distillation framework for global-scale urban man-made object remote sensing mapping

Accurate mapping of global urban man-made objects such as buildings and roads is critical for monitoring urbanization. Remote sensing imagery provides a cost-effective way of mapping these objects, but the challenge of “knowledge forgetting” arises due to urban diversity and the continuous growth of global samples. Although the existing knowledge distillation approaches can transfer knowledge from a larger teacher model to a smaller student model by distilling the knowledge learned from reliable labels, they fail to work for global-scale mapping, which lies in two aspects: low-quality labeling and fixed-size models. In this paper, we propose GUMONet, which is a semi-supervised knowledge distillation framework for global-scale urban man-made object mapping. For the first phase, a label diversity progressive learning module is introduced for generating high-quality labels in a semi-supervised manner. Label diversity is used to measure the diverse urban patterns based on spatial-semantic uncertainty, where the diversified labels clustered in object boundaries and heterogeneous areas are attributed to high spatial uncertainty and semantic uncertainty, respectively. Based on the label diversity, the model decision boundary is progressively determined from coarse to fine. Specifically, at the early stage, instances away from the decision boundary are selected to ensure the stability of the model training. As the iteration progresses, instances close to the decision boundary are associated with a higher probability of further enhancing the quality of the uncertain labels by hard sample mining. For the second phase, a size-variable knowledge distillation module is adopted to optimize the data-model matching process. This module consists of a noise teacher model that prevents overfitting by injecting noise perturbations to increase the data distribution complexity and a size-variable student model that avoids underfitting by dynamically adjusting its size with the growth of global samples. We applied GUMONet to six study areas across four continents, with data from different sensors, achieving an 18.97% improvement in intersection over union, compared with the previous methods. Our results also demonstrate a positive correlation between urban development and urban diversity, with a correlation coefficient of 0.749. As urban development progresses, urban diversity stabilizes and building transformation becomes the primary means of promoting further development.

МЕХАНИЗМЫ ФОРМИРОВАНИЯ СОВРЕМЕННОЙ ДЕСТРУКТИВНОЙ РЕАЛЬНОСТИ: СОЦИАЛЬНО-КОГНИТИВНЫЙ АСПЕКТ

Objective of the study is to identify the mechanisms of formation of modern destructive reality from the point of view of the socio-cognitive aspect. The methodological basis of the research. The authors consider the issue of transformation of forms and means of communication in the modern world and, as a consequence, the change of techniques of manipulation of consciousness and the formation of an actual political and cultural agenda. The methodology is based on a systematic approach based on the ideas and theoretical developments of the largest scientists of the modern period. Research results. The article shows the crisis moments of perception and transmission of information using the example of generation Z. The characteristic features of modern digital communication developed during the period of decreasing interpersonal interaction are presented: distancing, semantic uncertainty and phantomness. The availability of information and its excess lead to a lack of motivation for critical thinking of representatives of the information society generation, superficial perception, as well as a decrease in the overall level of culture and education. Prospects of the study. There is a need for spiritual and moral vaccination of public consciousness from the standpoint of traditional cultural values rooted in the Russian cultural core. Russian russians need to rethink the experience of thirty years of calculating individualism, pragmatism and atomism of Western civilization through the revival of traditional culture, inherent in the Russian people as a support, which through the mass media deforms and destroys the cultural core of Russian civilization, broadcasting what a consumer should strive for, what standards must be met (success, wealth, beauty). while completely ignoring the spiritual component.

The paradox of tradition: Alexander Spendiaryan, Lev Revutsky, Igor Stravinsky

The article is devoted to relevance of the folklore tradition in the work of composers of various national schools, namely, A. Spendiaryan, I. Stravinsky, L. Revutsky. Tradition is considered from the perspective of the main characteristics of metamodern as one of the important semantic guidelines for the listener, who is in the conditions of semantic uncertainty and oscillation in modern culture. The article attempts to highlight the paradoxical nature of tradition, which lies in its avant-garde potential against the background of “habitualness” of innovations in modern culture. From the history of arts, examples of using traditional elements by the avant-garde in the works of A. Archipenko, K. Malevich, M. Saryan, A. Erdeli are given. The interpretation of folklore in the works of Spendiaryan, Stravinsky and Revutsky reveals both common and different features, which indicates the innovative potential of tradition. Spendiaryan and Revutsky follow the integrity of the original folklore source and its active development, creating a new colorful material, while Stravinsky explores it and, on its basis, creates a new synthesis of word, movement and sound. The conclusion is made that tradition may sound modern, it may sound more “avant-garde” than many of the presented innovations․

The effect of speech degradation on the ability to track and predict turn structure in conversation

Conversation represents a considerable amount of the daily language usage and plays an important role in language acquisition. In conversation, listeners simultaneously process their interlocutor's turn and prepare their own next turn. As such the turn-taking dynamics heavily relies on prediction. In other words, listeners avail prior knowledge to constrain both speech perception and production. Here we explored the relation between prediction and comprehension while watching two interlocutors having a conversation. We capitalize on gaze switch as a proxy of predictive behaviour to class dialogue turns as more or less well predicted and explore how this affects dialogue comprehension. Moreover, we study the extent to which speech degradation, by increasing the global uncertainty of the context, affects the relation between predictions, brain correlates of prediction errors (N400) and global comprehension. Results show that 1) listeners direct gaze to the current speaker, in particular in challenging conditions, 2) gaze behaviour possibly affects the semantic processing of the upcoming turn (N400), 3) participants with a more efficient gaze predictive behaviour better solve semantic uncertainties at the turn onset, in particular in the most challenging listening condition. Our findings contribute to a better understanding of the relation between predictions, neural predictions errors and speech comprehension under challenging conditions.

ЧТО ОЗНАЧАЕТ "ПОТЕРЯТЬ ЛИЦО" В РУССКОМ ЯЗЫКЕ?

The article deals with the semantic uncertainty of the phraseologism to lose face in the modern Russian language. The purpose of the study is to establish the evaluative characteristics of “loss of face” in the context of other moral “losses” (lose shame, lose conscience). The work was carried out on the material of explanatory and phraseological dictionaries of the Russian language with the use of data from the National Corpus of the Russian Language. In the course of the study, methods and operational procedures were used, including contextual analysis of phraseological unit syntagmatics, elements of component and frame analysis, methods of equivalent substitutions and context transformations. The study found that the “loss of face” in the Russian language occupies a special position among other significant moral losses. “To lose conscience, shame” denotes the loss of morally valuable qualities denoted by these words, and is understood unambiguously in this regard. The polysemy of the word face leads to the fact that the phraseological units lose face can convey a whole range of meanings, among which the main ones are the following: loss of individuality, distinctive personality traits, loss of reputation and respect in the eyes of others, loss of control over oneself, one’s emotions. The evaluative characteristics of the phraseological unit to lose face also differ from the evaluation of “loss of shame” or “loss of conscience”. “Loss of shame (conscience)” receives public censure; “loss of face” is negatively evaluated and painfully realized by the subject himself.

How Many Types of Justice are in Thomas Hobbes’ Leviathan?

The article examines the problem of the multiplicity of justice in Leviathan by Thomas Hobbes. The Leviathan combines at least two understandings of justice; the civil one is connected with the keeping of covenants, while the natural one is a law of nature. We demonstrate that monistic views reducing civil justice to natural justice or natural justice to civil justice are as inadequately justified as denying justice at all. Hobbes uses two terms for justice, justice and equity. The latter is natural and binds the sovereign, while the former is created by the sovereign so that the sovereign is not accountable to the principle of justice. The natural poly-semantism of justice postulated in Leviathan finds its solution in the power of the sovereign, which sets the limits of semantic uncertainty and teaches his subjects what justice is. The case of Hobbes’ Foole shows that any definition of justice that goes against the definition of the sovereign will be interpreted as unacceptable. At the same time, there is a possibility for a number of other types of justice. Due to the introduction of the global and eschatological contexts, we get two types of natural justice (pre-civil and international), four types of civil justice (two local-civil and two global-civil), and one global theological (eschatological) justice. This number of conceptions can be considered in a contensive unity, because of the theological foundation of theorizing about justice in Leviathan due to the coincidence of natural and divine laws and the understanding of the commonwealth as a mortal God.

Problems of semantic reconstruction of the sociological narrative

A sociological narrative is a human’s storytelling in which another human is not always able to trace semantic connections in a relevant way. Such uncertainty has a detrimental effect in key cases of verbal description of social processes such as theory building, drafting laws and maintaining of document flow. Based on research in the areas of qualitative data analysis and knowledge management, the article expresses the conviction that a significant reason for the semantic uncertainty of the narrative is its construction by the author in the form of a text flow. The necessity and possibility of a natural language description of social processes in the form of a structural narrative is substantiated. Analytical coding is proposed as a tool for constructing such a narrative. In contrast to the approaches used today, analytical coding allows the researcher in a single instrumental procedure, on the one hand, to adhere to the habitual practice of verbal description of something; on the other hand, to structure their natural language wordings with the help of ontological methods of knowledge management. Specific analytical coding techniques are considered in detail within methodological cases in which a researcher finds him/herself when deciding to construct a narrative using analytical methods. Among such cases, the initial epistemological attitudes of the author of the narrative; localization of the meaning of a verbal statement by indicating the context; linking contextsensitive wordings; construction and generation of narrative semantic chains; instrumental control of narrative connectivity are mentioned. In conclusion, it is noted that the improvement of methods for constructing a structural narrative allows us reckoning on solving the problems of decomposition of verbal knowledge about social processes and on creating opportunities for interdisciplinary teamwork of specialists with different competencies