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На основе анализа применения моделей управления доступом с использованием тематических иерархических классификаторов в медицинских информационных системах с применением мобильных систем в качестве элементов предложены методы формирования доверительных прав на доступ к объектам. При использовании традиционных тематически иерархических моделей управления доступа логическая информационная архитектура ресурсов медицинских информационных систем формирует собой тематический иерархический классификатор (рубрикатор). Диаграмма Хассе вводит отношения порядка в тематическом классификаторе на решетке безопасности для формирования доверительно - тематических полномочий пользователей медицинских информационных систем. Построение диаграмм Хассе на решетке безопасности включающей несколько уровней безопасности достаточно сложная алгоритмическая задача. При построении доверительно - тематических полномочий пользователей для избегания неопределенности при неполноте построенной диаграммы Хассе и завышения предоставленных полномочий при формировании прав доступа предлагается использовать семантическую близость запроса на доступ пользователя и тематической рубрики иерархического классификатора. Анализ существующих подходов к формированию метрик семантической близости показал, что в качестве наилучшей метрики для задания доверительных полномочий пользователя может использоваться меры близости, основанные на иерархии понятий Based on the analysis of the application of access control models using thematically hierarchical classifiers in medical information systems using mobile technologies (MS), methods of forming access rights to objects are proposed. When using traditional thematically hierarchical access control models, the logical information architecture of the medical information systems resources forms a thematic hierarchical classifier (rubricator). The Hasse diagram introduces order relations into the thematic classifier on the security grid to form the trust-thematic powers of the medical information systems users. The construction of Hasse diagrams on a security grid that includes several security levels is a rather complex algorithmic task. When constructing trust-thematic user powers to avoid ambiguity in the case of incompleteness of the constructed Hasse diagram and overestimation of the granted powers when forming access rights, it is proposed to use the semantic proximity of the user's access request and the thematic heading of the hierarchical classifier. An analysis of existing approaches to the formation of semantic proximity metrics showed that proximity measures based on a hierarchy of concepts can be used as the best metric for setting the user's trust authority

There have been many attempts at classifying the semantic modification relations (ℜ) of N + N compounds but this work has not led to the acceptance of a definitive scheme, so that devising a reusable classification is a worthwhile aim. The scope of this undertaking is extended to other binominal lexemes, i.e. units that contain two thing-morphemes without explicitly stating ℜ, like prepositional units, N + relational adjective units, etc. The 25-relation taxonomy of Bourque (2014) was tested against over 15,000 binominal lexemes from 106 languages and extended to a 29-relation scheme (“Bourque2”) through the introduction of two new reversible relations. Bourque2 is then mapped onto Hatcher’s (1960) four-relation scheme (extended by the addition of a fifth relation, similarity, as “Hatcher2”). This results in a two-tier system usable at different degrees of granularities. On account of its semantic proximity to compounding, metonymy is then taken into account, following Janda’s (2011) suggestion that it plays a role in word formation; Peirsman and Geeraerts’ (2006) inventory of 23 metonymic patterns is mapped onto Bourque2, confirming the identity of metonymic and binominal modification relations. Finally, Blank’s (2003) and Koch’s (2001) work on lexical semantics justifies the addition to the scheme of a third, superordinate level which comprises the three Aristotelean principles of similarity, contiguity and contrast.

Aspect category detection is a subtask of aspect-level sentiment analysis, which aims at identifying the aspect categories present in an opinion. It is a difficult task because the category must be inferred from the terms of the opinion, and also because each opinion may include judgments for more than one aspect category. In recent years, the use of attention mechanisms has improved performance in different tasks, allowing the identification and prioritization of terms that mostly contribute to the classification. However, in multi-label problems, such as aspect category detection, different terms must be selected based on each category, which is a drawback for these models. Motivated by the same idea of identifying and highlighting the importance of terms, this paper proposes a weighing scheme that emphasizes terms in an opinion based on their semantic proximity to each aspect category. The proposed scheme has been evaluated on different SemEval datasets, demonstrating its effectiveness in this multi-label scenario. Moreover, it can be applied in scenarios with limited training data and can be combined with different classification models, including deep neural networks. La detección de categorías de aspecto es una subtarea dentro del análisis de sentimientos a nivel de aspecto. Esta subtarea aborda la identificación de aquellas categorías de aspecto presentes en una opinión. Se trata de una tarea desafiante pues la categoría debe inferirse de los términos de la opinión, aunado a esto, una opinión puede incluir evaluaciones de más de una categoría de aspecto. En los últimos años, el uso de mecanismos de atención ha permitido mejorar los resultados en distintas tareas, éstos permiten identificar y priorizar los términos clave que contribuyen a la clasificación. Sin embargo, en problemas multi-etiqueta, como la detección de categorías de aspecto, se deben seleccionar diferentes términos dependiendo de cada categoría lo cual es un inconveniente para estos modelos. Motivados por esta misma idea de identificar y destacar la importancia de términos clave, en este trabajo se propone un esquema que permite enfatizar los términos de una opinión en función de su proximidad semántica a cada categoría de aspecto. El esquema propuesto se evaluó en distintos conjuntos de datos de SemEval demostrando su efectividad en este escenario multi-etiqueta. Además, es posible aplicarlo a pesar de contar con pocos datos de entrenamiento, y puede combinarse con distintos modelos de clasificación, incluyendo redes neuronales profundas. The present work was supported by CONACyT/México (scholarship 756974 and grant CB-2015-01-257383). In addition, the authors thank CONACYT for the computational resources provided by the Deep Learning Platform for Language Technologies.

Graph Representation Learning aims to learn a rich and low-dimensional node embedding while preserving the graph properties. In this paper, we propose a novel Deep Attributed Graph Embedding (DAGE) that learns node representations based on both the topological structure and node attributes. DAGE a is able to capture, in a linear time and with a limited number of trainable parameters, the highly non-linear properties of attributed graphs. The proposed approach outperforms the current state-of-the-art approaches on node classification and node clustering tasks at a lower computational costs.

Оцінювання організацій і колективів з багаторівневою ієрархічною структурою (проєктні групи, групи дослідників, військові підрозділи, експертні комісії, медичні групи швидкого реагування) є сьогодні актуальною проблемою, вирішення якої дозволить по новому організувати їх швидку динамічну адаптацію для виконання важливих оперативних завдань, а також підготовку членів команди при відсутності достатніх компетенцій, навиків та досвіду. Оцінювання також виявляє слабкі та сильні сторони як колективу в цілому, так і кожного учасника, що у перспективі надає можливості їх подальшого росту. Оцінювання роботи колективів є комплексним, що включає як фахові знання з певної галузі діяльності, так і навички керування колективом (так звані навички лідерства, уміння організувати виконання завдань та досягнення цілей і отримання позитивних результатів роботи). Щоб покращити оцінювання ієрархічних агрегованих команд запропоновано використовувати онтологічний підхід: онтологія предметної області визначає знання про відповідні комбінації компетенцій для позицій учасників в організаційній структурі колективу. Інформація про компетенції претендентів отримується шляхом аналізу портфоліо (резюме, сертифікати, дипломи, заслуги). У випадку, коли наявність компетенцій є недостатньою для отримання певної позиції, система автоматично підбирає курси для отримання цих компетенцій. Більш складні завдання формування колективу можуть використовувати ієрархічний набір онтологій та вебсервісів для отримання результатів оцінювання та рекомендацій щодо підвищення компетентності. Створено покрокову методику виконання задачі оцінювання колективу з застосуванням методів семантичної подібності між моделями інформаційних об’єктів для підбору претендентів на посади, вибору апаратури, обладнання, озброєння. Апробовано підхід ієрархічного агрегатного оцінювання колективів, орієнтований на інтегроване багатокритеріальне прийняття рішень в умовах, коли сам набір критеріїв залежить від особливостей задачі і може бути створений на основі знань предметної області, але в різні моменти часу відносна значущість цих критеріїв може змінюватися. Цей підхід планується розширити за допомогою засобів і методів керування знаннями, інтелектуального аналізу даних і машинного навчання, які використовуються для отримання компетентних знань. Evaluation of organizations with multi-level hierarchical structure (project groups, groups of researchers, military units, expert commissions, medical rapid response teams) is an urgent problem today, the solution of which will allow to organize their rapid dynamic adaptation in a new way to perform important operational tasks, as well as training team members in the absence of sufficient competencies, skills and experience. The assessment also reveals the strengths and weaknesses of both the team as a whole and each participant, which in the future provides opportunities for their further growth. Evaluation of the work of teams is complex, including both professional knowledge in a certain field of activity and team management skills (so-called leadership skills, the ability to organize the execution of tasks and the achievement of goals and obtaining positive work results). In order to improve the evaluations of hierarchical aggregated teams we propose the use of ontological approach: domain ontology defines knowledge about relevant combinations of competencies for hierarchical team positions. Information about competencies of applicants is acquired by portfolio analysis (resume, certificates, diplomas, merits, etc.). More complex tasks can use a hierarchical set of ontologies and Web-services for obtaining evaluation results and recommendations for competence improving for various sublevels. We describe the step-by-step method of team evaluation that use elements of semantic similarity between different information objects for the matching of applicants and equipment with team positions. Proposed approach to hierarchical team evaluation is a component for integrated multi-criteria decision-making oriented on some special case of user tasks: the set of evaluation criteria is determined by task and can be built on the basis of domain knowledge, but importance of particular criterion is defined by state of environment in different points in time. This approach can be expanded by means and methods of knowledge management, Data mining and machine learning used for acquisition of competence knowledge.

Cet article étudie la relation d’équivalence sémantique entre les verbes néologiques par emprunt et les verbes déjà existants en français (booster/stimuler, whatsapper/communiquer). Une confrontation des schémas prédicatifs des paires verbales sélectionnées mène à déterminer des zones de recouvrement sémantique entre les unités et permet de distinguer différents cas de ressemblance, englobant d’une part l’identité totale et d’autre part la proximité sémantique à des degrés variés., This article studies the relationship of semantic equivalence between borrowedneological verbs and verbs already existing in French (booster/stimuler, whatsapper/communiquer). A comparison of the predicative patterns of the verbal pairs selected identifies areas of semantic overlap between the units and makes it possible to distinguish different degrees of resemblance, ranging from total identity to semantic proximity of varying degrees.

francaisBetween interference and convergence: the problem of suffix borrowing in ancient Italy. The case of etr. -θe/-te and Lat. -t(i)-. The paper focuses on one of the rare cases of morpheme borrowing we can spot between Etruscan and Latin, the suffixes ‑θe and ‑t(i)‑, whose phonetic and semantic proximity implies a probable common origin. It reviews all the Etruscan data about -θe (-te) and Latin and Sabellic ones about ‑t(i)‑, in order to determine the possible origin of the loan and what can tell us such a morphological borrowing about the bilingualism of the populations of central Italy in the first millennium BC. italianoL’article s’interesse a l’un des rares cas d’emprunts de morphemes attestes entre l’etrusque et le latin, celui des suffixes ‑θe et ‑t(i)‑, dont la proximite phonetique et semantique implique une probable origine commune. On y reprend l’ensemble des donnees etrusques concernant le suffixe ‑θe/‑te et latines concernant le suffixe ‑t(i)‑, eclairees par les donnees sabelliques, afin de determiner le sens eventuel de l’emprunt et ce que ce type d’emprunt morphologique a a nous dire sur le bilinguisme des populations de l’Italie centrale au Ier millenaire av. J.-C.

Development of an educational resource allows you to organize an effective learning process taking into account the semantic representation of resources. This article shows the result of an experimental study of the developed system, showing an increase in the quality of education and efficiency due to the semantic representation of resources, also creates conditions for the digitalization of the educational process. The work also used methods to prove a hypothesis by experiment. Using local reference methods such as the Dice measure, Jaccard index and Kulchinsky measure, and the Pearson correlation method, it is possible to characterize the existence of a linear relationship between two topics.

Este trabalho sintetiza a análise de artigos da base científica Web of ScienceTM, que relacionassem os termos "intenção empreendedora" e "comportamento empreendedor". Foram selecionados artigos em inglês e feita uma análise em relação à proximidade semântica de palavras utilizando o software NVIVO. No total, foram obtidos 85 artigos sobre ?intenção empreendedora? e 86 sobre ?comportamento empreendedor?. Após análise qualitativa dos dados, extraiu-se uma estrutura do tipo nuvem que representava em, termos visuais, a quantidade de palavras relacionadas ao tema em estudo. A contribuição deste estudo se volta para oferecer um panorama das publicações sobre a temática abordada com informações devidamente tabuladas. Assim, as características das publicações sobre essa temática podem ser organizadas

The increasing availability of textual corpora and data fetched from social networks is fuelling a huge production of works based on the model proposed by psychologist Robert Plutchik, often referred simply as the ``Plutchik Wheel''. Related researches range from annotation tasks description to emotions detection tools. Visualisation of such emotions is traditionally carried out using the most popular layouts, as bar plots or tables, which are however sub-optimal. The classic representation of the Plutchik's wheel follows the principles of proximity and opposition between pairs of emotions: spatial proximity in this model is also a semantic proximity, as adjacent emotions elicit a complex emotion (a primary dyad) when triggered together; spatial opposition is a semantic opposition as well, as positive emotions are opposite to negative emotions. The most common layouts fail to preserve both features, not to mention the need of visually allowing comparisons between different corpora in a blink of an eye, that is hard with basic design solutions. We introduce PyPlutchik, a Python library specifically designed for the visualisation of Plutchik's emotions in texts or in corpora. PyPlutchik draws the Plutchik's flower with each emotion petal sized after how much that emotion is detected or annotated in the corpus, also representing three degrees of intensity for each of them. Notably, PyPlutchik allows users to display also primary, secondary, tertiary and opposite dyads in a compact, intuitive way. We substantiate our claim that PyPlutchik outperforms other classic visualisations when displaying Plutchik emotions and we showcase a few examples that display our library's most compelling features., 18 pages, 13 figures. Submitted to IEEE for possible publication; copyright may change

Mining trajectory data to find interesting patterns is of increasing research interest due to a broad range of useful applications, including analysis of transportation systems, location-based social networks, and crowd behavior. The primary focus of this research is to leverage the abundance of trajectory data to automatically and accurately learn latent semantic relationships between different geographical areas (e.g., semantically correlated neighborhoods of a city) as revealed by patterns of moving objects over time. While previous studies have utilized trajectories for this type of analysis at the level of a single geographical area, the results cannot be easily generalized to inform comparative analysis of different geographical areas. In this paper, we study this problem systematically. First, we present a method that utilizes trajectories to learn low-dimensional representations of geographical areas in an embedded space. Then, we develop a statistical method that allows to quantify the degree to which real trajectories deviate from a theoretical null model. The method allows to (a) distinguish geographical proximity to semantic proximity, and (b) inform a comparative analysis of two (or more) models obtained by trajectories defined on different geographical areas. This deep analysis can improve our understanding of how space is perceived by individuals and inform better decisions of urban planning. Our experimental evaluation aims to demonstrate the effectiveness and usefulness of the proposed statistical method in two large-scale real-world data sets coming from the New York City and the city of Porto, Portugal, respectively. The methods we present are generic and can be utilized to inform a number of useful applications, ranging from location-based services, such as point-of-interest recommendations, to finding semantic relationships between different cities.

Установление семантической близости между набором слов является важной задачей, решение которой позволит продвинуться в ряде направлений, связанных с коммуникацией ипередачей информации. Для выявления семантической близости слов предлагается использовать визуализацию их векторного представления споследующей интерпретацией. Описан алгоритм предобработки исходного текста для достижения необходимого результата., Establishing semantic proximity between a set of words is an important task, the solution of which will allow us to advance in a number of areas related to communication and information transfer. To identify the semantic proximity of words, it is proposed to use the visualization of their vector representation with subsequent interpretation. The algorithm of preprocessing the source text to achieve the desired result is described., №1 (2020)

Providing personalized information to a wide audience, e.g., digital signage requires approaches and technologies different from those used for individual users. Such systems have to take care of user confidentiality and dynamically assess their interests. The paper developed earlier presented works on this topic and concentrates on the description of the ontological modeling of user interests and preferences as well as major components of such system. The developed ontological model intensively uses various types of relationships in order to provide for quantitative evaluation the user interest in a particular topic and semantic proximity of interests. It also enables generalization of interests, which is helpful for finding common interests for small amount of people, who are not related to each other and unlikely have much in common. Proposed structure of granular anonymous user profiles makes it possible to take care of the user confidentiality and to dynamically adapt to new information.

Recently, network embedding has attracted a surge of attention due to its ability to automatically extract features from graph-structured data. Though network embedding method has been intensively studied, most of the existing approaches pay attention to either structures or attributes. In this paper, we propose a novel attributed network embedding method based on a Siamese neural network, named SANE, to capture both the network structure and node attribute information in a principled way. Specifically, to preserve local semantic proximity, we adopt a Siamese neural network, which can directly learn the similarity of paired nodes with their attributes as input. Then, a skip-gram module is connected with the final shared hidden layer to capture high-order proximity based on the latent representation of node attributes. Thus, we can learn the complex interrelations between nodes. Empirically, we evaluate our model on several real-world datasets and the experimental results have verified the effectiveness of our proposed approach.

Актуальной проблемой интеграции ИС является анализ, согласование и отображение их онтологий, которые построены разными способами. В статье приводится краткий обзор процесса автоматического построения первоначальных версий онтологий предметной области. Предложен подход к семантической интеграции неоднородных онтологий сложных информационных систем. Основная идея заключается в рассмотрении онтологий из разных предметных областей и построенных разными способами. В статье описан метод вычисления семантической близости концептов, который позволяет количественно оценить сходство между понятиями. Соответствия между элементами (концептами) онтологий разделяются на несколько составляющих: лексическую, атрибутивную и реляционную. Для каждого концепта одной онтологии формируется множество релевантных семантических концептов другой онтологии. С целью ранжирования элементов результирующего множества предлагается определять пороговые значения меры близости. В работе предложен метод классификации значений близости концептов для установления их корректного отображения. Следует отметить, что основное ядро онтологии адекватно по семантики и повторяет построенную экспертами модель. Высокая точность результатов объясняется применением расширенного набора вариантов взаимного позиционирования концептов. Модель процесса интеграции применима к широкому кругу предметных областей и не сложна в реализации., The actual problem of integrating IP is analysis, matching and mapping of their ontologies, which are constructed in different ways. The article provides a brief overview of the process of automatic construction of the initial versions of domain ontologies. An approach to the semantic integration of heterogeneous ontologies of complex information systems is proposed. The basic idea is to consider ontologies from different subject areas, and constructed in different ways. The article describes a method for calculating the semantic proximity of concepts, which allows one to quantify the similarity between concepts. The correspondences between the elements (concepts) of ontologies are divided into several components: lexical, attributive and relational. For each concept of one ontology, a set of relevant semantic concepts of another ontology is formed. For the purpose of ranking the elements of the resulting set, it is proposed to determine the threshold values ​​of the proximity measure. A method for classifying the levels of proximity of concepts to establish their correct mapping is proposed. It should be noted that, excluding some error in comparing ontology concepts with the help of the software system, the main ontology core is adequately based on semantics and repeats the model constructed by experts. The high accuracy of the results is due to the use. The model of the integration process is applicable to a wide range of subject areas and is not difficult to implement., №1(25) (2019)

Usage similarity estimation addresses the semantic proximity of word instances in different contexts. We apply contextualized (ELMo and BERT) word and sentence embeddings to this task, and propose supervised models that leverage these representations for prediction. Our models are further assisted by lexical substitute annotations automatically assigned to word instances by context2vec, a neural model that relies on a bidirectional LSTM. We perform an extensive comparison of existing word and sentence representations on benchmark datasets addressing both graded and binary similarity. The best performing models outperform previous methods in both settings., *SEM 2019

Concept information can be expressed by text, images or general objects which semantic meaning is clear to a human in a specific cultural context. For a computer, when available, text with its semantics (e.g., metadata, comments, captions) can convey more precise meaning than images or general objects with low-level features (e.g., color distribution, shapes, sound peaks) to extract the concept underlying the object. Among semantic measures, web-based proximity measures e.g., confidence, PMING, NGD, Jaccard, Dice, are particularly useful for concept evaluation, exploiting statistical data provided by search engines on terms and expressions provided in texts associated with the object.

The paper analyzes the concepts and relations between them in the RuWordNet digital thesaurus of the Russian language. Semantically related words automatically extracted from the corpus of texts are compared with the words of the thesaurus. The characteristic cases of information gap (omission of word meanings and semantic relationships) in the thesaurus are revealed, their causes of a computer and cognitive nature are described. Recommendations on improving the RuWordNet thesaurus are given.

Semantic proximity search on heterogeneous graph is an important task, and is useful for many applications. It aims to measure the proximity between two nodes on a heterogeneous graph w.r.t. some given semantic relation. Prior work often tries to measure the semantic proximity by paths connecting a query object and a target object. Despite the success of such path-based approaches, they often modeled the paths in a weakly coupled manner, which overlooked the rich interactions among paths. In this paper, we introduce a novel concept of interactive paths to model the inter-dependency among multiple paths between a query object and a target object. We then propose an Interactive Paths Embedding (IPE) model, which learns low-dimensional representations for the resulting interactive-paths structures for proximity estimation. We conduct experiments on seven relations with four different types of heterogeneous graphs, and show that our model outperforms the state-of-the-art baselines.

Studies have shown that generating errors prior to studying information (pencil-?) can improve target retention relative to passive (i.e., errorless) study, provided that cues and targets are semantically related (pencil-ink) and not unrelated (pencil-frog). In two experiments, we manipulated semantic proximity of errors to targets during trial-and-error to examine whether it would modulate this error generation benefit. In Experiment 1, participants were shown a cue (band-?) and asked to generate a related word (e.g., drum). Critically, they were given a target that either matched the semantic meaning of their guess (guitar) or mismatched it (rubber). In Experiment 2, participants studied Spanish words where the English translation either matched their expectations (pariente-relative) or mismatched it (carpeta-folder). Both experiments show that errors benefit memory to the extent that they overlap semantically with targets. Results are discussed in terms of the retrieval benefits of activating related concepts during learning.

В работе используется многомерный типологический подход, позволяющий создать типологию «потребностных мотивов» и соответствующую ей типологию «мотивов женской лжи». При построении классификаций исследователями используется полнота разбиения целого и упорядоченность элементов (частей в целом), что является основанием базисного подхода к изучению психологической информации о личности. Результатом применения этого подхода выступает построение многомерного базиса как объединения нескольких одномерных базисов. При построении типологий создаются благоприятные возможности полного описания психологической информации об объектах (понятиях, явлениях), что является основанием целостного подхода. В итоге создается возможность системного структурирования информации о личности, то есть функциональной связанности компонентов-типов. Многомерный типологический подход имеет следующие признаки: во-первых, совокупность знаний, отраженных в концепции, приводится в систему на основании проверенных на практике достоверных результатов; во-вторых, предлагаемый подход обладает прогностическими возможностями, так как позволяет увидеть пути дальнейшего развития личностных характеристик. Критериями концепции являются целостность, системность, базисность. В концепции были выбраны и апробированы следующие методы: подбор психологических характеристик, их обобщение, сравнение и отождествление с характеристиками базового эталона, выявление профиля изучаемого качества, свойства субъекта. Главные принципы: индуктивно-дедуктивного исследования, «правильности» разбиения множества на подмножества, полярной дихотомичности, единой полярной дихотомической симметрии, образования «мерности» в типологии, упорядоченности, типологической «всеохватности», эталонного типологического базиса, изоморфизма, семантической близости, гомоморфизма, операционализации понятия. В данной статье проверяется на «базисность» типология женской лжи, описание которой дается в произведении Л. Е. Улицкой «Сквозная линия». Для этого на основе принципа семантической близости устанавливается взаимно-однозначное соответствие («типологический изоморфизм») типологии женской лжи с типологией «потребностных мотивов» и с типологией личности по Д. Голланду. В данном случае используется метод «базисного наложения», который обеспечивает смысловую общность этих трех типологий., In this paper, we use a multidimensional typological approach that allows us to create a typology of «need motives» and the corresponding typology of «motives of women's lies». In the construction of classifications, researchers use the completeness of the whole division and ordering of elements (parts as a whole), which is the basis of the basic approach to the study of psychological information about the person. The result of the application of this approach is the construction of a multidimensional basis as a Union of several one-dimensional bases. In the construction of typologies there are favorable opportunities for a full description of psychological information about objects (concepts, phenomena), which is the basis of a holistic approach. This creates the possibility of systemic structuring of information about the individual, i.e. functional connectivity component-types. The multidimensional typological approach has the following features: firstly, the set of knowledge reflected in the concept is brought into the system on the basis of proven reliable results, and secondly, the proposed approach has prognostic capabilities, since it has been tested in practice. It allows you to see the ways of further development of personal characteristics. The criteria of the concept are integrity, consistency, basis. The following methods were chosen and tested in the concept: selection of psychological characteristics, their generalization, comparison and identification with the characteristics of the basic standard, identification of the profile of the studied quality, the properties of the subject. Main principles: the inductive-deductive research, the «correct» partitioning of the set into sub-sets, polar dichotomy, a single dichotomous polar symmetry, the formation of «dimension» in the typology, governance, typological «inclusiveness», the reference of the typological basis of isomorphism, semantic proximity, of homomorphism, operationalization of the concept. In this article, the typology of women's lies is checked for «basis», the description of which is given in the work by L. E. Ulitskaya «Through Line». For this purpose, on the basis of the principle of semantic proximity is established a one-to-one correspondence («typological isomorphism») of the type of female lie with the typology of «need motives» and with the typology of personality by D. Holland. In this case, the method of «basic overlay» is used, which provides a semantic commonality of these three typologies.

This paper presents one of the ways solution to the problem of lexical ambiguity: the using methods of extraction and analysis of semantic relations between lexical units. The proposed methods are divided into groups, among which identified and discussed the most popular. As well, special attention is paid to the methods of k-nearest neighbor and mutual k-nearest neighbor, which allow to introduce the problem of extracting semantic proximity as one of the classification tasks.

This research includes the investigation, design and experimentation of models and measures of semantic and structural proximity for knowledge extraction and link prediction. The aim is to measure, predict and elicit, in particular, data from social or collaborative sources of heterogeneous information. The general idea is to use the information about entities (i.e. users) and relationships in collaborative or social repositories as an information source to infer the semantic context, and relations among the heterogeneous multimedia objects of any kind to extract the relevant structural knowledge. Contexts can then be used to narrow the domains and improve the performances of tasks such as disambiguation of entities, query expansion, emotion recognition and multimedia retrieval, just to mention a few.

This paper analyses the method of zonal correlation text analysis based on the comparison of the distribution of word counts in the J 1 zones of two or more texts. The compared texts are divided into the J 0, J 1, and J 2 zones in accordance with the previously developed interpretation of Bradford's law. A comparative analysis of the parameters of words that are contained in the J 1 text zones is performed. The distance, which points to the degree of semantic proximity of the compared texts, is calculated. The described method can be applied for automatic classification and author attribution of texts. In addition, it allows one to obtain adequate results based on a limited number of parameters.

In traditional database applications, queries intend to retrieve data satisfying precise conditions. As a result, thousands of data can be retrieved (overabundant answer) or, even worse, no data at all (empty answer). In both cases, the queries must be reformulated to produce more significant results and, typically, many related queries are submitted by a user before he can be finally satisfied. To overcome these problems, this paper proposes a unified solution in the framework of flexible queries with fuzzy semantics. This solution, based on the concept of semantic proximity and implemented in a tool for flexible query answering, allows the automatic reformulation of queries with empty or overabundant answers.