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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.

The review article discusses the key problems of semantic organization of the mental lexicon. It is understood as a dynamic, cognitively organized semantic network of lexical units. The paper presents the characteristics of the main models of semantic organization of mental vocabulary, highlights the advantages and disadvantages of each approach. It is noted that currently connectionist models are developing most actively. Among them there are the following: the small world network, which considers a certain fragment of the lexicon; thesaurus models that combine all units of the lexicon; computational and distributive models that build relationships between a large number of units based on corpus data about shared usage. The author analyses the sources of information about the organization of the mental lexicon, among which the leading position is occupied by the results of associative experiments and priming. It is revealed that research is also carried out on the subjects’ intuitive assessments of word similarity, analysis of unintentional speech errors. Neuropsychological technologies are used to study the mental lexicon. The problem of semantic relations in the mental lexicon, which are described using a “spatial” metaphor, is discussed separately: semantic proximity is considered as a distance or a vector.

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.

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.

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.

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.

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.

The paper proposes a method of constructing solutions based on the experience represented by cartographic images — centers and transformations of situations that do not change their meaning. The method is based on the metatransformation of images. The goal of metatransformation is to construct new situations by known precedents. The usage of metatransformations allows to evaluate the semantic proximity of situations and adapt the earlier decisions in a special way. The factors that determine the reliability of the generated solutions are analyzed.

Given ubiquitous graph data such as the Web and social networks, proximity search on graphs has been an active research topic. The task boils down to measuring the proximity between two nodes on a graph. Although most earlier studies deal with homogeneous or bipartite graphs only, many real-world graphs are heterogeneous with objects of various types, giving rise to different semantic classes of proximity. For instance, on a social network two users can be close for different reasons, such as being classmates or family members, which represent two distinct classes of proximity. Thus, it becomes inadequate to only measure a “generic” form of proximity as previous works have focused on. In this paper, we identify metagraphs as a novel and effective means to characterize the common structures for a desired class of proximity. Subsequently, we propose a family of metagraph-based proximity, and employ a supervised technique to automatically learn the right form of proximity within its family to suit the desired class. As it is expensive to match (i.e., find the instances of) a metagraph, we propose the novel approaches of dual-stage training and symmetry-based matching to speed up. Finally, our experiments reveal that our approach is significantly more accurate and efficient. For accuracy, we outperform the baselines by 11% and 16% in NDCG and MAP, respectively. For efficiency, dual-stage training reduces the overall matching cost by 83%, and symmetry-based matching further decreases the cost of individual metagraphs by 52%.

This study focuses on scientific writing and aims at improving verbal patterns teaching for L2 learners. Using a French corpus of scientific articles in humanities and social sciences (Scientext), we have collected and modelized lexicosyntactic patterns for the verb montrer and two of its synonyms. We aim at extracting specific structures for those three semantically close verbs, assuming that their semantic proximity will also be revealed in similar syntactic properties. We thus create a set of verbal patterns, with cooccurrence informations, to combine them to a tool designed to help L2 learners in their scientific writing.

This paper describes a software tool that implements an instancebased schema matching technique for OWL dialects. The technique is based on a matching algorithm that depends on the definition of similarity functions that evaluate the semantic proximity of elements from two different schemas. The tool is engineered to accommodate different similarity functions and variations of the matching algorithm, thereby facilitating experimentation with alternative setups.

文章に付与されたキーワード集合のような内容語 (ターム) の並びを「タームリスト」と呼ぶ. 本論文では, 翻訳先言語のコーパスのみを用いて, 各タームに対する訳語候補のなかから最適なものを選択する「翻訳多義解消」の新たな方法を提案する. 本手法では, 各タームに対する訳語候補を一つずつ集めてできる組み合わせのうち, 含まれる訳語同士の意味的関連性が最も高い組を選択する. 単語間の意味的関連性の尺度は各単語が翻訳先言語コーパスにおいてどの程度近い文脈に出現するかを数値化したものである. 翻訳実験の結果, 翻訳多義のある単語に対する平均正解率77.4%を達成した.

Chapter 5 has laid out an approach to identify the semantic proximity between two named entities like brand and product names, locations, and so forth. The underlying chapter will now work on this model and put it to use for the identification and recommending of technology synergies. That is, given a set of technologies associated with different business units, we want to know which are the pairs of technologies that exhibit the largest synergies.

The two preceding chapters have demonstrated that classification taxonomies can be put to use in improving recommender systems in terms of the quality of their recommendations. The taxonomy we resorted to for all the empirical evaluations was the one from Amazon.com. Now we want to give an example how Web 2.0 taxonomies, having been crafted by collective efforts of several thousands of volunteering editors, can likewise be used to these ends.

In this paper, we propose an in-situ Crowd-aware Sensing Platform, called "CrowdMon", which envisions the cooperation among mobile users in highly crowded urban areas such as metro and square. CrowdMon establishes a spontaneous connection from co-located users in a semantic proximity and enables them to share contextual information such as location, ambient music, and mood of places. To the best of our knowledge, CrowdMon is the first attempt to support crowd-aware services at a platform level. We show interesting use cases of CrowdMon and an initial system design to realize the crowd-based context sharing.

In this paper, we address the issue of multimedia content replication over peer-to peer networks. We use the concept of semantic proximity that exploits the file exchange patterns exhibited among peer users so as to decompose the network into semantic clusters. Through the optimization of the search process that exploits semantic associations between peer nodes, we derive a semantic organization of the network which is then used for applying efficient cluster-based content replication strategies. Two different schemes are examined; the unrestricted and the restricted approach. The first one distributes multimedia content within peers of a cluster so that the average hop distance among nodes and objects weighted by the object popularity is minimized. This approach does not take into account any Quality of Service (QoS) guarantees. On the other handm the second scheme iteratively partitions the nodes of a cluster during each object replication based on a QoS violation criterion. The proposed algorithms are experimentally evaluated and compared with other approaches to demonstrate the efficiency of our proposed schemes.

In modern data retrieval systems the main problem is how to store data properly and how to reach a high speed of processing of users researching requests. It is worth noting that the main point of information retrieval systems is not only research of exact conformity of requesting information, but also the point of researching of relevant information, where the level of relevance could be determined as the level of its semantic proximity to the researching request. Thus researching requests in such a kind of systems must be based on natural language in where initial information is formulated.

In this paper we present an empirical study of a workload gathered by crawling the eDonkey network --- a dominant peer-to-peer file sharing system --- for over 50 days.We first confirm the presence of some known features, in particular the prevalence of free-riding and the Zipf-like distribution of file popularity. We also analyze the evolution of document popularity.We then provide an in-depth analysis of several clustering properties of such workloads. We measure the geographical clustering of peers offering a given file. We find that most files are offered mostly by peers of a single country, although popular files don't have such a clear home country .We then analyze the overlap between contents offered by different peers. We find that peer contents are highly clustered according to several metrics of interest.We propose to leverage this property by allowing peers to search for content without server support, by querying suitably identified semantic neighbours . We find via trace-driven simulations that this approach is generally effective, and is even more effective for rare files. If we further allow peers to query both their semantic neighbours, and in turn their neighbours' neighbours, we attain hit rates as high as over 55% for neighbour lists of size 20.

Recent measurement studies of file-sharing peer-to-peer networks have demonstrated the presence of semantic proximity between peers and between shared files. In this paper we tackle the problem of exploiting the semantic locality of peer requests to improve the performance of a P2P network by the use of semantic caches. Such caches group together peers with similar interests as well as files with similar request patterns. The resulting two-level caching infrastructure can be integrated in a very natural way with the super-peer concept. The super-peers in our system cache pointers to files recently requested by their client peers. The client peers, on the other hand, keep caches of super-peers that answered most of their requests in the past. As a consequence, peers with similar interests are grouped (clustered) under the same super-peers, and also files with similar request patterns are indexed by the same super-peers. We discuss the design choices and optimizations of the presented model. We also evaluate our system versus the network that uses only one level of semantic caches.

In this chapter, we demonstrate that aligning a sentence with its translation is not fundamentally different from finding a sentence on the same topic in the target corpus, using the source sentence as a query. The two processes are based on the semantic proximity of two sentences in different languages, and their major difference is that information retrieval only needs to insure that the sentence found contains most of the information of the query, whereas sentence alignment requires that the parts that are not common to both languages be as small as possible. A crosslingual query system can be used to obtain candidates for sentence alignment, provided that the measure of semantic proximity slightly modified. More classical techniques can be used, taking sequential order into account, but our approach is very robust to text desynchronization, such as missing text segments in one language, or texts such as glossaries or indexes that are not in the same order in different languages.

The paper presents a knowledge-based model for processing network-like relationships between scientists in bibliographic databases. The need to reason about scientific structures has turned out to be a remaining problem in the field of person-related Information Retrieval (IR) considering research contexts. The method, which is introduced here, determines science-structural relationships between all scientists in sample domains of a database including their semantic proximity. As an expressive description of the “world” studied at an instantaneous point of time, such networks of personal relations can be used to draw inferences concerning the importance of scientists for a certain topic in regard to their structural position within a complete “research landscape”.

In a multidatabase system, schematic conflicts between two objects are usually of interest only when the objects have some semantic affinity. In this paper we try to reconcile the two perspectives. We first define the concept of semantic proximity and provide a semantic taxonomy. We then enumerate and classify the schematic and data conflicts. We discuss possible semantic similarities between two objects that have various types of schematic and data conflicts. Issues of uncertain information and inconsistent information are also addressed.

The interference of incongruous attribute names on attribute identification was studied with two different attributes, color and direction, and with two modes of identifying response, verbaland nonverbal. Nonverbal responses made for less interference under all conditions. Semantic proximity produced more interference with color than with direction. Interference in this sort of task may reflect characteristic ways in which incoming attribute information is processed.