Semantic Conversion Research Articles

Exploring English Translation Strategies Oriented by Big Data Technology

Abstract English translation is not a simple task, and it needs to be translated on the basis of different languages and cultures in order to get the ideal translation effect and promote cross-cultural communication between different languages. In this paper, with the help of web crawler technology to crawl the Chinese-English translation corpus data, and use the inverse document frequency and dynamic planning ideas to align the corpus data, the Chinese-English translation corpus is established. The LDA model is used to mine subject information in the Chinese-English translation corpus, and edge distribution estimation is used to construct the English translation model. For the application of the translation model and the Chinese-English translation corpus in improving the quality and effect of English translation, the word and sentence features, target word blocks, and the comparison of language features of human-computer translation from the Chinese-English corpus are verified in various aspects. The results show that the class/formant ratio of the source language in the corpus is between 56.73% and 75.84%, its average word length fluctuates in the range of [3.91,5.62], and the target word chunks of the English text are mainly based on nominal and verbal structures. The English translation model has a less significant discrepancy in other language features than human translation, except for the difference of 4.526 between sentence lengths. English translation needs to adopt a reasonable annotation method, semantic conversion, and technical tools to improve the quality of English translation.

Translating advertisements from Chinese to English: Strategies and methods

This study explores the strategies and methods of translating advertisements from Chinese to English. It adopts a functional approach to translation studies, encompassing the Skopos theory and equivalent translation theory. The translation strategies are divided into semantic, structural, language, and rhetorical levels. At the semantic level, the use of literal translation, liberal translation, and semantic conversion is employed to convey both the literal meaning and intended meaning of the advertisements. At the structural level, supplementary translation, condensed translation, and the division and merging of sentences are utilized to adjust the structure to meet the translation requirements. At the language level, extensive use of adjectives, frequent use of imperative verbs, and personal pronouns are employed to highlight features, prompt actions, and establish connections. At the rhetorical level, the use of puns, simulation, personification and exaggeration is employed to enhance interest, convey messages, and imbue products with human-like qualities. By employing a functional approach and various translation strategies, advertisements can be effectively translated, capturing the interest and resonance of the target audience. However, this study has certain limitations in terms of sample selection and research scope. Future research can further explore additional translation strategies and methods to improve the effectiveness and quality of advertisement translation.

Potential Target Discovery and Drug Repurposing for Coronaviruses: Study Involving a Knowledge Graph-Based Approach.

The global pandemics of severe acute respiratory syndrome, Middle East respiratory syndrome, and COVID-19 have caused unprecedented crises for public health. Coronaviruses are constantly evolving, and it is unknown which new coronavirus will emerge and when the next coronavirus will sweep across the world. Knowledge graphs are expected to help discover the pathogenicity and transmission mechanism of viruses. The aim of this study was to discover potential targets and candidate drugs to repurpose for coronaviruses through a knowledge graph-based approach. We propose a computational and evidence-based knowledge discovery approach to identify potential targets and candidate drugs for coronaviruses from biomedical literature and well-known knowledge bases. To organize the semantic triples extracted automatically from biomedical literature, a semantic conversion model was designed. The literature knowledge was associated and integrated with existing drug and gene knowledge through semantic mapping, and the coronavirus knowledge graph (CovKG) was constructed. We adopted both the knowledge graph embedding model and the semantic reasoning mechanism to discover unrecorded mechanisms of drug action as well as potential targets and drug candidates. Furthermore, we have provided evidence-based support with a scoring and backtracking mechanism. The constructed CovKG contains 17,369,620 triples, of which 641,195 were extracted from biomedical literature, covering 13,065 concept unique identifiers, 209 semantic types, and 97 semantic relations of the Unified Medical Language System. Through multi-source knowledge integration, 475 drugs and 262 targets were mapped to existing knowledge, and 41 new drug mechanisms of action were found by semantic reasoning, which were not recorded in the existing knowledge base. Among the knowledge graph embedding models, TransR outperformed others (mean reciprocal rank=0.2510, Hits@10=0.3505). A total of 33 potential targets and 18 drug candidates were identified for coronaviruses. Among them, 7 novel drugs (ie, quinine, nelfinavir, ivermectin, asunaprevir, tylophorine, Artemisia annua extract, and resveratrol) and 3 highly ranked targets (ie, angiotensin converting enzyme 2, transmembrane serine protease 2, and M protein) were further discussed. We showed the effectiveness of a knowledge graph-based approach in potential target discovery and drug repurposing for coronaviruses. Our approach can be extended to other viruses or diseases for biomedical knowledge discovery and relevant applications.

Philosophy of meaning and conceptualization

Philosophy reveals the foundations of conceptualization as a generic form of spirituality, and philosophy itself is semantic. As an active state of the human spirit, conceptualization leads to development of semiotically shaped meanings, that is, the reduction of information to human and value dimensions, which is often expressed figuratively. The genesis of meanings has two opposite to each other beginnings — down-to-earth and sublime: a review of acquired experience and attribution to the sphere of due and ideas. One of the objects of philosophical reflection should be a phenomenon that can be called a semantic conversion of human existence, that is, virtual reconstruction in a semantic evaluative and instructive form of social, generational and personal and individual events, virtual humanization of the universe. Both the profitable and flawed sides of social evolution are recorded in meanings, characters are depicted in a semantic way, from which subjects learn lessons. Individuals live their own lives and virtually meaningfully can live the lives of other personalities. The human world is enriched with typical semantic realities. The main meaningful points of the philosophy of meaning are given at the end of the article.

Philosophy of meaning and conceptualization

Philosophy reveals the foundations of conceptualization as a generic form of spirituality, and philosophy itself is semantic. As an active state of the human spirit, conceptualization leads to development of semiotically shaped meanings, that is, the reduction of information to human and value dimensions, which is often expressed figuratively. The genesis of meanings has two opposite to each other beginnings — down-to-earth and sublime: a review of acquired experience and attribution to the sphere of due and ideas. One of the objects of philosophical reflection should be a phenomenon that can be called a semantic conversion of human existence, that is, virtual reconstruction in a semantic evaluative and instructive form of social, generational and personal and individual events, virtual humanization of the universe. Both the profitable and flawed sides of social evolution are recorded in meanings, characters are depicted in a semantic way, from which subjects learn lessons. Individuals live their own lives and virtually meaningfully can live the lives of other personalities. The human world is enriched with typical semantic realities. The main meaningful points of the philosophy of meaning are given at the end of the article.

Leveraging hierarchical semantic‐emotional memory in emotional conversation generation

AbstractHandling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the generation of semantically coherent and emotionally reasonable responses. However, most previous works generate emotional responses solely from input posts, which do not take full advantage of the training corpus and suffer from generating generic responses. In this study, we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation (called HSEMEC), which can learn abstract semantic conversation patterns and emotional information from the large training corpus. The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation. Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation. For reproducibility, we release the code and data publicly at: https://github.com/siat‐nlp/HSEMEC‐code‐data.

事件域認知模型(ECM)視角下的反義複合詞內部語義屬性研究

This paper takes 264 antonym compound words collected in Modern Chinese Dictionary (Seventh Edition) as the research object. Firstly, this paper classifies and counts antonym compound words according to the part of speech of antonym compound morphemes, and found that the whole part of speech of antonym compound words and the part of speech of word-building morphemes are basically the same, only part of them lead to the phenomenon of the conversion and concurrent conversion, which is mainly created by semantic conversion and grammatical conversion. Secondly, according to the relationship between the meaning of the morpheme and the word, antonym compound words can be divided into additive type, partial (meaning) type, extended type and transferred type. In the meanwhile, this paper mainly uses the Event-domain Cognitive Model(ECM), the intrinsic metaphor and metonymy mechanism to explain the part-of-speech conversion and meaning change of antonyms. Applying ECM to have cognitive analysis of part-of-speech conversion and semantic changes of antonym compounds can not only help learners to use antonym compound words more accurately, but also verify the explanatory power of ECM cognitive model to part-of-speech conversion and semantic changes.

Visual Relationship-Based Identification of Key Construction Scenes on Highway Bridges

Highway bridges play an important role in traffic construction; however, accidents caused by bridge construction occur frequently, resulting in significant loss of life and property. The identification of bridge construction scenes not only keeps track of the construction progress, but also enables real-time monitoring of the construction process and the timely detection of safety hazards. This paper proposes a deep learning method in artificial intelligence (AI) for identifying key construction scenes of highway bridges based on visual relationships. First, based on the analysis of bridge construction characteristics and construction process, five key construction scenes are selected. Then, by studying the underlying features of the five scenes, a construction scene identification feature information table is built, and construction scene identification rules are formulated. Afterward, a bridge key construction scene identification model (CSIN) is built; this model comprises target detection, visual relationship extraction, semantic conversion, scene information fusion, and identification results output. Finally, the effectiveness of the proposed method is verified experimentally. The results show that the proposed method can effectively identify key construction scenes for highway bridges with an accuracy rate of 94%, and enable the remote intelligent monitoring of highway bridge construction processes to ensure that projects are carried out safely.

Temporal interpretation of the world in dagestanian languages

The article deals with certain aspects of conceptualization of time, predetermined by the national specificity of the world perception. The topicality of the given study is accounted for by the extremely differentiated verbalization of space and time peculiar to the Dagestanian languages. The aim of the article is to identify the common origin of temporal and spatial vocabulary in the Dagestanian languages. To achieve the goal, the authors used a comparative-typological method, as well as a cognitive approach to reveal the role of national consciousness in conceptualization of time. The article also examines some aspects of national peculiarities of time conceptualization aimed at finding out the genetic connection between temporal and space vocabulary. The data under study allows claiming that in the basis of most lexemes with temporal meaning lies a semantic component denoting “space”. Temporal lexemes representing cycling time were developed by means of objectivation of the heavenly bodies’ movement. Meanwhile temporal vocabulary representing line time was formed by means of semantic conversion which allowed transforming local adverbs onto temporal. The most important results obtained by the authors are as follows: (a) the conceptualization of time in the Dagestanian languages is realized on the basis of spacial parameters, so the vocabulary of temporal semantics are formed on the basis of spatial vocabulary; b) the temporal vocabulary of the Dagestanian languages has evolved along the lines of spatial representations > space-time representations> time representations. The results achieved are important for the cognitive studies of the Dagestanian languages, as well as for the further description of the space and time conceptualization in the world language pictures.

Типы семантических преобразований дендронимов в поэтических текстах А. В. Ширяевца

The article deals with the tropeic word usage of dendronyms and their symbolic meanings as ways of semantic transformations of dendronyms in the poetic texts of A.V. Shiryaevets. The purpose of this article is to describe the types of semantic change of dendronyms. The study analyzes the tropeic and individual-author connotative levels of semantic transformations from the standpoint of lexical semantics, the doctrine of the paradigmatic nature of poetic images, the necessity to consider the language phenomenon in the aspect of «universal – individual». The composition of semantic features of dendronyms, updated in the context of comparative tropes, is described: birch – 'innocent’, ‘joyful’, ‘affectionate’; oak – ‘red-bearded’, linden – ‘hot’; poplar – ‘safety’; cherry tree, apple tree – ‘love, longing’; willow, apple tree, birch - ‘gold’, ‘emerald’, ‘fire’. The ways of representation of these features in poetic contexts are revealed: anthropomorphic images of comparison, the technique of folk-poetic figurative parallelism. Symbolic semantic conversions of dendronyms in the works of A.V. Shiryaevets are characterized: green (blooming) tree – transience and frailty of human life; birch - a symbol of the Motherland and childhood; tree - memory. In the aspect of «universal – individual» stable formulas of folklore and general poetic images of comparison used by the poet are defined: an oak – well-done, a blooming tree (a tree that opens its leaves) – a girl who blooms for love, a poplar - a watchman, leaves - gold, emerald, fire. Individual author's transformations of stable poetic formulas are defined: spring color – spring incense, tree – memory of the serene past.

Interval-Valued Intuitionistic Uncertain Linguistic Multi-attribute Decision-Making Method for Plant Location Selection with Partitioned Hamy Mean

Plant location selection (PLS) is a typical and complex multi-attribute group decision-making (MAGDM) problem, which requires consideration of several alternatives and multiple attributes. However, existing research results on PLS lack sufficient consideration of complex relationship patterns between attributes, which often leads to inaccurate decision results. Thus, in this paper, a new MAGDM technique is proposed to solve the problem of PLS, in which complex interrelationship structures exist between multi-inputs and attribute values are represented as interval-valued intuitionistic uncertain linguistic variables (IVIULVs). First, to solve the shortcomings of the existing operations, some new operational rules are redefined for IVIULVs based on linguistic scale functions and Archimedean T-conorms and T-norms. These new rules of operation hold the closedness, and are flexible for semantic transformation processing. Then, to more accurately reflect the partition structure and the complex interrelationship pattern among multi-input arguments, we extend the traditional Hamy mean and propose the partitioned Hamy mean (PHAM), which can eliminate the impact of unrelated attributes on the results and meet the semantic conversion needs of different decision makers. Furthermore, we develop two new fuzzy linguistic aggregation operators: interval-valued intuitionistic uncertain linguistic PHAM (IVIULPHAM) and its weighted version (IVIULWPHAM). Finally, a novel MAGDM technique (IVIUL-MAGDM) based on the IVIULWPHAM operator is proposed to solve the problem of PLS. Several examples of PLS are also illustrated to show the validity and advantages of the IVIUL-MAGDM method by comparing with the other existing MAGDM methods.

Multiple Protocols Interworking With Open Connectivity Foundation in Fog Networks

Intelligent applications have greatly promoted the development of fog computing. The protocols have emerged diversely, causing the problems in the interworking between terminal devices, which has seriously hindered the development of fog computing. Based on the open connectivity foundation (OCF) protocol, this paper proposes an interworking solution for intelligent devices and designs an OCF-based multi-protocol gateway to access non-OCF services. The main functions are divided into three categories: manager, adapter, and device agent. In part, the manager provides centralized management services, the adapter provides communication services between the client and the server device, and the device agent provides access services for the server device. The semantic conversion scheme of an OCF protocol with other protocols is proposed in the device agent, which solves the problem of interoperability between an OCF client device and non-OCF server devices. Finally, the OCF multi-protocol interworking gateway was built, and the semantic conversion between the OCF protocol, AllJoyn protocol, and the ZigBee protocol was implemented. The interoperability between the OCF client, AllJoyn device, and ZigBee device was verified.

Contributions of Letter-Speech Sound Learning and Visual Print Tuning to Reading Improvement: Evidence from Brain Potential and Dyslexia Training Studies.

We use a neurocognitive perspective to discuss the contribution of learning letter-speech sound (L-SS) associations and visual specialization in the initial phases of reading in dyslexic children. We review findings from associative learning studies on related cognitive skills important for establishing and consolidating L-SS associations. Then we review brain potential studies, including our own, that yielded two markers associated with reading fluency. Here we show that the marker related to visual specialization (N170) predicts word and pseudoword reading fluency in children who received additional practice in the processing of morphological word structure. Conversely, L-SS integration (indexed by mismatch negativity (MMN)) may only remain important when direct orthography to semantic conversion is not possible, such as in pseudoword reading. In addition, the correlation between these two markers supports the notion that multisensory integration facilitates visual specialization. Finally, we review the role of implicit learning and executive functions in audiovisual learning in dyslexia. Implications for remedial research are discussed and suggestions for future studies are presented.

Wybrane aspekty konwersji semantycznej – przypadek tolerancji

Selected Aspects of Semantic Conversion – The Case of Toleration
 The text refers to the selected aspects of semantic conversion defined as the process of transformation of the meaning of the terms. This paper deals with the notion of “toleration”. The classic negative tolerance was described as a lack of intolerance. Otherwise, the positive tolerance is often characterized as acceptance, esteem, or affirmation for differences. Such distinction between them has the effect that in the face of the negative theory positive tolerance appears paradoxical. On the other hand, in perspective of the positive theory the classic idea seems to be intolerance. Basing on positive concept may lead to a reductionist alternative: acceptance (esteem, affirmation) – intolerance. In the article the Author also considers the problem of connections between toleration and the power of individual. Furthermore, the paper examines simple and complex objects of tolerance and the problem of the person as a subject of toleration.

Enhancing factory data integration through the development of an ontology: from the reference models reuse to the semantic conversion of the legacy models

Data integration is one of the most crucial challenges for current manufacturing companies. Indeed, while the extended use of software tools generate more and more data about products, processes, and production resources, still this huge amount of data is represented using different formats and non-aligned structures. This issue is worsened by the fact that data can be found scattered in not linked databases and hosted in mutually incompatible systems. The growing need to access these data and the knowledge that they encapsulate on a global view from different perspectives is addressed by various approaches in order to support the integration between involved systems. This paper deals with the applicability of Semantic Web technologies in industrial context to enhance semantic interoperability. In particular, it proposes a systematic approach to support the development of a semantic model, focusing on the combination of two main critical aspects: the reuse of existing reference models and the semantic migration of the existing legacy models. Even both the aspects have been separately studied in previous research works and even their overlap is recurring during the stage of ontology development, to the best of our knowledge the literature is missing studies covering the synergistic and automatic combination of these two aspects within a holistic approach. The application possibilities of the approach are also investigated within a real case study from a high-precision mould-making company; thus demonstrating its feasibility for use in complex manufacturing contexts.

Semantic processing of EHR data for clinical research

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.

Critical attacks in code-based cryptography

Code-based cryptographic schemes are promising candidates for post-quantum cryptography since they are fast, require only basic arithmetic, and have a well understood security. While there is strong evidence that cryptosystems like McEliece and Niederreiter are secure, they have certain weaknesses when used without semantic conversions. Critical attacks generally can't be avoided by increasing the key size of several code-based cryptosystems. In this paper we present a survey on critical attacks in code-based cryptography and we propose a specific conversion with a smaller redundancy of data than Korara's et al. and which protects against CCA2. Our purpose is to evaluate three cryptosystems: McEliece, Niederreiter and HyMES. We analyse their security against several models such as: Broadcast, Known Partial plaintext, Message-resend, Related-message, Chosen ciphertext, Lunchtime, Reaction attack and Malleability. Our work follows a first work done by Imai and Kobara 2001 which does not cover the whole known attacks and does not deal with the HyMES scheme.

A menagerie of timed automata

Timed automata are state-machine-like structures used to model real-time systems. Since their invention in the early 1990s, a number of often subtly differing variants have appeared in the literature; one of this article’s key contributions is defining, highlighting, and reconciling these differences. The article achieves this by defining a baseline theory of timed automata, characterizing each variant both syntactically and semantically, and giving, when possible, syntactic and semantic conversion to and from the baseline version. This article also surveys various extensions to the basic timed-automaton framework.

A Study on Automated Semantic Analysis of Customer Satisfaction Comments-A Case Study on Service Quality of Hotels on a Chinese Tourism Website

This study aims to present a model of comment semantic vocabulary built on ontological theories to perform semantic conversion of customer comments on hotel services to ratings through the process of word matching. Ultimately, ratings given by the customers and ratings derived from the conversion of semantic analysis would be used for the analysis of customers’ satisfaction on hotel service quality. The method proposed in this study has proven that semantic analysis is capable of delivering results of substantial accuracy. Automated semantic analysis would not only allow corporate managers to boost their efficiency in data collection and processing through cloud information services but also help them better understand contents that customers really care about. By paying more attention and making more effort to improve upon shortcomings identified by customers, corporate managers would be able to raise customer satisfaction and improve the image of their businesses at the same time.

A Method of Geospatial Data Files Conversion Based on Semantic

Data conversion is a necessary step to establish a unified data storage and management mode. Based on the analysis of the structure of Shapefile and the principle of semantic conversion, this paper proposes a method of geospatial data files conversion based on semantic. This method to a relational database file shape file conversion, the application of this method can not only increase the data read speed, and ease of data management and sharing.