Construction Of Semantic Network Research Articles

메타버스(Metaverse) 관광 뉴스기사의 잠재적 프레임 분석: 텍스트마이닝

The tourism sector, which has been stagnant due to the prolonged Covid-19, is trying to actively utilize the metaverse to build a new paradigm. At this moment, it is necessary to develop a strategy to utilize the metaverse for building a sustainable competitive advantage in the tourism sector of the future. Although metaverse is emerging as a new management theme in the tourism sector, research related to it is insufficient. Therefore, this study tried to identify the utilization and issues of metaverse conducted in the tourism sector through text mining of news articles. To this end, 1,171 news articles were collected from Big Kinds, 75 key words were extracted using Textome, and semantic network construction and CONCOR analysis were performed using Ucinet and Netdraw. As a result of the analysis, it was possible to confirm the organic connection relationship of a total of 7 types of potential frames in news articles related to domestic metaverse tourism. Based on the analysis results of this study, the utilization and direction of metaverse tourism were presented in conclusion,

Semantic Representation of Context for Description of Named Rivers in a Terminological Knowledge Base.

The description of named entities in terminological knowledge bases has never been addressed in any depth in terminology. Firm preconceptions, rooted in philosophy, about the only referential function of proper names have presumably led to disparage their inclusion in terminology resources, despite the relevance of named entities having been highlighted by prominent figures in the discipline of terminology. Scholars from different branches of linguistics depart from the conservative stance on proper names and have foregrounded the need for a novel approach, more linguistic than philosophical, to describing proper names. Therefore, this paper proposed a linguistic and terminological approach to the study of named entities when used in scientific discourse, with the purpose of representing them in EcoLexicon, an environmental knowledge base designed according to the premises of Frame-based Terminology. We focused more specifically on named rivers (or potamonyms) mentioned in a coastal engineering corpus. Inclusion of named entities in terminological knowledge bases requires analyzing the context that surrounds them in specialized texts because these contexts convey specialized knowledge about named entities. For the semantic representation of context, this paper thus analyzed the local syntactic and semantic contexts that surrounded potamonyms in coastal engineering texts and described the semantic annotation of the predicate-argument structure of sentences where a potamonym was mentioned. The semantic variables annotated were the following: (1) semantic category of the arguments; (2) semantic role of the arguments; (3) semantic relation between the arguments; and (4) lexical domain of the verbs. This method yielded valuable insight into the different semantic roles that named rivers played, the entities and processes that participated in the events educed by potamonyms through verbs, and how they all interacted. Furthermore, since arguments are specialized terms and verbs are relational constructs, the analysis of argument structure led to the construction of semantic networks that depicted specialized knowledge about named rivers. These conceptual networks were then used to craft the thematic description of potamonyms. Accordingly, the semantic network and the thematic description not only constituted the representation of a potamonym in EcoLexicon, but also allowed the geographic contextualization of specialized concepts in the terminological resource.

Construction of semantic networks and determining the degree of divergence of texts

The article presents a method for comparing text documents, which is based on the construction and comparison of the corresponding semantic networks. This technique can become the basis for building systems for comparing legal documents in the framework of parliamentary control. An algorithm for constructing semantic networks as one of the types of ontologies is also considered. This algorithm can also be used in systems for automated summarizing legal information in order to generate concise information-rich reports, brief annotations or digests. The proposed technique can be used in processing queries during information retrieval, providing the ability to determine the degree of similarity or difference in the structure and semantics of texts.

Perceptions of the Yangtze River cruise ship brand based on text semantic analysis: A web big data perspective

Relying on the rich natural and human resources of the Yangtze River, the Yangtze River cruise has been a popular and famous tourism route. This study adopts a big data approach to study the perception of and satisfaction with the Yangtze River cruise brand, so as to come up with some practical suggestions for the enhancement of its value. Based on the analysis of the current development of the Yangtze River cruise tour, this paper conducted a textual analysis on 2,260 user reviews of two important Chinese travel OTA companies (Ctrip.com and Tuniu.com) through the theory and method of web text mining. A word frequency analysis, the construction of semantic networks, and a sentiment analysis were carried out. The results of the study show that the market perception of each cruise company’s brand is not very different, and the relevant companies have not yet developed their own special products and services. “Service” is the most frequently mentioned high-frequency word in tourists’ reviews. The sentiment analysis shows that the frequency of positive emotions in tourists’ reviews is high (exceeding 80 %). Through the satisfaction analysis of six dimensions of Yangtze River cruises, it is found that “cruise dining” and “cruise entertainment” are weak points.

Structural analysis of relevance propagation models

Relevance relations constitute the core of information retrieval. Topical ontologies, such as collaborative webpage classification projects, can provide a basis for identifying and analyzing such relations. New meaningful relevance relations can be automatically inferred from these ontologies by composing existing ones. In this work, several relevance propagation models are analyzed in terms of complex network theory. Structural properties such as Characteristic path length, Clustering coefficient and Degree distribution are computed over the models in order to understand the nature of each underlying network. This analysis raises interesting points about the Small-world and Scale-free structure of some relevance propagation models. Moreover, other connectivity and centrality measures are computed to gain additional insight into the topology of relevance. Finally, the analysis is complemented by providing visualizations of the k-core decomposition of different relevance propagation models. To illustrate the generalizability of the proposed methodology the analysis is carried out on an ontology from a different domain. The major theoretical implication of this analysis is the derivation of new instruments to typify semantic networks derived from relevance relations. The results can be exploited in a pragmatic way, as the parameters and properties derived by this analysis can serve as prior knowledge to algorithms for the automatic or semi-automatic construction of semantic networks.

Percepción y Accesibilidad Tecnológica de Universitarios en el Suroeste de República Dominicana durante el Covid-19

La pandemia generó suspensión de clases presenciales y la implementación de la educación virtual. El objetivo de este estudio fue diagnosticar y analizar la percepción de estudiantes universitarios y su accesibilidad tecnológica durante la emergencia educativa en el suroeste de República Dominicana (enero-abril de 2020). Se empleó un método mixto de investigación, se aplicó una encuesta y se analizaron las respuestas mediante estadística descriptiva e inferencia estadística, análisis descriptivo y construcción de redes semánticas. Respecto a la accesibilidad tecnológica, los resultados muestran que predomina el uso de teléfonos en vez de computadoras y falta de acceso a Internet. Según los estudiantes existen más aspectos negativos que positivos en la educación virtual, ya que faltan equipos tecnológicos y accesibilidad a internet, lo que genera frustración e incomodidad, así como dificultades de aprendizaje. También la sobrecarga de asignaturas y actividades, ya que más de 60 tareas fueron asignadas para desarrollar en un mes, lo que para ellos demuestra insensibilidad de los docentes frente a la situación. El distanciamiento social generó ansiedad debido al aislamiento y falta de concentración para el aprendizaje. Entre los aspectos positivos está el uso de la tecnología, que facilita el aprendizaje y permite la continuidad de los estudios.

Percepção das vulnerabilidades associadas a adesão ao tratamento por adolescentes em hemodiálise

Adolescentes com Doença Renal Crônica em hemodiálise são submetidos a uma complexa terapêutica que propicia alterações significativas em seu âmbito biopsicossocial, sendo a adesão ao tratamento um dos principais eixos da atenção à saúde para garantir qualidade de vida nessa população. Este estudo objetivou descrever os aspectos percebidos na vivência imposta pela doença, bem como a sua correlação com a adesão ao tratamento. Tratou-se de um estudo descritivo e exploratório com abordagem qualitativa. Os dados foram coletados em duas unidades de diálise entre fevereiro e junho de 2017 por meio de análise documental e entrevistas. Empregou-se o método de Análise do Conteúdo, com auxílio do Software ATLAS. ti (Analysis of Qualitative Data) 7.5.1 para a construção de redes semânticas. A amostra contou com 7 adolescentes, 7 familiares e 15 profissionais. Verificou-se que o contexto é permeado por desfavoráveis condicionantes socioeconômicos, frágil rede de apoio, exclusão escolar, isolamento social, dor e sofrimento, percebidos como vulnerabilidades para a adesão ao tratamento. Em contrapartida, a troca de experiências e a identificação de necessidades individuais sinalizou contribuir para potencializar a adesão à terapêutica pelos adolescentes.

Enhancing Poor Readers’ Reading Comprehension Ability through Word Semantic Knowledge Training

Word semantic knowledge (WSK) is crucial to the development of one’s reading comprehension ability. A total of 608 students (208 poor readers in the experimental group and 200 poor readers in the control group one, and 200 typical readers in control group 2) from grade 4 participated in this study. Selected reading ability assessments were administered at pretest, post-test and a 3-month delayed post-test. After 6 weeks of intervention, poor readers from the experimental group scored higher in Chinese reading abilities than the poor readers in control group one. Experimental group students reached a similar level of reading abilities with typical readers. Findings from this study suggested the main challenge in reading activities for those students who had reading problems is the characters’ semantic identification. The current study confirmed the self-agency learning mode is an effective approach to semantic intervention design on semantic network construction for primary school learners. HIGHLIGHTS Children can understand the complex and abstract reading materials through word semantic intervention. There is a positive causal effect of word semantic knowledge on reading comprehension, content inference, receptive vocabulary, word recognition, and reading mastery goals. Self-agency learning mode is an effective approach to semantic knowledge intervention design Word semantic training is an effective method in facilitating poor readers’ reading abilities at early reading stages

Empoderamiento digital y desarrollo de competencias Matemáticas en la formación del docente de Matemáticas

The present work was oriented to reveal the level of digital empowerment and the development of mathematical competences in the initial formation of the mathematics teacher at the Hermilio Valdizan University. The objective of the study was to evaluate the implications of the use of digital resources: mathematical software and interactive pages in the teaching-learning process of the topics of mathematics and the development of reasoning, problem solving and argumentation skills, communication and argumentation. The research was framed in the qualitative methodology based on the techniques of participant observation, in-depth interview and evaluation rubric. The data collected through the interview was subjected to a process of codification, categorization and construction of semantic networks with the help of the Atlas.ti, while the data from the observation process and the rubric are presented and interpreted statistically in (frequencies and percentages). From the most significant findings it can be seen that most of the teachers in initial training have a considerable digital empowerment, expressed in the use of free software, collaboration resources, social networks and interactive pages for the solution of mathematical problems in algebraic form and graph. On the other hand, more than 85% state that their mathematical competences were developed optimally with the use of technology. It is concluded that digital empowerment is fundamental for the learning of mathematics and the development of reasoning skills, problem solving, modeling and mathematical communication during the initial training of the mathematics teacher.

Современный визуальный дидактический регулятив в проекте «Жизнь замечательных мелодий»

The article presents a searching-experimental Project “The Life of Remarkable Melodies” (“LRM”), the conception of which is based on the application of the didactic principle of multidimensionality to the phenomenon of the “musical image” in the form of specially selected and ranged creative arrangements and performances of remarkable melodies in assembly works — mono-anthologies, as well as on the technology of comparative music listening. According to the latter, the listener — the User of the “LRM” Project — intuitively emphasizes the distinctive features of the arrangement and specifies his or her preferences. The peculiarity of the “LRM” Project is in the convenient multipage interface and the exclusive title included in the text of Testimonial RU 2018612220: Instructional Program Complex “The Life of Remarkable Melodies.” For the aggrandizement of demonstrativeness, the article makes use of didactic means of figurative visualization of knowledge, which would initiate a synesthetic perception of depicted informative and operative components, facilitating the process of operating it in the teaching process. In particular, a visual didactic directive is introduced of the logical-semantic type, the “Self-Effectivity of the User of the LRM Project,” obtained by modeling information presented in a natural language. It realizes the conception of construction of semantic networks, moreover, the figurative form of the directive is provided by the graphics of a “solar” — radial type.

A system for extracting and comparing memes in online forums

From their origins in the sociological field, memes have recently become of interest in the context of `viral' transmission of basic information units (memes) in online social networks. However, much work still needs to be done in terms of metrics and practical data processing issues. In this paper we define a theoretical basis and processing system for extracting and matching memes from free format text. The system facilitates the work of a text analyst in extracting this type of data structures from online text corpuses and n performing empirical experiments in a controlled manner. The general aspects related to the solution are the automatic processing of unstructured text without need for preprocessing (such as labelling and tagging), identification of co-occurences of concepts and corresponding relations, construction of semantic networks and selecting the top memes. The system integrates these processes which are generally separate in other state of the art systems. The proposed system is important because unstructured online text content is growing at a greater rate than other content (e.g. semi-structured, structured) and integrated and automated systems for knowledge extraction from this content will be increasingly important in the future. To illustrate the method and metrics we process several real online discussion forums, extracting the principal concepts and relations, building the memes and then identifying the key memes for each document corpus using a sophisticated matching process. The results show that our method can automatically extract coherent key knowledge from free text, which is corroborated by benchmarking with a set of other text analysis approaches, as well as a user study evaluation.

Using syntactic text analysis to estimate educational tasks’ difficulty and complexity

We suggest a routine for automatic estimation of complexity and difficulty of educational tasks. This routine is based on syntactic text analysis, phrases' predicative structures identification, and semantic network construction. Then we develop a mathematical model which employs a notion on semantic distance between notions---words to calculate the amount of knowledge in a semantic network. We show that the amount of knowledge in a semantic network is a measure in the set of all semantic networks, and the semantic distance makes this set the metric one.

Efficient semantic network construction with application to PubMed search

Exploring PubMed to find relevant information is challenging and time-consuming because PubMed typically returns a long list of articles as a result of query. Semantic network helps users to explore a large document collection and to capture key concepts and relationships among the concepts. The semantic network also serves to broaden the user’s knowledge and extend query keyword by detecting and visualizing new related concepts or relations hidden in the retrieved documents. The problem of existing semantic network techniques is that they typically produce many redundant relationships, which prevents users from quickly capturing the underlying relationships among concepts. This paper develops an online PubMed search system, which displays semantic networks having no redundant relationships in real-time as a result of query. To do so, we propose an efficient semantic network construction algorithm, which prevents producing redundant relationships during the network construction. Our extensive experiments on actual PubMed data show that the proposed method (COMPACT) is significantly faster than the method removing redundant relationships afterward. Our method is implemented and integrated into a relevance-feedback PubMed search engine, called RefMed, “http://dm.postech.ac.kr/refmed”.

Automatic construction of semantic networks

Six scientists are members of the group. The project started in August 1975.