• Proceedings of the Korean Information Science Society Conference
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• 1998.10c
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• pp.177-179
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• 1998

본 논문은 의미론적 어휘개념에 기반한 한국어 동사 Isa 계층구조 시스템을 이용한 Semantic Network을 구축하며, 이를 활용하여 부사와 동사 간의 공기제약관계 설정에 유효한 개념 분류를 수행한다. 일반적으로 많이 쓰이는 한국어 동사 658개를 대상으로 semantic network을 구축한 결과, SENKOV는 44개의 top node를 가지고 있으며 depth 는 약 2.35이었다. 한국어 동사의 semantic network은 영어에서와 마찬가지로 명사보다 top node의 개수가 많고 depth가 훨씬 더 얕았다. 그리고 성상부사의 selectional restriction에 유효한 개념분류를 하는데 SENKOV를 활용하였다.

• Journal of Korean Clinical Nursing Research
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• v.25 no.3
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• pp.209-220
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• 2019

Purpose: A significant amount of public opinion about nurse bullying is expressed on the internet. The purpose of this study was to analyze the linkage structures among words extracted from comments on internet articles related to nurse workplace bullying using semantic network analysis. Methods: From February 2018 to April 2019, comments made on news articles posted to the Daum and Naver web portal containing keywords such as "nurse", "Taeum", and "bullying" were collected using a web crawler written in Python. A morphological analysis performed with Open Korean Text in KoNLPy generated 54 major nodes. The frequencies, eigenvector centralities, and betweenness centralities of the 54 nodes were calculated and semantic networks were visualized using the UCINET and NetDraw programs. Convergence of iterated correlations (CONCOR) analysis was performed to identify structural equivalence. Results: This paper presents results about March 2018 and January 2019 because these months had highest number of articles. Of the 54 major nodes, "nurse", "hospital", "patient", and "physician" were the most frequent and had the highest eigenvector and betweenness centralities. The CONCOR analysis identified work environment, nurse, gender, and military clusters. Conclusion: This study structurally explored public opinion about nurse bullying through semantic network analysis. It is suggested that various studies on nursing phenomena will be conducted using social network analysis.

• CELLMED
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• v.13 no.2
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• pp.3.1-3.21
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• 2023

In this study, the drinking culture of the Choseon dynasty is examined with the text frequency analysis technique on the entire 『Choseonwangjosilok (朝鮮王朝實錄)』. This study examined a total of 1,968 volumes and 948 books about 27 kings of Choseon , which spans a total of 518 years, through web crawling on the National Institute of Korean History website. Python 3.8 was used to extract sentences related to alcohol, Rhino 1.4.5 was used for morphological analysis to extract nouns, and Gephi 0.9.2 was used for semantic network analysis. According to 『Choseonwangjosilok (朝鮮王朝實錄)』 about alcohol culture, the results of the analysis are as follow: Alcoholic beverages were more often used in court or in ritual ceremonies rather than those based on specific ingredients or manufacturing methods commonly used by the general public. regarding the ruling class through semantic network analysis l in the 『Choseonwangjosilok (朝鮮王朝實錄)』, the Choseon dynasty was found to be highly associated with political issues related to maintaining the power relations within the Korean royal court system. At times, alcohol was used to maintain personal relationships, while at other times it was seen as an essential item in state ceremonies. It was also used as a highly political means to maintain and strengthen national power.

• Korean Institute of Interior Design Journal
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• v.19 no.6
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• pp.75-82
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• 2010

The purposes of this study are to present the criteria for a sensibility rating scale for measuring the general public's perception of museums' spatial environment, particularly lobby space, through an empirical analysis; and to clarify the characteristics of the presented rating scale in terms of each rating element and factor. For this study, a survey was conducted during September 11-17, 2010, and a total of 370 museum visitors participated in the survey. A sensibility rating scale used for the survey consisted of a total of 32 adjectives selected from a literature review of previous studies. To specify the dimensions of semantic space using the semantic adjectives, words with opposite meanings were analyzed with the semantic differential technique developed by Osgood et al. Using SPSS, a reliability analysis, factor analysis, and cluster analysis were conducted on the data obtained from the survey. The results of this study can be summarized as follows: According to the general public's perception of museum lobbies, five factors were found from the 19 semantic ratings of the Gyeonggido Museum of Modern Art and the 20 semantic ratings of the Seoul Museum of Art, respectively. In the case of Gyeonggido Museum of Modern Art, three additional semantic words of 'orderly', 'open', and 'original', which did not appear in the case of Seoul Museum of Art, were discovered. In the case of Seoul Museum of Art, more detailed semantic words such as 'restrained', 'ordinary', 'concrete', and 'intellectual (rational)' were obtained. Five semantic elements, which describe the two museums, were: Feelings of 'pleasantness', 'value, 'usage', 'aesthetics', and 'materials'. According to a comparative analysis of the two lobby spaces in terms of semantic rating elements, Gyeonggido Museum of Modern Art was perceived to be an orderly, original, open, soft, and female-like space, whereas Seoul Museum of Art was perceived to be aesthetic, restrained, concrete, realistic, intellectual and rational. In the coming years, the results of this study will serve as valuable data for constructing a sensibility rating scale for evaluating spatial environments of museums.

• Journal of Gifted/Talented Education
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• v.27 no.1
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• pp.17-35
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• 2017

The purpose of this study is to analyze the learning objectives characteristics of educational programs of centers for the university affiliated science-gifted education using semantic network analysis, we examined the applicability of semantic network analysis in analyzing learning objectives by comparing the results of analysis with Bloom's revised taxonomy. For this purpose, 702 learning objectives presented in 169 science subjects were selected as subjects to be analyzed. After classifying and coding the learning objectives according to Bloom's revised taxonomy, we conducted a semantic network analysis to investigate the relationship between learning objectives. The results of the analysis are as follows. First, we looked at the number of learning objectives used for each subject, and about 3 elementary school levels and about 6 middle school levels were used. Second, the knowledge dimension such as 'factual and conceptual knowledge' and cognitive process dimension such as 'remember', 'understand', and 'create' was high regardless of the research method and school level. Third, the results of analysis based on the weighting through the semantic network analysis method, the elementary school level emphasize activities th be applied to the actual experimental process through learning about scientific facts, while the middle school level emphasize the understanding of scientific facts and concepts themselves. As a result, it can be seen that the semantic network analysis can analyze characteristics of various learning objectives rather than the conventional simple statistical analysis.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.4 no.3
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• pp.134-138
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• 2003

The purpose of this study is to analyse the argument alternations in terms of semantic perspective. Argument alternation has long been an interesting topic for the linguists regardless of their linguistic schools. Semantic analysis of argument alternation is attempted by Dowty(2001) based on the Levin(1993)'s classification. The study is focused on the phenomenon where meaning changes with argument alternations even though those sentences look the same syntactically and lineally. 1 tried not only to classify verbs according to the meaning changes but to explain the alternations in semantic point of view. The verbs are divided into 4 types- Touch type, Hit type, Cut type, and Break type. Each type of verbs are tested if they show special characteristics with three alternations-Middle alternation, Body-part possessor Ascension, and Conative Alternation. And semantic analysis is tried based on that classification.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.6
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• pp.2761-2781
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• 2018

With the continuous growth of data size and the use of compression technology, data reduction has great research value and practical significance. Aiming at the shortcomings of the existing semantic compression algorithm, this paper is based on the analysis of ItCompress algorithm, and designs a method of bidirectional order selection based on interval partitioning, which named An Optimized Iterative Semantic Compression Algorithm (Optimized ItCompress Algorithm). In order to further improve the speed of the algorithm, we propose a parallel optimization iterative semantic compression algorithm using GPU (POICAG) and an optimized iterative semantic compression algorithm using Spark (DOICAS). A lot of valid experiments are carried out on four kinds of datasets, which fully verified the efficiency of the proposed algorithm.

• The Journal of Korean Association of Computer Education
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• v.14 no.3
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• pp.37-49
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• 2011

A user can use resources on the Semantic Web using a Semantic Web browser. In order to utilize the functions of Semantic Web browsers in education, we compared the functions of well-known Semantic Web browsers such as Tabulator, Contextual Search Browser (CSB), Magpie, and Piggy Bank. In order to utilize Semantic Web browsers in education, a user needs to understand the features of each Semantic Web browser and our work can help both teachers and students. Tabulator is an RDF browser that can help to check whether resources can be used for learning and relevance of resources. CSB can be used to search educational resources using a conrtext file that contains the subjects of learning. It can also help learning by showing semantic web resources in the form of triple set as well as by supporting highlighting function. Magpie can help learners without basic knowledge on learning materials by providing interpretation based on a glossary file and related background knowledge. Piggy Bank supports conversion of web resources into semantic web resources and allows to browse semantic web resources in various views as well as to share semantic web resources.

• The Journal of Korean Association of Computer Education
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• v.20 no.5
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• pp.79-88
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• 2017

To upgrade the subjective question grading, we need the syntactic-semantic analysis to analyze syntatic-semantic relation between words in answering. However, since the syntactic-semantic tree has structural and semantic relation between words, we can not apply the method calculating the similarity between vectors. This paper suggests the comparing system with syntactic-semantic tree which has structural and semantic relation between words. In this thesis, we suggest similarity calculation principles for comparing the trees and verify the principles through experiments. This system will help the subjective question grading by comparing the trees and be utilized in distinguishing similar documents.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.12
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• pp.5782-5799
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• 2018

With the development of GPS and the popularity of mobile devices with positioning capability, collecting massive amounts of trajectory data is feasible and easy. The daily trajectories of moving objects convey a concise overview of their behaviors. Different social roles have different trajectory patterns. Therefore, we can identify users or groups based on similar trajectory patterns by mining implicit life patterns. However, most existing daily trajectories mining studies mainly focus on the spatial and temporal analysis of raw trajectory data but missing the essential semantic information or behaviors. In this paper, we propose a novel trajectory semantics calculation method to identify groups that have similar behaviors. In our model, we first propose a fast and efficient approach for stay regions extraction from daily trajectories, then generate semantic trajectories by enriching the stay regions with semantic labels. To measure the similarity between semantic trajectories, we design a semantic similarity measure model based on spatial and temporal similarity factor. Furthermore, a pruning strategy is proposed to lighten tedious calculations and comparisons. We have conducted extensive experiments on real trajectory dataset of Geolife project, and the experimental results show our proposed method is both effective and efficient.