• Genomics & Informatics
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• v.17 no.2
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• pp.20.1-20.5
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• 2019

Entity normalization, or entity linking in the general domain, is an information extraction task that aims to annotate/bind multiple words/expressions in raw text with semantic references, such as concepts of an ontology. An ontology consists minimally of a formally organized vocabulary or hierarchy of terms, which captures knowledge of a domain. Presently, machine-learning methods, often coupled with distributional representations, achieve good performance. However, these require large training datasets, which are not always available, especially for tasks in specialized domains. CONTES (CONcept-TErm System) is a supervised method that addresses entity normalization with ontology concepts using small training datasets. CONTES has some limitations, such as it does not scale well with very large ontologies, it tends to overgeneralize predictions, and it lacks valid representations for the out-of-vocabulary words. Here, we propose to assess different methods to reduce the dimensionality in the representation of the ontology. We also propose to calibrate parameters in order to make the predictions more accurate, and to address the problem of out-of-vocabulary words, with a specific method.

• International Journal of Advanced Culture Technology
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• v.8 no.4
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• pp.306-317
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• 2020

This study aimed to identify the factors affecting elderly care willingness (ECW) of nursing students. A total of 313 nursing students participated in the study. Data were collected from May to June 2019 using Interpersonal Reactivity Index-Korean version, Semantic Differential Scaling for evaluating attitudes toward elders, Scale of Ageism, and Questionnaire for care willingness toward older adults. Data were analyzed by t-test, ANOVA, Pearson's correlation coefficient, and multiple regression using SPSS 21.0 Program. The factors with the most significant influence on the elderly care willingness was prejudice toward the elderly (β=-.30, p=.001), followed by attitude toward the elderly (β=-.25, p=.004), third year (β=-.23, p=.016), satisfaction with volunteer experience with the elderly (β=-.21, p=.008), satisfaction with nursing major (β=-.17, p=.032), and empathy (β=.16, p=.046). These factors explained 46% of the elderly care willingness. In conclusion, it is necessary to reduce prejudice against the elderly and develop positive attitudes toward the elderly to increase the willingness of nursing students to care for older adults. In particular, further study is required to increase the willingness of nursing students to care for the elderly through developing programs such as curriculums for positive contact experiences with the elderly and volunteer activities for the elderly.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.3
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• pp.771-791
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• 2022

Protein-protein interaction (PPI) extraction from original text is important for revealing the molecular mechanism of biological processes. With the rapid growth of biomedical literature, manually extracting PPI has become more time-consuming and laborious. Therefore, the automatic PPI extraction from the raw literature through natural language processing technology has attracted the attention of the majority of researchers. We propose a PPI extraction model based on the large pre-trained language model and adversarial training. It enhances the learning of semantic and syntactic features using BioBERT pre-trained weights, which are built on large-scale domain corpora, and adversarial perturbations are applied to the embedding layer to improve the robustness of the model. Experimental results showed that the proposed model achieved the highest F1 scores (83.93% and 90.31%) on two corpora with large sample sizes, namely, AIMed and BioInfer, respectively, compared with the previous method. It also achieved comparable performance on three corpora with small sample sizes, namely, HPRD50, IEPA, and LLL.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.5
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• pp.801-808
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• 2022

Research on how to embed knowledge in large-scale Linked Data and apply neural network models for entity matching is relatively scarce. The most fundamental problem with this is that different labels lead to lexical heterogeneity. In this paper, we propose an extended GCN (Graph Convolutional Network) model that combines re-align structure to solve this lexical heterogeneity problem. The proposed model improved the performance by 53% and 40%, respectively, compared to the existing embedded-based MTransE and BootEA models, and improved the performance by 5.1% compared to the GCN-based RDGCN model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.8
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• pp.2571-2586
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• 2022

The rapid popularity of government social media has generated huge amounts of text data, and the analysis of these data has gradually become the focus of digital government research. This study uses Python language to analyze the big data of the Chinese provincial government Weibo. First, this study uses a web crawler approach to collect and statistically describe over 360,000 data from 31 provincial government microblogs in China, covering the period from January 2018 to April 2022. Second, a word separation engine is constructed and these text data are analyzed using word cloud word frequencies as well as semantic relationships. Finally, the text data were analyzed for sentiment using natural language processing methods, and the text topics were studied using LDA algorithm. The results of this study show that, first, the number and scale of posts on the Chinese government Weibo have grown rapidly. Second, government Weibo has certain social attributes, and the epidemics, people's livelihood, and services have become the focus of government Weibo. Third, the contents of government Weibo account for more than 30% of negative sentiments. The classified topics show that the epidemics and epidemic prevention and control overshadowed the other topics, which inhibits the diversification of government Weibo.

• Educational Technology International
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• v.13 no.1
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• pp.49-77
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• 2012

The development of mobile devices and network technology is changing the ways in which people communicate with one another. Mobile text message has emerged as one of the most frequently used form of communication, which also gave rise to various non-verbal texts such as emoticons. Nonetheless, the use of text messages has largely been denied in education because text messages often involve colloquial and non-verbal texts considered inappropriate or grammatically incorrect by the teacher. In efforts to provide a theoretical framework to better understand mobile e-text communication, this research compared the practical usages of non-verbal texts in the mobile e-learning environment. The study developed three types of text messages according to the degree of using non-verbal texts and their phraseology as instructors' messages, which were then distributed to 259 students via mobile text messaging. The perceptions of students were analyzed using a semantic differential scale and a questionnaire. The results showed clear differences in students' perceptions of non-verbal text and traditional text, and that optimally designed non-verbal texts turned out to encourage the students' interaction the most out of the three types of text messages. Following the discussion of the results, an expanded theoretical framework beyond Ong's concepts of orality and literacy is also suggested to understand the evolution of mobile e-text communication in education.

• Journal of Ginseng Culture
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• v.4
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• pp.13-37
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• 2022

Even before recorded history, the Korean people took ginseng. Later, poetry passed down from China developed into a literary style in which intellectuals from the Silla, Goryeo, and Joseon Dynasties expressed their thoughts concisely. The aim of this paper is to find Korean poems related to ginseng and to look for their semantic network. To this end, "Korea Classical DB ", produced by the Institute for the Translation of Korean Classics, was searched to find ginseng poems. As the result of a search in November 2021, two poems from the Three Kingdoms Period, two poems from the Goryeo Dynasty, and 23 poems from the Joseon Dynasty were searched. An examination of these poems found that the first ginseng poem was "Goryeoinsamchan," which was sung by people in Goguryeo around the 6th century. Ginseng poetry during the Goryeo Dynasty is represented by Anchuk's poem. Anchuk sang about the harmful effects of ginseng tributes from a realistic point of view. Ginseng poetry in the Joseon Dynasty is represented by Seo Geo-jeong in the early period and Jeong Yakyong in the late period. Seo Geo-jeong's ginseng poem is a romantic poem that praises the mysterious pharmacological effects of ginseng. A poem called "Ginseng" by Yongjae Seonghyeon is also a romantic poem that praises the mysterious medicinal benefits of ginseng. As a scholar of Realist Confucianism, Dasan Jeong Yak-yong wrote very practical ginseng poems. Dasan left five ginseng poems, the largest number written by one poet. Dasan tried ginseng farming himself and emerged from the experience as a poet. The story of the failure and success of his ginseng farming was described in his poems. At that time, ginseng farming was widespread throughout the country due to the depletion of natural ginseng and the development of ginseng farming techniques after the reign of King Jeongjo. Since the early 19th century, ginseng farming had been prevalent on a large scale in the Gaeseong region, and small-scale farming had also been carried out in other regions. What is unusual is Kim Jin-soo's poem. At that time, in Tong Ren Tang, Beijing (the capital of the Qing Dynasty), ginseng from Joseon sold well under the "Songak Sansam" brand. Kim Jin-Soo wrote about this brand of ginseng in his poem. In 1900, Maecheon Hwanghyeon also created a ginseng poem, written in Chinese characters. Thus, the semantic network of Korean ginseng poems is identified as follows: 1) Ginseng poetry in the spirit of the people - Emerging gentry in the Goryeo Dynasty (Anchuk). 2) Romantic ginseng poetry - Government School in the early Joseon Dynasty (Seo Geo-jeong, Seonghyeon, etc.). 3) Practical ginseng poetry - Realist School in the late Joseon Dynasty (Jeong Yak-yong, Kim Jin-soo, Hwang Hyun, etc.). This semantic network was extracted while examining the development of Korean ginseng poems.

• Journal of Korean Society of Forest Science
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• v.104 no.4
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• pp.685-696
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• 2015

This study intends to evaluate visual preference and image for 10 types of forest sceneries which have own distinct characteristics without overlapping each other among 22 types of beautiful forest sceneries based on Korea National Parks. Z scale and paired comparison methods were used to analyze visual preference for forest scenery, and SD scale method was also adopted to assess visual image for forest scenery. Experiments were conducted with 3 different university students groups. The results of this study can be summarized as follows. First, the evaluation results of Z scale (N=70) and paired comparison (N=64) methods show similar visual preference for different types of forest scenery. 'Scenery inside forests' and 'valley and rock' sceneries have the highest level of preference, and 'panorama of ridges', 'ridge of curious rock peaks', and 'waterfall and cliff' also have relatively higher preference level than others. However, 'community of dead trees' has the lowest preference in forest sceneries. Second, the factor analysis outputs of the image scores for beautiful forest scenery types by SD scale (N=66) show 3 factors of 'mysterious charm', 'colorful sense', and 'vividness/variety'. Examined the variables of forest scenery types for each factor, the image of 'mysterious charm' is based on the sceneries of mountain ridges, the image of 'colorful sense' is focused on the sceneries of color-expressed sense of the season, and the image of 'vividness/variety' is based on the sceneries of waterscape and curious rock peaks.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.75-88
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• 2020

Given the development of information technology, various methods for efficient information delivery have been constructed as the method of delivering product information moves from offline and 2D to online and 3D. These attempts not only are about delivering product information in an online space where no real product exists but also play a crucial role in diversifying and revitalizing online shopping by providing virtual experiences to consumers. 360 product image is a photorealistic VR that allows a subject to be rotated and photographed to view objects in three dimensions. 360 product image has also attracted considerable attention considering that it can deliver richer information about an object compared with the existing still image photography. 360 product image is influenced by divergent production factors, and accordingly, a difference emerges in the responses of users. However, as the history of technology is short, related research is also insufficient. Therefore, this study aimed to grasp the responses of users, which vary depending on the type of products and the number of source images in the 360 product image process. To this end, a representative product among the product groups that can be frequently found in online shopping malls was selected to produce a 360 product image and experiment with 75 users. The emotional responses to the 360 product image were analyzed through an experimental questionnaire to which the semantic classification method was applied. The results of this study could be used as basic data to understand and grasp the sensitivity of consumers to 360 product image.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.153-173
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• 2013

The ontology has been gaining increasing interests by recent arise of the semantic web and related technologies. The focus is mostly on inference query processing that requires high-level techniques for storage and searching ontologies efficiently, and it has been actively studied in the area of semantic-based searching. W3C's recommendation is to use RDFS and OWL for representing ontologies. However memory-based editors, inference engines, and triple storages all store ontology as a simple set of triplets. Naturally the performance is limited, especially when a large-scale ontology needs to be processed. A variety of researches on proposing algorithms for efficient inference query processing has been conducted, and many of them are based on using proven relational database technology. However, none of them had been successful in obtaining the complete set of inference results which reflects the five characteristics of the ontology properties. In this paper, we propose a new index structure called hyper cube index to efficiently process inference queries. Our approach is based on an intuition that an index can speed up the query processing when extensive inferencing is required.