• Journal of International Academy of Physical Therapy Research
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• v.10 no.4
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• pp.1926-1933
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• 2019

Background: Physical therapy has been widely studied in various fields, however, the academic trends and characteristics has not been systematically analyzed. Semantic network analysis is used as an approach for this study. Objective: To explore academic trends and knowledge system in the physiotherapy research in the Journal of International Academy Physical Therapy (J of IAPTR) Study design : Literature review Method: Semantic network analysis was conducted using the titles of 272 articles published in the Journal of IAPTR from 2010 to 2019. Results: Frequency analysis revealed following most frequently used key words; Stroke (27 times), Balance (21 times), Elder (13 times), Forward head posture (FHP, 11 times), Muscle activity (9 times). The relationship between the presented keywords is divided into six subgroups (FHP and pain, walk and quality, elder and balance, stroke and apoptosis, muscle strength and function) according to their correlation and frequency to be used together. Conclusion: The study is considered to be of help to researchers who want to identify research trends in physiotherapy.

• 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.

• The Journal of Korean Association of Computer Education
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• v.7 no.2
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• pp.57-65
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• 2004

For the purpose of an effective instruction-learning process by systematic management of knowledge between instructor and learner in e-Learning, we have developed the authoring system in which the instructor is able to author easily on various lecture frames according to the instructional design theory. The authored contents with the relations among the learning objects based on SCORM standard would help learner to conceptualize the contents. A knowledge map is constructed on the relations among the learning objects using RDF of the semantic web. We introduce the ontology in which the instructor can make a dictionary of terminology by registering the words of the teaching area. The learning activity and comprehension of students can be assessed using each student's learning map along the interaction points which are introduced to present the individual learning by considering each student's capacity of understanding and achievement.

• Journal of the Korean BIBLIA Society for library and Information Science
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• v.17 no.1
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• pp.285-303
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• 2006

This study suggests the method of converting thesauri to SKOS step by step and it is formalized in three stages of the conversion process. The study develops output and guidelines for each stage. The converting stages are: (1) Collecting and analyzing thesauri for understanding about structure of terms and semantics of relation. (2) Defining the conversion method and creating ontology of the thesauri. (3) Examining the preservation of forms and various semantic relations between the thesauri and then creating SKOS ontology. This method can be applied to the thesauruses with complicated relations in concepts. In the future, it is needed to have an embodiment of conversion after making the algorithm of conversion by stage with the method suggested in this research.

• Journal of Advanced Navigation Technology
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• v.12 no.5
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• pp.516-523
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• 2008

Recently, the Web has been constructed based on collective intel1igence and growing up quickly. User created contents have been made the mainstream in this environments. So it's required to make an efficient technique of searching for the contents. The current searching technique mainly is achieved by key words. Semantic Web based on similarity and relationship of a language and using user tags in web2.0 also have been researched with activity. Generally, the web of the participation architecture has a lot of user created contents, various forms and classification. Therefore, it is necessary to classify and to efficiently search for a lot of user created contents. In this paper, we propose a depth searching technique considering the relationship among the tags that descript user contents. It is expected that the proposed depth searching techniques can reduce the time taken to search for the unwanted contents and the increase the efficiency of the contents searching using a service of suggestion words in tags groups.

• Journal of the Korean earth science society
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• v.33 no.1
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• pp.59-72
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• 2012

The purpose of this study is to explore the types of interlanguage that middle school students use in learning about the concept of diastrophism. Eighteen students from two classes in a middle school in Seoul participated in the study and each participant was interviewed four times. Data were analyzed in terms of semantic relations and thematic patterns. As a result, eight interlanguage types were identified and described, which are using everyday language as resources, combining scientific words with everyday words, conjugating scientific words imperfectly, and using semantic relations inappropriately. The implication of interlanguage for science learning and teaching is discussed.

• Journal of the Korea Society of Computer and Information
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• v.11 no.2 s.40
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• pp.93-99
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• 2006

Ontology is an indispensable component in intelligent and semantic processing of knowledge and information, such as in semantic web. Ontology is considered to be constructed generally on the basis of taxonomy of human concepts about the world. However. as human concepts are unstructured and obscure, ontology construction based on the taxonomy of human concepts cannot be realized systematically furthermore automatically. So, we try to do this from the relation among linguistic symbols regarded representing human concepts, in short, words. We show the similarity between taxonomy of human concepts and relation among words. And we propose a methodology to construct and generate automatically ontologies from these relations mon words and a series of algorithm to convert these relations into ontologies. This paper presents the process and concrete application of this methodology.

• Journal of KIISE
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• v.43 no.12
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• pp.1376-1384
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• 2016

Computers require analytic and processing capability for all possibilities of human expression in order to process sentences like human beings. Linguistic information processing thus forms the initial basis. When analyzing a sentence syntactically, it is necessary to divide the sentence into components, find obligatory arguments focusing on predicates, identify the sentence core, and understand semantic relations between the arguments and predicates. In this study, the method applied a case frame dictionary based on The Korean Standard Dictionary of The National Institute of the Korean Language; in addition, we used a CRF Model that constructed subcategorization of predicates as featured in Korean Lexical Semantic Network (UWordMap) for semantic role labeling. Automatically tagged semantic roles based on the CRF model, which established the information of words, predicates, the case-frame dictionary and hypernyms of words as features, were used. This method demonstrated higher performance in comparison with the existing method, with accuracy rate of 83.13% as compared to 81.2%, respectively.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.10
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• pp.2643-2657
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• 2023

Scene graphs are structured representations that can clearly convey objects and the relationships between them, but are often heavily biased due to the highly skewed, long-tailed relational labeling in the dataset. Indeed, the visual world itself and its descriptions are biased. Therefore, Unbiased Scene Graph Generation (USGG) prefers to train models to eliminate long-tail effects as much as possible, rather than altering the dataset directly. To this end, we propose Geometric and Semantic Improvement (GSI) for USGG to mitigate this issue. First, to fully exploit the feature information in the images, geometric dimension and semantic dimension enhancement modules are designed. The geometric module is designed from the perspective that the position information between neighboring object pairs will affect each other, which can improve the recall rate of the overall relationship in the dataset. The semantic module further processes the embedded word vector, which can enhance the acquisition of semantic information. Then, to improve the recall rate of the tail data, the Class Balanced Seesaw Loss (CBSLoss) is designed for the tail data. The recall rate of the prediction is improved by penalizing the body or tail relations that are judged incorrectly in the dataset. The experimental findings demonstrate that the GSI method performs better than mainstream models in terms of the mean Recall@K (mR@K) metric in three tasks. The long-tailed imbalance in the Visual Genome 150 (VG150) dataset is addressed better using the GSI method than by most of the existing methods.

• The KIPS Transactions:PartB
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• v.11B no.4
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• pp.491-500
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• 2004

This paper presents an ontology construction method using various computational language resources, and an ontology-based word sense disambiguation method. In order to acquire a reasonably practical ontology the Kadokawa thesaurus is extended by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. To apply the ontology to disambiguate word senses, we apply the previously-secured dictionary information to select the correct senses of some ambiguous words with high precision, and then use the ontology to disambiguate the remaining ambiguous words. The mutual information between concepts in the ontology was calculated before using the ontology as knowledge for disambiguating word senses. If mutual information is regarded as a weight between ontology concepts, the ontology can be treated as a graph with weighted edges, and then we locate the weighted path from one concept to the other concept. In our practical machine translation system, our word sense disambiguation method achieved a 9% improvement over methods which do not use ontology for Korean translation.