• Journal of the Korean Society for Library and Information Science
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• v.53 no.3
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• pp.247-271
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• 2019

Uncertainty in scientific knowledge means an uncertain state where propositions are neither true or false at present. The existing studies have analyzed the propositions written in the academic literature, and have conducted the performance evaluation based on the rule based and machine learning based approaches by using the corpus. Although they recognized that the importance of word construction, there are insufficient attempts to expand the word by analyzing the meaning of uncertainty words. On the other hand, studies for analyzing the structure of networks by using bibliometrics and text mining techniques are widely used as methods for understanding intellectual structure and relationship in various disciplines. Therefore, in this study, semantic relations were analyzed by applying Word2Vec to existing uncertainty words. In addition, WordNet, which is an English vocabulary database and thesaurus, was applied to perform a network analysis based on hypernyms, hyponyms, and synonyms relations linked to uncertainty words. The semantic and lexical relationships of uncertainty words were structurally identified. As a result, we identified the possibility of automatically expanding uncertainty words.

• The Korean Journal of Pain
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• v.32 no.4
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• pp.271-279
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• 2019

Background: We utilized diffusion tensor imaging (DTI) to evaluate the cerebral white matter changes that are associated with phantom limb pain in patients with unilateral arm amputation. It was anticipated that this would complement previous research in which we had shown that changes in cerebral blood volume were associated with the cerebral pain network. Methods: Ten patients with phantom limb pain due to unilateral arm amputation and sixteen healthy age-matched controls were enrolled. The intensity of phantom limb pain was measured by the visual analogue scale (VAS) and depressive mood was assessed by the Hamilton depression rating scale. Diffusion tensor-derived parameters, including fractional anisotropy, mean diffusivity, axial diffusivity (AD), and radial diffusivity (RD), were computed from the DTI. Results: Compared with controls, the cases had alterations in the cerebral white matter as a consequence of phantom limb pain, manifesting a higher AD of white matter in both hemispheres symmetrically after adjusting for individual depressive moods. In addition, there were associations between the RD of white matter and VAS scores primarily in the hemispheres related to the missing hand and in the corpus callosum. Conclusions: The phantom limb pain after unilateral arm amputation induced plasticity in the white matter. We conclude that loss of white matter integrity, particularly in the hemisphere connected with the missing hand, is significantly correlated with phantom limb pain.

• Journal of Korean Academy of Nursing
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• v.49 no.3
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• pp.317-328
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• 2019

Purpose: The purpose of this study was to identify the effect of ghrelin on memory impairment in a rat model of vascular dementia induced by chronic cerebral hypoperfusion. Methods: Randomized controlled groups and the posttest design were used. We established the representative animal model of vascular dementia caused by bilateral common carotid artery occlusion and administered $80{\mu}g/kg$ ghrelin intraperitoneally for 4 weeks. First, behavioral studies were performed to evaluate spatial memory. Second, we used molecular biology techniques to determine whether ghrelin ameliorates the damage to the structure and function of the white matter and hippocampus, which are crucial to learning and memory. Results: Ghrelin improved the spatial memory impairment in the Y-maze and Morris water maze test. In the white matter, demyelination and atrophy of the corpus callosum were significantly decreased in the ghrelin-treated group. In the hippocampus, ghrelin increased the length of hippocampal microvessels and reduced the microvessels pathology. Further, we confirmed angiogenesis enhancement through the fact that ghrelin treatment increased vascular endothelial growth factor (VEGF)-related protein levels, which are the most powerful mediators of angiogenesis in the hippocampus. Conclusion: We found that ghrelin affected the damaged myelin sheaths and microvessels by increasing angiogenesis, which then led to neuroprotection and improved memory function. We suggest that further studies continue to accumulate evidence of the effect of ghrelin. Further, we believe that the development of therapeutic interventions that increase ghrelin may contribute to memory improvement in patients with vascular dementia.

• Journal of the Korean Society for information Management
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• v.36 no.1
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• pp.191-213
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• 2019

The process of obtaining scientific knowledge is conducted through research. Researchers deal with the uncertainty of science and establish certainty of scientific knowledge. In other words, in order to obtain scientific knowledge, uncertainty is an essential step that must be performed. The existing studies were predominantly performed through a hedging study of linguistic approaches and constructed corpus with uncertainty word manually in computational linguistics. They have only been able to identify characteristics of uncertainty in a particular research field based on the simple frequency. Therefore, in this study, we examine pattern of scientific knowledge based on uncertainty word according to the passage of time in biomedical literature where biomedical claims in sentences play an important role. For this purpose, biomedical propositions are analyzed based on semantic predications provided by UMLS and DMR topic modeling which is useful method to identify patterns in disciplines is applied to understand the trend of entity based topic with uncertainty. As time goes by, the development of research has been confirmed that uncertainty in scientific knowledge is moving toward a decreasing pattern.

• Journal of The Korean Society of Inherited Metabolic disease
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• v.20 no.2
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• pp.50-54
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• 2020

Vici syndrome is a rare, autosomal recessive multisystem disorder characterized by agenesis of the corpus callosum, cataracts, cardiomyopathy, hypopigmentation, immunodeficiency, and delayed development. We report the case of a 3-year-old boy diagnosed with Vici syndrome. He initially presented with hypotonia and sucking problem. Whole-exome sequencing identified novel compound heterozygous mutations, namely c.2254C>T (p.Gln752Ter) and c.5511-5518+2 del TATGCAAAGT in the EPG5 gene. The diagnostic challenges can be attributed to the diverse clinical manifestations. Thus, whole-exome sequencing is a useful diagnostic tool for the genetically and clinically heterogeneous Vici syndrome. This is the first Korean report of a patient with Vici syndrome.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.469-484
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• 2022

Since a large text corpus contains hundred-thousand unique words, text data is one of the typical large-dimensional data. Therefore, various feature selection methods have been proposed for dimension reduction. Feature selection methods can improve the prediction accuracy. In addition, with reduced data size, computational efficiency also can be achieved. The chi-square statistic and the information gain are two of the most popular measures for identifying interesting terms from text data. In this paper, we investigate the theoretical properties of the chi-square statistic and the information gain. We show that the two filtering metrics share theoretical properties such as non-negativity and convexity. However, they are different from each other in the sense that the information gain is prone to select more negative features than the chi-square statistic in imbalanced text data.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.35-46
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• 2023

In this study, we propose a Dual Approach methodology to enhance the accuracy of document classifiers by utilizing both contextual and keyword information. Firstly, contextual information is extracted using Google's BERT, a pre-trained language model known for its outstanding performance in various natural language understanding tasks. Specifically, we employ KoBERT, a pre-trained model on the Korean corpus, to extract contextual information in the form of the CLS token. Secondly, keyword information is generated for each document by encoding the set of keywords into a single vector using an Autoencoder. We applied the proposed approach to 40,130 documents related to healthcare and medicine from the National R&D Projects database of the National Science and Technology Information Service (NTIS). The experimental results demonstrate that the proposed methodology outperforms existing methods that rely solely on document or word information in terms of accuracy for document classification.

• The Journal of Korean Institute of Next Generation Computing
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• v.13 no.2
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• pp.102-110
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• 2017

Current word sense disambiguation techniques employ various machine learning-based methods. Various approaches have been proposed to address this problem, including the knowledge base approach. This approach defines the sense of an ambiguous word in accordance with knowledge base information with no training corpus. In unsupervised learning techniques that use a knowledge base approach, graph-based and similarity-based methods have been the main research areas. The graph-based method has the advantage of constructing a semantic graph that delineates all paths between different senses that an ambiguous word may have. However, unnecessary semantic paths may be introduced, thereby increasing the risk of errors. To solve this problem and construct a fine-grained graph, in this paper, we propose a model that iteratively constructs the graph while eliminating unnecessary nodes and edges, i.e., senses and semantic paths. The hybrid similarity estimation model was applied to estimate a more accurate sense in the constructed semantic graph. Because the proposed model uses BabelNet, a multilingual lexical knowledge base, the model is not limited to a specific language.

• Journal of the Korean Society of Radiology
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• v.81 no.4
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• pp.985-989
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• 2020

Chlorfenapyr is widely used as an insecticide, despite it being fatal to humans. However, chlorfenapyr-induced central nervous system toxicity has rarely been reported. We report the magnetic resonance imaging (MRI) findings in a rare case of chlorfenapyr-induced toxic leukoencephalopathy. A 71-year-old man who had ingested chlorfenapyr approximately two weeks prior visited our hospital and presented with bilateral lower motor weakness and voiding dysfunction that had developed two days before admission. Brain MRI revealed extensive bilateral white matter abnormalities involving the corpus callosum, internal capsule, brain stem, and bilateral middle cerebellar peduncle. Furthermore, spine MRI revealed diffuse swelling and hyperintensity on the T2-weighted images.

• The KIPS Transactions:PartB
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• v.11B no.6
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• pp.749-758
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• 2004

In this paper, we propose a new method utilizing only raw corpus without additional human effort for disambiguation of target word selection in English-Korean machine translation. We use two data-driven techniques; one is the Latent Semantic Analysis(LSA) and the other the Probabilistic Latent Semantic Analysis(PLSA). These two techniques can represent complex semantic structures in given contexts like text passages. We construct linguistic semantic knowledge by using the two techniques and use the knowledge for target word selection in English-Korean machine translation. For target word selection, we utilize a grammatical relationship stored in a dictionary. We use k- nearest neighbor learning algorithm for the resolution of data sparseness Problem in target word selection and estimate the distance between instances based on these models. In experiments, we use TREC data of AP news for construction of latent semantic space and Wail Street Journal corpus for evaluation of target word selection. Through the Latent Semantic Analysis methods, the accuracy of target word selection has improved over 10% and PLSA has showed better accuracy than LSA method. finally we have showed the relatedness between the accuracy and two important factors ; one is dimensionality of latent space and k value of k-NT learning by using correlation calculation.