• The Transactions of The Korean Institute of Electrical Engineers
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• v.60 no.1
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• pp.167-173
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• 2011

Most of the terminologies used in medical domain is not intended to be applied directly in clinical setting but is developed to integrate the terms by defining the reference terminology or concept relations between the terms. Therefore, it is needed to develop the subsets of the terminology which classify categories properly for the purpose of use and extract and organize terms with high utility based on the classified categories in order to utilize the clinical terms conveniently as well as efficiently. Moreover, it is also necessary to develop and upgrade the terminology constantly to meet user's new demand by changing or correcting the system. This study has developed a mapping tool that allows accurate expression and interpretation of clinical terms used for medical records in electronic medical records system and can furthermore secure semantic interoperability among the terms used in the medical information model and generate common terms as well. The system is designed to execute both 1:1 and N:M mapping between the concepts of terms at a time and search for and compare various terms at a time, too. Also, in order to enhance work consistency and work reliability between the task performers, it allows work in parallel and the observation of work processes. Since it is developed with Java, it adds new terms in the form of plug-in to be used. It also reinforce database access security with Remote Method Invocation (RMI). This research still has tasks to be done such as complementing and refining and also establishing management procedures for registered data. However, it will be effectively used to reduce the time and expenses to generate terms in each of the medical institutions and improve the quality of medicine by providing consistent concepts and representative terms for the terminologies used for medical records and inducing proper selection of the terms according to their meaning.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.12
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• pp.2211-2217
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• 2016

The Locator/Identifier Separation Protocol (LISP) is a new routing architecture that implements a new semantic for IP addressing. It enables the separation of IP addresses into two new numbering spaces: Endpoint Identifiers (EIDs) and Routing Locators (RLOCs). This approach will solve the issue of rapid growth of the Internet's DFZ (default-free zone). In this paper, we propose an algorithm called the Propagated-Mode Mapping System to improve the map request process of LISP-DDT.

• Journal of KIISE:Software and Applications
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• v.33 no.1
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• pp.114-129
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• 2006

Ontology mapping is the task of finding semantic correspondences between two ontologies. In order to improve the effectiveness of ontology mapping, we need to consider the characteristics and constraints of data models used for implementing ontologies. Earlier research on ontology mapping, however, has proven to be inefficient because the approach should transform input ontologies into graphs and take into account all the nodes and edges of the graphs, which ended up requiring a great amount of processing time. In this paper, we propose a multi-strategic mapping approach to find correspondences between ontologies based on the syntactic or semantic characteristics and constraints of the topic maps. Our multi-strategic mapping approach includes a topic name-based mapping, a topic property-based mapping, a hierarchy-based mapping, and an association-based mapping approach. And it also uses a hybrid method in which a combined similarity is derived from the results of individual mapping approaches. In addition, we don't need to generate a cross-pair of all topics from the ontologies because unmatched pairs of topics can be removed by characteristics and constraints of the topic maps. For our experiments, we used oriental philosophy ontologies, western philosophy ontologies, Yahoo western philosophy dictionary, and Yahoo german literature dictionary as input ontologies. Our experiments show that the automatically generated mapping results conform to the outputs generated manually by domain experts, which is very promising for further work.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2006.05a
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• pp.617-618
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• 2006

The participants in collaborative environment want to get the right documents which are intended to find. In general search system, it searches documents which contain only the keywords. For searching different word-expressions for the same meaning, we perform mapping before searching. Our mapping logic consists of three steps. First, the character matching is the mapping of two terminologies that have identical character strings. Second, the definition comparing is the method that compares two terminologies' definitions. Third, the similarity checking pairs terminologies which were not mapped by two prior steps. In this paper, we propose Engineering Information Search System based on ontology mapping.

• KIPS Transactions on Software and Data Engineering
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• v.5 no.2
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• pp.69-78
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• 2016

Ischemic stroke is a disease that the brain tissues cannot function by reducing blood flow due to thrombosis or embolisms. Due to the nature of the disease, it is most important to identify the status of cerebral vessel and the medical images are necessarily used for its diagnosis. Among many indicators, brain MRI is most widely utilized because experts can effectively obtain the semantic information such as cerebral anatomy aiding the diagnosis with it. However, in case of emergency diseases like ischemic stroke, even though a intelligent system is required for supporting the prompt diagnosis and treatment, the current systems have some difficulties to provide the information of medical images intuitively. In other words, as the current systems have managed the medical images based on the basic meta-data such as image name, ID and so on, they cannot consider semantic information inherent in medical images. Therefore, in this paper, to provide core information like cerebral anatomy contained in brain MRI, we suggest a template-driven medical images mapping method. The key idea of the method is defining the mapping characteristics between anatomic feature and representative images by using template images that can be representative of the whole brain MRI image set and revealing the semantic relations that only medical experts can check between images. With our method, it will be possible to manage the medical images based on semantic.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.9
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• pp.753-758
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• 2016

In this paper, we propose a novel method for estimating a vehicle's current position, even on roads that have similar patterns. In the proposed method, we classified the semantic information of the nodes in detail and added the semantic information of the link to solve the problem due to similar and repeated patterns. We also improved the mapping method by comparing the result of the duplicated matching with that of the only matching obtained just before corresponding duplicated matching. From the simulation results, we verify that the performance of the proposed method is better than that of the existing method.

• The Journal of Information Systems
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• v.18 no.3
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• pp.327-349
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• 2009

Traditional keyword-based searching methods suffer from low accuracy and high complexity due to the rapid growth in the amount of information. Accordingly, many researchers attempt to implement a so-called semantic search which is based on the semantics of the user's query. Semantic information can be described using a semantic modeling language, such as Topic Map. In this paper, we propose a new method to map a topic map to a traditional Relational Database (RDB) without any information loss. Although there have been a few attempts to map topic maps to RDB, they have paid scant attention to handling multi-role topics. In this paper, we propose a new storage structure to map multi-role topics to traditional RDB. The proposed structure consists of a mapping table, role tables, and content tables. Additionally, we devise a query translator to convert a user's query to one appropriate to the proposed structure.

• Journal of Korea Society of Digital Industry and Information Management
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• v.14 no.4
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• pp.35-45
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• 2018

Users are sharing many of contents such as text, image, video, and so on in SNS. There are various information as like as personal interesting, opinion, and relationship in social media contents. Therefore, many of recommendation systems or search systems are being developed through analysis of social media contents. In order to extract subject-related topics of social context being collected from social media channels in developing those system, it is necessary to develop ontologies for semantic analysis. However, it is difficult to develop formal ontology because social media contents have the characteristics of non-formal data. Therefore, we develop a social topic system based on semantic and subject correlation. First of all, an extracting system of social topic based on semantic relationship analyzes semantic correlation and then extracts topics expressing semantic information of corresponding social context. Because the possibility of developing formal ontology expressing fully semantic information of various areas is limited, we develop a self-extensible architecture of ontology for semantic correlation. And then, a classifier of social contents and feed back classifies equivalent subject's social contents and feedbacks for extracting social topics according semantic correlation. The result of analyzing social contents and feedbacks extracts subject keyword, and index by measuring the degree of association based on social topic's semantic correlation. Deep Learning is applied into the process of indexing for improving accuracy and performance of mapping analysis of subject's extracting and semantic correlation. We expect that proposed system provides customized contents for users as well as optimized searching results because of analyzing semantic and subject correlation.

• International Journal of Contents
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• v.7 no.4
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• pp.108-115
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• 2011

The current study aims to analyze the acceptance of International Standard Terminology (IST) related to herbs and formulas used in Korea. It also intends to examine limitations of each term source by linking texts for herbal medicine research and formula research used in schools of oriental medicine with medicinal substance-formula classification names within the IST framework. This study examined 64 medicinal classification names of IST, including synonyms, 41 formula classification names, 65 classification names of "Herbal Medicine Study," 89 medicinal classification names of "Shin's Clinical Herbal Medicine Study," and lastly 83 formula classification names of "Formula Study." Data on their chief virtue, efficacy and characteristics as medicinal substances were extracted from their definitions, and such data were used to perform Chinese character-English mapping using the IST. The outcomes of the mapping were then analyzed in terms of both lexical matching and semantic matching. In terms of classification names for medicinal substances, "Herbal Medicine Study" had 60.0% lexical matching, whereas "Shin's Clinical Herbal Medicine Study" had 48.3% lexical matching. When semantic matching was also applied, "Herbal Medicine Study" showed a value of 87.7% and "Shin's Clinical Herbal Medicine Study" 74.2%. In terms of formula classification names, lexical matching was 28.9% of 83 subjects, and when semantic matching was also considered, the value was 30.1%. When the conceptual elements of this study were applied, some IST terms that are classified with other codes were found to be conceptually consistent, and some terms were not accepted due to different depths in the classification systems of each source.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.8
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• pp.3942-3961
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• 2019

Recently, the models of deep super-resolution networks can successfully learn the non-linear mapping from the low-resolution inputs to high-resolution outputs. However, for large scaling factors, this approach has difficulties in learning the relation of low-resolution to high-resolution images, which lead to the poor restoration. In this paper, we propose Stage Generative Adversarial Networks (Stage-GAN) with semantic maps for image super-resolution (SR) in large scaling factors. We decompose the task of image super-resolution into a novel semantic map based reconstruction and refinement process. In the initial stage, the semantic maps based on the given low-resolution images can be generated by Stage-0 GAN. In the next stage, the generated semantic maps from Stage-0 and corresponding low-resolution images can be used to yield high-resolution images by Stage-1 GAN. In order to remove the reconstruction artifacts and blurs for high-resolution images, Stage-2 GAN based post-processing module is proposed in the last stage, which can reconstruct high-resolution images with photo-realistic details. Extensive experiments and comparisons with other SR methods demonstrate that our proposed method can restore photo-realistic images with visual improvements. For scale factor ${\times}8$, our method performs favorably against other methods in terms of gradients similarity.