• Asian Journal of Innovation and Policy
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• v.10 no.3
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• pp.294-315
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• 2021

Since the division of South and North Korea in 1945, there has been little exchange in science and technology, despite some interchange in a few fields including the chemistry area. Accordingly, the difference in scientific and technological terminology between the two Koreas has become intensified. This is because North Korea carried out a campaign to purify the Korean language and blocked the inflow of foreign words. They also tried to convert into their own North Korean terms in many fields. This circumstance in North Korea aggravated the heterogeneity of inter-Korean scientific and technological terms. In particular, the heterogeneity of chemical terminology has worsened due to the different characteristics of the technology donor countries such as the United States and Japan in South Korea, and China and the Soviet Union in North Korea between the two Koreas and the different way of technological development. The purpose of this study is to collect chemical terminology data used in two Koreas and analyze similarities and differences. Through comparative analysis of inter-Korean terminology in the chemical field, it can be possible to recognize how the chemical terms between the two Koreas have changed since the division and the degree of heterogeneity based on different technical systems and language policies. The outcome of this study would present basic data on the unification of chemical terminology in preparation for before and after unification, and contribute to communication and academic exchange between researchers in the inter-Korean scientific and technological fields, including chemistry.

• Journal of Korean Elementary Science Education
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• v.28 no.3
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• pp.331-339
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• 2009

The purpose of this study is to make a list of scientific terms to decrease students' difficulties of science learning. By using inductive method, database has established from elementary school science textbooks and laboratory observation books. All terms from physics area of science textbooks and laboratory observation books at the levels of grade 3 to 6 were analyzed based on the Standard Korean Dictionary (1999) and Book of Physics Terminology (2005). As a result, we made a list of 204 scientific terms by grade level. Those were 51 words for grade 3, 55 words for grade 4, 56 words for grade 5, and 42 words for grade 6. And there were some incongruities among textbooks, the Standard Korean Dictionary and the Book of Physics Terminology.

• Journal of the Korean Society for information Management
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• v.28 no.1
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• pp.89-104
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• 2011

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In order to extract these entities automatically from scientific documents at once, we developed a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer and terminology extractor.

• Korean Journal of Oriental Medicine
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• v.1 no.1
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• pp.101-125
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• 1995

The terminology used for oriental medicine has not yet been standardized so far and this might cause the problems in developing theories and clinical research of oriental medicine. To establish scientific backgroupd of oriental medicine, it is required that all the terminology used for oriental medicine should be standardized and unified. For more efficient oriental medical practice, the standardization, unification of the terms and conditions used for diagnosis in oriental medicine should be achieved. The aim of this study are as follows; 1. To provide clear and logical systems for the diagnosis of symptoms and diseases. 2. To provide the theoritical clearmess of oriental medicine and to promote the public facilities for study. 3. To provide ways of idea exchange and understanding between oriental medicine and various biological sciences. 4. To provide practical basis for hospital administration for oriental medicine.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.62 no.3
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• pp.293-299
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• 2013

Power system technologies in the area of smart grid are being converged with communication system technologies or information technologies(IT). This interdisciplinary action affects on two parts vigorously so that IT has become a essential part of power industry. But terminologies of two parts are confused and it is necessary that these are standardized to develop the industry. The interpretation works with scientific and technological standard of the smart grid terminologies which are IEC TC 57 glossary and non-glossary terminologies are introduced. Based on the interpretation works of the smart grid terminologies, an online terminology information system for the standardization of smart grid terminologies is proposed. The system provides the capability of collection and delivery of academic society opinions for the selected and interpreted terminologies.

• The Journal of Korean Medicine
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• v.32 no.6
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• pp.85-94
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• 2011

Objectives: This pilot study was carried out with the purpose of suggesting a methodology on mapping and registering Korean medicinal material concepts at 2011AB of the UMLS. Methods: 411 medicinal material concepts were mapped to biomedical terminology within the metathesaurus of the UMLS. Based on the forms of listing on the UMLS and thesauri information, the medicinal material concepts were classified into three groups and mapped. Results: 76 concepts in Group 1 which English CUIs have Chinese AUIs were mapped considering scientific names and Chinese strings. 259 concepts in Group 2 that have CUIs in the form of 'botanical name/Chinese pinyin' were mapped with the information of Korean and Chinese Pharmacopoeias. Groups 3, 76 concepts of English names in the Korean Pharmacopoeia are matched considering their botanic names and used parts. Conclusion: This study suggested a methodology to map Korean medicinal material concepts to international standard terminology, which will help ensure interoperability and compatibility between traditional medicine terminology and the UMLS.

• Journal of Information Management
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• v.2 no.3
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• pp.2-4
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• 1964

Scientific documentation is one of the three preconditions for scientific research and its technical application in the industrialization process. To solve the documentation problems on an international level, UNESCO promotes abstracting, standardization of terminology and of scientifc publications, automatic information processing and translating. Moreover, UNESCO has helped to establish since 1950, on the request of interested national governments, 12 documentation centers within the framework of United Nations Technical Assistance Programme by providing experts, fellowships and equipment. Some data on these documentation centers and a comparison of their features should be of interest for all those who wish to set up new documentation centers or to improve already existing ones.

• Journal of Information Management
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• v.40 no.4
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• pp.1-22
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• 2009

Large-scaled information extraction plays an important role in advanced information retrieval as well as question answering and summarization. Information extraction can be defined as a process of converting unstructured documents into formalized, tabular information, which consists of named-entity recognition, terminology extraction, coreference resolution and relation extraction. Since all the elementary technologies have been studied independently so far, it is not trivial to integrate all the necessary processes of information extraction due to the diversity of their input/output formation approaches and operating environments. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we define scientific as a set of 10 types of named entities and technical terminologies in a biomedical domain. in order to automatically extract these entities from scientific documents at once, we develop a framework for scientific core entity extraction which embraces all the pivotal language processors, named-entity recognizer, co-reference resolver and terminology extractor. Each module of the integrated system has been evaluated with various corpus as well as KEEC 2009. The system will be utilized for various information service areas such as information retrieval, question-answering(Q&A), document indexing, dictionary construction, and so on.

• Proceedings of the Korea Contents Association Conference
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• 2010.05a
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• pp.425-427
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• 2010

Large-scaled information extraction consists of named-entity recognition, terminology extraction and relation extraction. Since all the elementary technologies have been studied independently so far, test collections for related machine learning models also have been constructed independently. As a result, it is difficult to handle scientific documents to extract both named-entities and technical terms at once. In this study, we integrate named-entities and terminologies with PLOT(Person, Location, Organization, Terminology) in a biomedical domain and construct a test collection of PLOT and relations between PLOTs.

• The KIPS Transactions:PartD
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• v.18D no.5
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• pp.329-338
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• 2011

Terminology recognition system which is a preceding research for text mining, information extraction, information retrieval, semantic web, and question-answering has been intensively studied in limited range of domains, especially in bio-medical domain. We propose a domain independent terminology recognition system based on machine learning method using dictionary, syntactic features, and Web search results, since the previous works revealed limitation on applying their approaches to general domain because their resources were domain specific. We achieved F-score 80.8 and 6.5% improvement after comparing the proposed approach with the related approach, C-value, which has been widely used and is based on local domain frequencies. In the second experiment with various combinations of unithood features, the method combined with NGD(Normalized Google Distance) showed the best performance of 81.8 on F-score. We applied three machine learning methods such as Logistic regression, C4.5, and SVMs, and got the best score from the decision tree method, C4.5.