• Journal of the Korea Institute of Information Security & Cryptology
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• v.22 no.3
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• pp.565-573
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• 2012

To investigate the business corruption, the obtainments of the business data such as personnel, manufacture, accounting and distribution etc., is absolutely necessary. Futhermore, the investigator should have the systematic extraction solution from the business data of the enterprise database, because most company manage each business data through the distributed database system, In the general business environment, the database exists in the system with upper layer application and big size file server. Besides, original resource data which input by user are distributed and stored in one or more table following the normalized rule. The earlier researches of the database structure analysis mainly handled the table relation for database's optimization and visualization. But, in the point of the digital forensic, the data, itself analysis is more important than the table relation. This paper suggests the extraction technique from the table relation which already defined in the database. Moreover, by the systematic analysis process based on the domain knowledge, analyzes the original business data structure stored in the database and proposes the solution to extract table which is related incident.

• The Journal of the Korea Contents Association
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• v.19 no.11
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• pp.278-287
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• 2019

Spatial information extraction is to retrieve static and dynamic aspects in natural language text by explicitly marking spatial elements and their relational words. This paper proposes a deep learning approach for spatial information extraction for Korean language using a two-step bidirectional LSTM-CRF ensemble model. The integrated model of spatial element extraction and spatial relation attribute extraction is proposed too. An experiment with the Korean SpaceBank demonstrates the better efficiency of the proposed deep learning model than that of the previous CRF model, also showing that the proposed ensemble model performed better than the single model.

• Journal of Information Processing Systems
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• v.7 no.3
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• pp.459-472
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• 2011

The vast number of biomedical literature is an important source of biomedical interaction information discovery. However, it is complicated to obtain interaction information from them because most of them are not easily readable by machine. In this paper, we present a method for extracting biomedical interaction information assuming that the biomedical Named Entities (NEs) are already identified. The proposed method labels all possible pairs of given biomedical NEs as INTERACTION or NO-INTERACTION by using a Maximum Entropy (ME) classifier. The features used for the classifier are obtained by applying various NLP techniques such as POS tagging, base phrase recognition, parsing and predicate-argument recognition. Especially, specific verb predicates (activate, inhibit, diminish and etc.) and their biomedical NE arguments are very useful features for identifying interactive NE pairs. Based on this, we devised a twostep method: 1) an interaction verb extraction step to find biomedically salient verbs, and 2) an argument relation identification step to generate partial predicate-argument structures between extracted interaction verbs and their NE arguments. In the experiments, we analyzed how much each applied NLP technique improves the performance. The proposed method can be completely improved by more than 2% compared to the baseline method. The use of external contextual features, which are obtained from outside of NEs, is crucial for the performance improvement. We also compare the performance of the proposed method against the co-occurrence-based and the rule-based methods. The result demonstrates that the proposed method considerably improves the performance.

• The korean journal of orthodontics
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• v.23 no.1 s.40
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• pp.123-136
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• 1993

The purpose of this report is to present the successful improvement of occlusal relationship and facial esthetics in class II division 1 malocclusion with severe labioversion of upper anterior teeth and severe overjet, and in class II malocclusion with infraversion of bilateral maxillary canines by MEAW mechanics, which enables us to get effective distal on mass movement of maxillary dentition, with upper second molar extraction. After treatment, there were natural contact points at canine and premolar regions, normal occlusal relation-ship and treatment results, satisfied the gnathologic concept, in this 2 cases. Compared with the routine treatment with premolar extraction, the treatment time and patients' discomfort were reduced. And the MEAW mechanics, which enables us to get effective distal on mass movement of maxillary dentition, resulted in reduction of the treatment time and getting the good treatment results. Consequently, the majot concerns in orthodontic treatment are appropriate diagnosis and treatment plan, so, the application of second molar extraction with appropriate case analysis and diagnosis is very helpful to orthodontic treatment.

• Korean Journal of Environmental Agriculture
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• v.32 no.3
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• pp.171-178
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• 2013

BACKGROUND: Consensus of heavy metal phytoavailability in soils needs to be introduced for soil management protocols in relation to safer food production in the contaminated agricultural soils. For this, setting up the method for evaluation of metal phytoavailability in soil is an essential prerequisite. METHODS AND RESULTS: The current study was carried to select a proper single extraction method for determination of phytoavailable metal concentration in soil. Two extraction methods were examined including 1 M $NH_4NO_3$ extraction and 0.01 M $Ca(NO_3)_2$ extraction methods using 142 soil samples collected from the agricultural soils nearby abandoned mining area in Korea. Corelation analysis was conducted between phytoavailable metal concentrations and soil properties potentially influencing on the metal phytoavailability. Both methods showed similar significance (p<0.001) in correlation with soil properties such as soil pH. However, higher correlation coefficients between phytoavailable metal concentrations and soil properties were observed when used $Ca(NO_3)_2$ extraction rather than using $NH_4NO_3$ extraction. CONCLUSION(S): It appeared that 0.01 M $Ca(NO_3)_2$ extraction was better option for determination of phytoavailable metals in soils and further study to test the efficiency of this method is required in combination with plant uptake.

• Journal of the Korean Society of Food Science and Nutrition
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• v.22 no.1
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• pp.73-77
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• 1993

This study was intended to investigate the effects of solvents, particle size of a sample, sample to solvent ratio, temperature and time on the extraction of garlic oleoresin. Among eleven solvents used for oleoresin extraction from garlic, the optimal solvent was methyl alcohol. The most appropriate particle size of garlic, extracting temperature and mixing ratio of garlic to methyl alcohol were 20mesh, $25^{\circ}C$ and 1 to 3(w/w), respectively. On the basis of yield in oleoresin extraction, optimum extracting time was about 4 hours. The yield of oleoresin under the above-mentioned conditions was 21.3%. "L" and "b" as color appearance were decreased, whereas "a"was increased slightly during 60 days storage at 5$^{\circ}C$, $25^{\circ}C$ and 4$0^{\circ}C$. Changes in the pyruvate content showed close relation to pH value. During storage pyruvate content of garlic oleoresin was decreased as increasing storage temperature.

• Journal of Information Processing Systems
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• v.16 no.2
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• pp.447-459
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• 2020

After reading a document, people construct a concept about the information they consumed and merge multiple words to set up keywords that represent the material. With that in mind, this study suggests a smarter and more efficient keyword extraction method wherein scholarly journals are used as the basis for the establishment of production rules based on a concept information of words appearing in a document in a way in which author-provided keywords are functional although they do not appear in the body of the document. This study presents a new way to determine the importance of each keyword, excluding non-relevant keywords. To identify the validity of extracted keywords, titles and abstracts of journals about natural language and auditory language were collected for analysis. The comparison of author-provided keywords with the keyword results of the developed system showed that the developed system was highly useful, with an accuracy rate as good as up to 96%.

• 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.

• Journal of KIISE:Software and Applications
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• v.37 no.6
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• pp.464-469
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• 2010

In this paper, we propose a method to extract isa/instanceOf relation from category structure. Existing researches use lexical patterns to get isa/instanceOf relation from the category structure, e.g. head word matching, to determine whether the given category link is isa/instanceOf relation or not. In this paper, we propose a new approach which analyzes other category links related to the given category link to determine whether the given category link is isa/instanceOf relation or not. The experimental result shows that our algorithm can cover many cases which the existing algorithms were not able to deal with.

• Annual Conference on Human and Language Technology
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• 2021.10a
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• pp.482-485
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• 2021

최근 지식 그래프를 확장하기 위해 많은 연구가 진행되고 있다. 지식 그래프를 확장하기 위해서는 relation을 기준으로 entity의 방향성을 고려하는 것이 매우 중요하다. 지식 그래프를 확장하기 위한 대표적인 연구인 관계 추출은 문장과 2개의 entity가 주어졌을 때 relation을 예측한다. 최근 사전학습 언어모델을 적용하여 관계 추출에서 높은 성능을 보이고 있지만, entity에 대한 방향성을 고려하여 relation을 예측하는지 알 수 없다. 본 논문에서는 관계 추출에서 entity의 방향성을 고려하여 relation을 예측하는지 실험하기 위해 문장 수준의 Adversarial Attack과 단어 수준의 Sequence Labeling을 적용하였다. 또한 관계 추출에서 문장에 대한 이해를 높이기 위해 BERT모델을 적용하여 실험을 진행하였다. 실험 결과 관계 추출에서 entity에 대한 방향성을 고려하지 않음을 확인하였다.