• Interdisciplinary Bio Central
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• 제3권2호
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• pp.7.1-7.6
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• 2011

Background: Published manuscripts are the main source of biological knowledge. Since the manual examination is almost impossible due to the huge volume of literature data (approximately 19 million abstracts in PubMed), intelligent text mining systems are of great utility for knowledge discovery. However, most of current text mining tools have limited applicability because of i) providing abstract-based search rather than sentence-based search, ii) improper use or lack of ontology terms, iii) the design to be used for specific subjects, or iv) slow response time that hampers web services and real time applications. Results: We introduce an advanced text mining system called PubMine that supports intelligent knowledge discovery based on diverse bio-ontologies. PubMine improves query accuracy and flexibility with advanced search capabilities of fuzzy search, wildcard search, proximity search, range search, and the Boolean combinations. Furthermore, PubMine allows users to extract multi-dimensional relationships between genes, diseases, and chemical compounds by using OLAP (On-Line Analytical Processing) techniques. The HUGO gene symbols and the MeSH ontology for diseases, chemical compounds, and anatomy have been included in the current version of PubMine, which is freely available at http://pubmine.kobic.re.kr. Conclusions: PubMine is a unique bio-text mining system that provides flexible searches and analysis of biological entity relationships. We believe that PubMine would serve as a key bioinformatics utility due to its rapid response to enable web services for community and to the flexibility to accommodate general ontology.

• 한국생물정보학회:학술대회논문집
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• 한국생물정보시스템생물학회 2005년도 BIOINFO 2005
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• pp.101-106
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• 2005

Recently, as the size of genetic knowledge grows faster, automated analysis and systemization into high-throughput database has become hot issue. One essential task is to recognize and identify genomic entities and discover their relations. However, ambiguity of name entities is a serious problem because of their multiplicity of meanings and types. So far, many effective techniques have been proposed to analyze documents. Yet, accuracy is high when the data fits the model well. The purpose of this paper is to design and implement a document classification system for identifying entity problems using text/data mining combination, supplemented by rich data mining algorithms to enhance its performance. we propose RTP ost system of different style from any traditional method, which takes fault tolerant system approach and data mining strategy. This feedback cycle can enhance the performance of the text mining in terms of accuracy. We experimented our system for classifying RB-related documents on PubMed abstracts to verify the feasibility.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제11권8호
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• pp.4133-4145
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• 2017

Advances in science and technology are driving us to the better life but also forcing us to make more investment at the same time. Therefore, the government has provided the investment to carry on the promising futuristic technology successfully. Indeed, a lot of resources from the government have supported into the science and technology R&D projects for several decades. However, the performance of the public investments remains unclear in many ways, so thus it is required that planning and evaluation about the new investment should be on data driven decision with fact based evidence. In this regard, the government wanted to know the trend and issue of the science and technology with evidences, and has accumulated an amount of database about the science and technology such as research papers, patents, project reports, and R&D information. Nowadays, the database is supporting to various activities such as planning policy, budget allocation, and investment evaluation for the science and technology but the information quality is not reached to the expectation because of limitations of text mining to drill out the information from the unstructured data like the reports and papers. To solve the problem, this study proposes a practical text mining methodology for the science and technology trend analysis, in case of aerospace technology, and conduct text mining methods such as ontology development, topic analysis, network analysis and their visualization.

• 한국컴퓨터정보학회논문지
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• 제24권7호
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• pp.125-133
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• 2019

Because military weapons systems for special purposes are small and complex, they are not easy to maintain. Therefore, it is very important to maintain combat strength through quick maintenance in the event of a breakdown. In particular, naval ships are complex weapon systems equipped with various equipment, so other equipment must be considered for maintenance in the event of equipment failure, so that skilled maintenance personnel have a great influence on rapid maintenance. Therefore, in this paper, we analyzed maintenance data of defense equipment maintenance information system through text mining and social network analysis(SNA), and tried to identify the naval ship maintenance expertise. The defense equipment maintenance information system is a system that manages military equipment efficiently. In this study, the data(2,538cases) of some naval ship maintenance teams were analyzed. In detail, we examined the contents of main maintenance and maintenance personnel through text mining(word cloud, word network). Next, social network analysis(collaboration analysis, centrality analysis) was used to confirm the collaboration relationship between maintenance personnel and maintenance expertise. Finally, we compare the results of text mining and social network analysis(SNA) to find out appropriate methods for finding and finding naval ship maintenance expertise.

• 한국임상약학회지
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• 제27권4호
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• pp.221-227
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• 2017

Background: As personalized healthcare industry has attracted much attention, big data analysis of healthcare data is essential. Lots of healthcare data such as product labeling, biomedical literature and social media data are unstructured, extracting meaningful information from the unstructured text data are becoming important. In particular, text mining for adverse drug reactions (ADRs) reports is able to provide signal information to predict and detect adverse drug reactions. There has been no study on text analysis of expert opinion on Korea Adverse Event Reporting System (KAERS) databases in Korea. Methods: Expert opinion text of KAERS database provided by Korea Institute of Drug Safety & Risk Management (KIDS-KD) are analyzed. To understand the whole text, word frequency analysis are performed, and to look for important keywords from the text TF-IDF weight analysis are performed. Also, related keywords with the important keywords are presented by calculating correlation coefficient. Results: Among total 90,522 reports, 120 insulin ADR report and 858 tramadol ADR report were analyzed. The ADRs such as dizziness, headache, vomiting, dyspepsia, and shock were ranked in order in the insulin data, while the ADR symptoms such as vomiting, 어지러움, dizziness, dyspepsia and constipation were ranked in order in the tramadol data as the most frequently used keywords. Conclusion: Using text mining of the expert opinion in KIDS-KD, frequently mentioned ADRs and medications are easily recovered. Text mining in ADRs research is able to play an important role in detecting signal information and prediction of ADRs.

• 한국정보통신학회:학술대회논문집
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• 한국정보통신학회 2019년도 춘계학술대회
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• pp.325-326
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• 2019

본 논문은 텍스트 마이닝 기법으로 명사의 빈도수를 조사하여 워드클라우드를 나타내는 기존의 방법을 개선하여 지능적 워드클라우드를 구현하는 방법을 제안한다. 텍스트 마이닝 시에 명사 단어를 추출하는 사전에 누락된 신조어 등의 단어를 효과적으로 추가하고, 동사 등 다른 품사위주의 워드클라우드를 시각적으로 보여주는 방법을 제안한다. 실험에서 기존 명사의 빈도수 추출에는 KoNLP 패키지를 사용하였고, 지원되지 않는 신조어 80개를 추가하였고 빈도수를 수동으로 조사하여 추가하였다.

• 한국정보시스템학회지:정보시스템연구
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• 제28권4호
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• pp.155-174
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• 2019

Purpose Social interest in financial statement notes has recently increased. However, contrary to the keen interest in financial statement notes, there is no morphological analyzer for accounting terms, which is why researchers are having considerable difficulty in carrying out research. In this study, we build a morphological analyzer for accounting related text mining techniques. This morphological analyzer can handle accounting terms like financial statements and we expect it to serve as a springboard for growth in the text mining research field. Design/methodology/approach In this study, we build customized korean morphological analyzer to extract proper accounting terms. First, we collect Company's Financial Statement notes, financial information data published by KPFIS(Korea Public Finance Information Service), K-IFRS accounting terms data. Second, we cleaning and tokeninzing and removing stopwords. Third, we customize morphological analyzer using n-gram methodology. Findings Existing morphological analyzer cannot extract accounting terms because it split accounting terms to many nouns. In this study, the new customized morphological analyzer can detect more appropriate accounting terms comparing to the existing morphological analyzer. We found that accounting words that were not detected by existing morphological analyzers were detected in new customized morphological analyzers.

• 한국산업융합학회 논문집
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• 제27권3호
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• pp.581-590
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• 2024

This study aims to extract key topics through text mining of incident records (incident history, post-incident measures, preventive measures) from construction safety accident case data available on the public data portal. It also seeks to provide fundamental insights contributing to the establishment of manuals for disaster prevention by identifying correlations between these topics. After pre-processing the input data, we used the LDA-based topic modeling technique to derive the main topics. Consequently, we obtained five topics related to incident history, and four topics each related to post-incident measures and preventive measures. Although no dominant patterns emerged from the topic pattern analysis, the study holds significance as it provides quantitative information on the follow-up actions related to the incident history, thereby suggesting practical implications for the establishment of a preventive decision-making system through the linkage between accident history and subsequent measures for reccurrence prevention.

• 품질경영학회지
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• 제47권3호
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• pp.597-613
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• 2019

Purpose: The purpose of this study is to analyze the trend of quality research by analyzing the entire JKSQM(Journal of the Korean Society for Quality Management). Methods: This study is to analyze the frequency of words used in the abstract of the all JKSQM by applying the text mining processing. We use wordcrowd among text mining techniques. Results: 22 words of high frequency were presented in the abstract of the paper published in the JKSQM for 42 years. The frequency of words was shown on a 10 year basis, and the four important words were plotted on a change graph for each Vol. Frequent words of each Vol. are added in the appendix. Conclusion: The main research results are as follows. First, there has been no significant change in research trends over the last 40 years. Second, the early SQC words have been widely used, and since 1990, many words such as service-oriented words have been used, indicating a change in the times. Third, the use of the words of the 4th industrial revolution since 2010 is weak. In the above analysis, the trend of quality research in Korea is within the quality category and can be considered conservative. Now, it is expected that everything will be changed in the period of the 4th Industrial Revolution, and it is time to study the direction of quality in Korea.

• 인터넷정보학회논문지
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• 제19권1호
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• pp.155-166
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• 2018

These days, online media, such as blogospheres, online communities, and social networking sites, provides the uncountable user-generated content (UGC) to discover market intelligence and business insight with. The business has been interested in consumers, and constantly requires the approach to identify consumers' opinions and competitive advantage in the competing market. Analyzing consumers' opinion about oneself and rivals can help decision makers to gain in-depth and fine-grained understanding on the human and social behavioral dynamics underlying the competition. In order to accomplish the comparison study for rival products and companies, we attempted to do competitive analysis using text mining with online UGC for two popular and competing ramens, a market leader and a market follower, in the Korean instant noodle market. Furthermore, to overcome the lack of the Korean sentiment lexicon, we developed the domain specific sentiment dictionary of Korean texts. We gathered 19,386 pieces of blogs and forum messages, developed the Korean sentiment dictionary, and defined the taxonomy for categorization. In the context of our study, we employed sentiment analysis to present consumers' opinion and statistical analysis to demonstrate the differences between the competitors. Our results show that the sentiment portrayed by the text mining clearly differentiate the two rival noodles and convincingly confirm that one is a market leader and the other is a follower. In this regard, we expect this comparison can help business decision makers to understand rich in-depth competitive intelligence hidden in the social media.