• Proceedings of the Korean Society for Information Management Conference
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• 2001.08a
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• pp.197-200
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• 2001

말모둠은 디지털화된 텍스트의 모음으로 주로 언어 연구를 위한 자료로 이용되었다. 그러나 말모둠의 구축사례가 늘면서 검색기법이 다양해지고 내용의 범위가 확대되어 말모둠의 역할은 보다 다양해지고 있다. 본 연구는 말모둠의 개념, 구축현황, 웹사이트에서의 제공 내용 등을 조사하여 디지털도서관에서의 말모둠 활용 가능성을 알아보았다.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.47-73
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• 2020

KTX rolling stocks are a system consisting of several machines, electrical devices, and components. The maintenance of the rolling stocks requires considerable expertise and experience of maintenance workers. In the event of a rolling stock failure, the knowledge and experience of the maintainer will result in a difference in the quality of the time and work to solve the problem. So, the resulting availability of the vehicle will vary. Although problem solving is generally based on fault manuals, experienced and skilled professionals can quickly diagnose and take actions by applying personal know-how. Since this knowledge exists in a tacit form, it is difficult to pass it on completely to a successor, and there have been studies that have developed a case-based rolling stock expert system to turn it into a data-driven one. Nonetheless, research on the most commonly used KTX rolling stock on the main-line or the development of a system that extracts text meanings and searches for similar cases is still lacking. Therefore, this study proposes an intelligence supporting system that provides an action guide for emerging failures by using the know-how of these rolling stocks maintenance experts as an example of problem solving. For this purpose, the case base was constructed by collecting the rolling stocks failure data generated from 2015 to 2017, and the integrated dictionary was constructed separately through the case base to include the essential terminology and failure codes in consideration of the specialty of the railway rolling stock sector. Based on a deployed case base, a new failure was retrieved from past cases and the top three most similar failure cases were extracted to propose the actual actions of these cases as a diagnostic guide. In this study, various dimensionality reduction measures were applied to calculate similarity by taking into account the meaningful relationship of failure details in order to compensate for the limitations of the method of searching cases by keyword matching in rolling stock failure expert system studies using case-based reasoning in the precedent case-based expert system studies, and their usefulness was verified through experiments. Among the various dimensionality reduction techniques, similar cases were retrieved by applying three algorithms: Non-negative Matrix Factorization(NMF), Latent Semantic Analysis(LSA), and Doc2Vec to extract the characteristics of the failure and measure the cosine distance between the vectors. The precision, recall, and F-measure methods were used to assess the performance of the proposed actions. To compare the performance of dimensionality reduction techniques, the analysis of variance confirmed that the performance differences of the five algorithms were statistically significant, with a comparison between the algorithm that randomly extracts failure cases with identical failure codes and the algorithm that applies cosine similarity directly based on words. In addition, optimal techniques were derived for practical application by verifying differences in performance depending on the number of dimensions for dimensionality reduction. The analysis showed that the performance of the cosine similarity was higher than that of the dimension using Non-negative Matrix Factorization(NMF) and Latent Semantic Analysis(LSA) and the performance of algorithm using Doc2Vec was the highest. Furthermore, in terms of dimensionality reduction techniques, the larger the number of dimensions at the appropriate level, the better the performance was found. Through this study, we confirmed the usefulness of effective methods of extracting characteristics of data and converting unstructured data when applying case-based reasoning based on which most of the attributes are texted in the special field of KTX rolling stock. Text mining is a trend where studies are being conducted for use in many areas, but studies using such text data are still lacking in an environment where there are a number of specialized terms and limited access to data, such as the one we want to use in this study. In this regard, it is significant that the study first presented an intelligent diagnostic system that suggested action by searching for a case by applying text mining techniques to extract the characteristics of the failure to complement keyword-based case searches. It is expected that this will provide implications as basic study for developing diagnostic systems that can be used immediately on the site.

• Science of Emotion and Sensibility
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• v.22 no.2
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• pp.49-58
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• 2019

In the era of the 4th Industrial Revolution, artificial intelligence (AI) has become one of the core technologies in terms of the business strategy among information technology companies. Both international and domestic major portal companies are launching AI search services. These AI search services utilize voice, images, and other unstructured data to provide different experiences from existing text-based search services. An unfamiliar experience is a factor that can hinder the usability of the service. Therefore, the usability testing of the AI search services is necessary. This study examines the usability of the AI search service on the Naver App 8.9.3 beta version by comparing it with the search services of the current Naver App and targets 30 people in their 20s and 30s, who have experience using Naver apps. The usability of Smart Lens, Smart Voice, Smart Around, and AiRS, which are the Naver App beta versions of their artificial intelligence search service, is evaluated and statistically significant usability changes are revealed. Smart Lens, Smart Voice, and Smart Around exhibited positive changes, whereas AiRS exhibited negative changes in terms of usability. This study evaluates the change in usability according to the application of the artificial intelligence search services and investigates the correlation between the evaluation factors. The obtained data are expected to be useful for the usability evaluation of services that use AI.

• Journal of the Korean Society for information Management
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• v.7 no.1
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• pp.3-23
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• 1990

The evolution in information and communication technologies and the changing role in information and library systems are raised to force significan changes in Korea. A number of library schools started new subject in their curriculm and revised their present curriculum. As a result the rationale for the revision and contents of new subject is provided from 1970 decade. With long-ranging effect on the library profession we. in Korea, should review for our curricular implications. The information System Department in California state university and FIABID curriculum in Germany are proposed to consider in undergoing transformation in information and library science.

• Journal of the Korean Society for information Management
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• v.14 no.2
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• pp.285-301
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• 1997

Massive amount ofinformation is provided for the internet users. Therfore, the users are exposed even to the useless information. In this paper, a Intelligent Web-Agent system is present as a solution for this kind if users inconvinience. This Intelligent Web-Agent system i devised users to search by the keyword about which they get information and commend the sites which have more intensive relation with the examine keyword, judge by the users and the case-base constructed by the Intelligent Web-Agent system itself previously, so the users can access the essential web sites in short time. Intelligent Web-Agent system is compose of a interface-system and a learning system. According to the experiment, using the Intelligent Web-Agent System quicker than the case when not using the Intelligent Web-Agent System.

• Journal of the Korea Society of Computer and Information
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• v.5 no.2
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• pp.50-59
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• 2000

This study aims to design the structure of User Interface Agent System through finding out the user's intention on internet search engine. When building an agent system by using the intelligent method. we can Introduce the RBR system that is generally accepted as an efficient means of solving the problems in Interface Agent System. The RBR system turned out to be a success due to the modularity, uniformity naturalness and the like, but it also has various problems such as bottle-neck in knowledge acquisition, memory absence and limited application. To get rid of such problems, the CBR cycle was introduced in this study.

• Journal of the Korean Society for Library and Information Science
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• v.40 no.2
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• pp.103-123
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• 2006

Assuming that the knowledge creation and retrieval functions could be the most important factors for a successful knowledge management(KM) especially during the promotion stage of KM, this study suggests an e-learning application as one of best methods for producing knowledge and also the integrated knowledge repository model in which learning, knowledge. and information objects can be semantically associated through topic map-based knowledge map. The traditional KM system provides a simple directory-based knowledge map. which can not provide the semantic links between topics or objects. The proposed model can be utilized as a solution to solve the above-mentioned disadvantages of the traditional models. In order to collect the basic data for the proposed model, first, case studies utilizing interviews and surveys were conducted targeting at three Korean insurance companies' knowledge managers(or e-learning managers) and librarians. Second, the related studies and other topic map-based pilot systems were investigated.

• The Journal of the Korea Contents Association
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• v.20 no.5
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• pp.609-622
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• 2020

With the development of society, the scale of the statute is not only increasing, but also the content is getting complicated. The scale of safety standards existing in the law is also increasing and specialized, making it difficult to integrate and manage to minimize conflicts or overlaps among safety standards. For the integrated management of safety standards, a technology that searches for and extracts safety standards in laws and regulations must first be secured. In this study, considering the limitations of time and manpower, a tool for automatic detection of safety standards is developed based on several specific cases. The safety standards classified in the previous studies and the safety standards announced by the Ministry of Interior and Safety were analyzed, and also statute information which includes safety standards extracted by the National Disaster Management Institute in 2018 was collected. After the collected laws were refined and morphological analysis was performed, a safety standard thesaurus was constructed and indexed to develop a safety standard search tool. When automatic search tools are routinely applied to find safety standards in the future, it is expected that these tools will help to solve overlapping or conflicting problems of complex safety standards.

• Journal of the Korean association of regional geographers
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• v.5 no.1
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• pp.137-150
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• 1999

Five land use change detection methods were applied to 1994 and 1997 Landsat Thematic Mapper (TM) images of Pook-Gu, Taegu city to determine the land-cover changes between the two dates. The two images were coregistred to UTM coordinates. A post-classification comparison method was the most commonly used quantitative method of change detection. A pre-classification comparison method was more effective method to change detection of land cover than a post-classification comparison method. Two indices were used to assess the accuracies of the studied methods. A image differencing method was found to be most accurate for detecting change verse no change among five land use change detection methods. The difference image of band 2 was found to be most accurate. The overall accuracy and Kappa index agreement of the difference image of band 2 were 0.810 and 0.447.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.68-79
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• 2011

This paper focuses on the opinion information extraction and analysis system through Web mining that is based on statistics collected from Web contents. That is, users' opinion information which is scattered across several websites can be automatically analyzed and extracted. The system provides the opinion information search service that enables users to search for real-time positive and negative opinions and check their statistics. Also, users can do real-time search and monitoring about other opinion information by putting keywords in the system. Proposing technique proved that the actual performance is excellent by comparison experiment with other techniques. Performance evaluation of function extracting positive/negative opinion information, the performance evaluation applying dynamic window technique and tokenizer technique for multilingual information retrieval, and the performance evaluation of technique extracting exact multilingual phonetic translation are carried out. The experiment with typical movie review sentence and Wikipedia experiment data as object as that applying example is carried out and the result is analyzed.