• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.111-136
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• 2018

In this paper, we propose a methodology to extract answer information about queries from various types of unstructured documents collected from multi-sources existing on web in order to expand knowledge base. The proposed methodology is divided into the following steps. 1) Collect relevant documents from Wikipedia, Naver encyclopedia, and Naver news sources for "subject-predicate" separated queries and classify the proper documents. 2) Determine whether the sentence is suitable for extracting information and derive the confidence. 3) Based on the predicate feature, extract the information in the proper sentence and derive the overall confidence of the information extraction result. In order to evaluate the performance of the information extraction system, we selected 400 queries from the artificial intelligence speaker of SK-Telecom. Compared with the baseline model, it is confirmed that it shows higher performance index than the existing model. The contribution of this study is that we develop a sequence tagging model based on bi-directional LSTM-CRF using the predicate feature of the query, with this we developed a robust model that can maintain high recall performance even in various types of unstructured documents collected from multiple sources. The problem of information extraction for knowledge base extension should take into account heterogeneous characteristics of source-specific document types. The proposed methodology proved to extract information effectively from various types of unstructured documents compared to the baseline model. There is a limitation in previous research that the performance is poor when extracting information about the document type that is different from the training data. In addition, this study can prevent unnecessary information extraction attempts from the documents that do not include the answer information through the process for predicting the suitability of information extraction of documents and sentences before the information extraction step. It is meaningful that we provided a method that precision performance can be maintained even in actual web environment. The information extraction problem for the knowledge base expansion has the characteristic that it can not guarantee whether the document includes the correct answer because it is aimed at the unstructured document existing in the real web. When the question answering is performed on a real web, previous machine reading comprehension studies has a limitation that it shows a low level of precision because it frequently attempts to extract an answer even in a document in which there is no correct answer. The policy that predicts the suitability of document and sentence information extraction is meaningful in that it contributes to maintaining the performance of information extraction even in real web environment. The limitations of this study and future research directions are as follows. First, it is a problem related to data preprocessing. In this study, the unit of knowledge extraction is classified through the morphological analysis based on the open source Konlpy python package, and the information extraction result can be improperly performed because morphological analysis is not performed properly. To enhance the performance of information extraction results, it is necessary to develop an advanced morpheme analyzer. Second, it is a problem of entity ambiguity. The information extraction system of this study can not distinguish the same name that has different intention. If several people with the same name appear in the news, the system may not extract information about the intended query. In future research, it is necessary to take measures to identify the person with the same name. Third, it is a problem of evaluation query data. In this study, we selected 400 of user queries collected from SK Telecom 's interactive artificial intelligent speaker to evaluate the performance of the information extraction system. n this study, we developed evaluation data set using 800 documents (400 questions * 7 articles per question (1 Wikipedia, 3 Naver encyclopedia, 3 Naver news) by judging whether a correct answer is included or not. To ensure the external validity of the study, it is desirable to use more queries to determine the performance of the system. This is a costly activity that must be done manually. Future research needs to evaluate the system for more queries. It is also necessary to develop a Korean benchmark data set of information extraction system for queries from multi-source web documents to build an environment that can evaluate the results more objectively.

• Journal of Intelligence and Information Systems
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• v.19 no.3
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• pp.93-111
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• 2013

As Internet and information technology (IT) continues to develop and evolve, the issue of big data has emerged at the foreground of scholarly and industrial attention. Big data is generally defined as data that exceed the range that can be collected, stored, managed and analyzed by existing conventional information systems and it also refers to the new technologies designed to effectively extract values from such data. With the widespread dissemination of IT systems, continual efforts have been made in various fields of industry such as R&D, manufacturing, and finance to collect and analyze immense quantities of data in order to extract meaningful information and to use this information to solve various problems. Since IT has converged with various industries in many aspects, digital data are now being generated at a remarkably accelerating rate while developments in state-of-the-art technology have led to continual enhancements in system performance. The types of big data that are currently receiving the most attention include information available within companies, such as information on consumer characteristics, information on purchase records, logistics information and log information indicating the usage of products and services by consumers, as well as information accumulated outside companies, such as information on the web search traffic of online users, social network information, and patent information. Among these various types of big data, web searches performed by online users constitute one of the most effective and important sources of information for marketing purposes because consumers search for information on the internet in order to make efficient and rational choices. Recently, Google has provided public access to its information on the web search traffic of online users through a service named Google Trends. Research that uses this web search traffic information to analyze the information search behavior of online users is now receiving much attention in academia and in fields of industry. Studies using web search traffic information can be broadly classified into two fields. The first field consists of empirical demonstrations that show how web search information can be used to forecast social phenomena, the purchasing power of consumers, the outcomes of political elections, etc. The other field focuses on using web search traffic information to observe consumer behavior, identifying the attributes of a product that consumers regard as important or tracking changes on consumers' expectations, for example, but relatively less research has been completed in this field. In particular, to the extent of our knowledge, hardly any studies related to brands have yet attempted to use web search traffic information to analyze the factors that influence consumers' purchasing activities. This study aims to demonstrate that consumers' web search traffic information can be used to derive the relations among brands and the relations between an individual brand and product attributes. When consumers input their search words on the web, they may use a single keyword for the search, but they also often input multiple keywords to seek related information (this is referred to as simultaneous searching). A consumer performs a simultaneous search either to simultaneously compare two product brands to obtain information on their similarities and differences, or to acquire more in-depth information about a specific attribute in a specific brand. Web search traffic information shows that the quantity of simultaneous searches using certain keywords increases when the relation is closer in the consumer's mind and it will be possible to derive the relations between each of the keywords by collecting this relational data and subjecting it to network analysis. Accordingly, this study proposes a method of analyzing how brands are positioned by consumers and what relationships exist between product attributes and an individual brand, using simultaneous search traffic information. It also presents case studies demonstrating the actual application of this method, with a focus on tablets, belonging to innovative product groups.

• 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.

• Journal of agricultural medicine and community health
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• v.28 no.2
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• pp.107-122
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• 2003

Objectives: The objective of this study is to investigate the use rate and some aspect of complementary therapies used by patients with chronic illness(hypertension, diabetic mellitus and chronic arthritis). Methods: 600 patients visiting the health center for one month(Jan. 2001) were interviewed on their complementary therapies used by the subjects for the previous year. Results: About fourteen-eight percent of the respondents used therapies; 35% of patients with hypertension, 44.6% of patients with diabetic mellitus and 62.9% of patients with chronic arthritis, which shows the highest rate among patients with three chronic disease. The use rate of complementary therapies indicates few meaningful differences according to the general characteristics of the interviewees. Hypertension patients used herb medication(31.0%) acupuncture(29.6%) and most of all the other therapies. Diabetic patients used dietary therapy(57.5%) and herb medication(35.1%). Chronic arthritis patients used acupuncture(85%) and herb medication(34.7%). 36.8% of all the patients who used complementary therapies tried more than two therapies. 18.3% of hypertension patients, 24.1% of diabetic patients and 55.9% of chronic arthritis patients used more than two therapies. Acupuncture(47%) was used most frequently, followed by herb medications(26.3%), health assistance utensils(21.8%). oriental therapy(21.8%), physical therapy(9.5%), health assistance food(8.4%), herb(7.7%), Korea hand acupuncture(3.2%), abdomen respiration(1.1%), and pore therapy(0.7%) Oriental clinic was visited most frequently(42.8%), which was used to cure diseases(61.8%), and to relieve symptoms(26.0%). (p<0.001) The cost spent on complementary therapies last year was 90,000 won(40.3%) and there are some cases of more than 500,000 won(31.2%). Most of the patients(56.1%) were satisfied with the complementary therapies, with 6% of them having side effects. 74% of the patients used complementary therapies answered that they would continue them and 56.1% of them also answered that they would continue them and 56.1% of them also answered that they would advise other patients to do them. Advantages(compared with those of orthodox medical treatment) are psychological comfort(28.1%), body protection(26.0%), effectiveness(20.0%). 34% of the patients using complementary therapies wanted to have informational orientation on complementary therapies. These findings reveal that a considerable number of patients with chronic illness(47.5%) tried a variety of complementary therapies. Though 6% of the patients using therapies had side effects, most of the subjects seemed satisfied with them and they are supposed to continue them. Conclusions: In conclusion, health center personnels and medical doctors should pay more attention to the complementary therapies used by patients with chronic illness. They also have to try their best to advise more scientific and informative complementary programs with less side effects and more help to improve their conditions.

• Journal of Distribution Research
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• v.17 no.1
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• pp.37-63
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• 2012

In franchise business, exclusive sales territory (sometimes EST in table) protection is a very important issue from an economic, social and political point of view. It affects the growth and survival of both franchisor and franchisee and often raises issues of social and political conflicts. When franchisee is not familiar with related laws and regulations, franchisor has high chance to utilize it. Exclusive sales territory protection by the manufacturer and distributors (wholesalers or retailers) means sales area restriction by which only certain distributors have right to sell products or services. The distributor, who has been granted exclusive sales territories, can protect its own territory, whereas he may be prohibited from entering in other regions. Even though exclusive sales territory is a quite critical problem in franchise business, there is not much rigorous research about the reason, results, evaluation, and future direction based on empirical data. This paper tries to address this problem not only from logical and nomological validity, but from empirical validation. While we purse an empirical analysis, we take into account the difficulties of real data collection and statistical analysis techniques. We use a set of disclosure document data collected by Korea Fair Trade Commission, instead of conventional survey method which is usually criticized for its measurement error. Existing theories about exclusive sales territory can be summarized into two groups as shown in the table below. The first one is about the effectiveness of exclusive sales territory from both franchisor and franchisee point of view. In fact, output of exclusive sales territory can be positive for franchisors but negative for franchisees. Also, it can be positive in terms of sales but negative in terms of profit. Therefore, variables and viewpoints should be set properly. The other one is about the motive or reason why exclusive sales territory is protected. The reasons can be classified into four groups - industry characteristics, franchise systems characteristics, capability to maintain exclusive sales territory, and strategic decision. Within four groups of reasons, there are more specific variables and theories as below. Based on these theories, we develop nine hypotheses which are briefly shown in the last table below with the results. In order to validate the hypothesis, data is collected from government (FTC) homepage which is open source. The sample consists of 1,896 franchisors and it contains about three year operation data, from 2006 to 2008. Within the samples, 627 have exclusive sales territory protection policy and the one with exclusive sales territory policy is not evenly distributed over 19 representative industries. Additional data are also collected from another government agency homepage, like Statistics Korea. Also, we combine data from various secondary sources to create meaningful variables as shown in the table below. All variables are dichotomized by mean or median split if they are not inherently dichotomized by its definition, since each hypothesis is composed by multiple variables and there is no solid statistical technique to incorporate all these conditions to test the hypotheses. This paper uses a simple chi-square test because hypotheses and theories are built upon quite specific conditions such as industry type, economic condition, company history and various strategic purposes. It is almost impossible to find all those samples to satisfy them and it can't be manipulated in experimental settings. However, more advanced statistical techniques are very good on clean data without exogenous variables, but not good with real complex data. The chi-square test is applied in a way that samples are grouped into four with two criteria, whether they use exclusive sales territory protection or not, and whether they satisfy conditions of each hypothesis. So the proportion of sample franchisors which satisfy conditions and protect exclusive sales territory, does significantly exceed the proportion of samples that satisfy condition and do not protect. In fact, chi-square test is equivalent with the Poisson regression which allows more flexible application. As results, only three hypotheses are accepted. When attitude toward the risk is high so loyalty fee is determined according to sales performance, EST protection makes poor results as expected. And when franchisor protects EST in order to recruit franchisee easily, EST protection makes better results. Also, when EST protection is to improve the efficiency of franchise system as a whole, it shows better performances. High efficiency is achieved as EST prohibits the free riding of franchisee who exploits other's marketing efforts, and it encourages proper investments and distributes franchisee into multiple regions evenly. Other hypotheses are not supported in the results of significance testing. Exclusive sales territory should be protected from proper motives and administered for mutual benefits. Legal restrictions driven by the government agency like FTC could be misused and cause mis-understandings. So there need more careful monitoring on real practices and more rigorous studies by both academicians and practitioners.

• Journal of Global Scholars of Marketing Science
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• v.17 no.2
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• pp.111-131
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• 2007

As a brand becomes a core asset in creating a corporation's value, brand marketing has become one of core strategies that corporations pursue. Recently, for customer relationship management, possession and consumption of goods were centered on brand for the management. Thus, management related to this matter was developed. The main reason of the increased interest on the relationship between the brand and the consumer is due to acquisition of individual consumers and development of relationship with those consumers. Along with the development of relationship, a corporation is able to establish long-term relationships. This has become a competitive advantage for the corporation. All of these processes became the strategic assets of corporations. The importance and the increase of interest of a brand have also become a big issue academically. Brand equity, brand extension, brand identity, brand relationship, and brand community are the results derived from the interest of a brand. More specifically, in marketing, the study of brands has been led to the study of factors related to building of powerful brands and the process of building the brand. Recently, studies concentrated primarily on the consumer-brand relationship. The reason is that brand loyalty can not explain the dynamic quality aspects of loyalty, the consumer-brand relationship building process, and especially interactions between the brands and the consumers. In the studies of consumer-brand relationship, a brand is not just limited to possession or consumption objectives, but rather conceptualized as partners. Most of the studies from the past concentrated on the results of qualitative analysis of consumer-brand relationship to show the depth and width of the performance of consumer-brand relationship. Studies in Korea have been the same. Recently, studies of consumer-brand relationship started to concentrate on quantitative analysis rather than qualitative analysis or even go further with quantitative analysis to show effecting factors of consumer-brand relationship. Studies of new quantitative approaches show the possibilities of using the results as a new concept of viewing consumer-brand relationship and possibilities of applying these new concepts on marketing. Studies of consumer-brand relationship with quantitative approach already exist, but none of them include sub-dimensions of consumer-brand relationship, which presents theoretical proofs for measurement. In other words, most studies add up or average out the sub-dimensions of consumer-brand relationship. However, to do these kind of studies, precondition of sub-dimensions being in identical constructs is necessary. Therefore, most of the studies from the past do not meet conditions of sub-dimensions being as one dimension construct. From this, we question the validity of past studies and their limits. The main purpose of this paper is to overcome the limits shown from the past studies by practical use of previous studies on sub-dimensions in a one-dimensional construct (Naver & Slater, 1990; Cronin & Taylor, 1992; Chang & Chen, 1998). In this study, two arbitrary groups were classified to evaluate reliability of the measurements and reliability analyses were pursued on each group. For convergent validity, correlations, Cronbach's, one-factor solution exploratory analysis were used. For discriminant validity correlation of consumer-brand relationship was compared with that of an involvement, which is a similar concept with consumer-based relationship. It also indicated dependent correlations by Cohen and Cohen (1975, p.35) and results showed that it was different constructs from 6 sub-dimensions of consumer-brand relationship. Through the results of studies mentioned above, we were able to finalize that sub-dimensions of consumer-brand relationship can viewed from one-dimensional constructs. This means that the one-dimensional construct of consumer-brand relationship can be viewed with reliability and validity. The result of this research is theoretically meaningful in that it assumes consumer-brand relationship in a one-dimensional construct and provides the basis of methodologies which are previously preformed. It is thought that this research also provides the possibility of new research on consumer-brand relationship in that it gives root to the fact that it is possible to manipulate one-dimensional constructs consisting of consumer-brand relationship. In the case of previous research on consumer-brand relationship, consumer-brand relationship is classified into several types on the basis of components consisting of consumer-brand relationship and a number of studies have been performed with priority given to the types. However, as we can possibly manipulate a one-dimensional construct through this research, it is expected that various studies which make the level or strength of consumer-brand relationship practical application of construct will be performed, and not research focused on separate types of consumer-brand relationship. Additionally, we have the theoretical basis of probability in which to manipulate the consumer-brand relationship with one-dimensional constructs. It is anticipated that studies using this construct, which is consumer-brand relationship, practical use of dependent variables, parameters, mediators, and so on, will be performed.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.79-104
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• 2020

Recently, as deep learning has attracted attention, the use of deep learning is being considered as a method for solving problems in various fields. In particular, deep learning is known to have excellent performance when applied to applying unstructured data such as text, sound and images, and many studies have proven its effectiveness. Owing to the remarkable development of text and image deep learning technology, interests in image captioning technology and its application is rapidly increasing. Image captioning is a technique that automatically generates relevant captions for a given image by handling both image comprehension and text generation simultaneously. In spite of the high entry barrier of image captioning that analysts should be able to process both image and text data, image captioning has established itself as one of the key fields in the A.I. research owing to its various applicability. In addition, many researches have been conducted to improve the performance of image captioning in various aspects. Recent researches attempt to create advanced captions that can not only describe an image accurately, but also convey the information contained in the image more sophisticatedly. Despite many recent efforts to improve the performance of image captioning, it is difficult to find any researches to interpret images from the perspective of domain experts in each field not from the perspective of the general public. Even for the same image, the part of interests may differ according to the professional field of the person who has encountered the image. Moreover, the way of interpreting and expressing the image also differs according to the level of expertise. The public tends to recognize the image from a holistic and general perspective, that is, from the perspective of identifying the image's constituent objects and their relationships. On the contrary, the domain experts tend to recognize the image by focusing on some specific elements necessary to interpret the given image based on their expertise. It implies that meaningful parts of an image are mutually different depending on viewers' perspective even for the same image. So, image captioning needs to implement this phenomenon. Therefore, in this study, we propose a method to generate captions specialized in each domain for the image by utilizing the expertise of experts in the corresponding domain. Specifically, after performing pre-training on a large amount of general data, the expertise in the field is transplanted through transfer-learning with a small amount of expertise data. However, simple adaption of transfer learning using expertise data may invoke another type of problems. Simultaneous learning with captions of various characteristics may invoke so-called 'inter-observation interference' problem, which make it difficult to perform pure learning of each characteristic point of view. For learning with vast amount of data, most of this interference is self-purified and has little impact on learning results. On the contrary, in the case of fine-tuning where learning is performed on a small amount of data, the impact of such interference on learning can be relatively large. To solve this problem, therefore, we propose a novel 'Character-Independent Transfer-learning' that performs transfer learning independently for each character. In order to confirm the feasibility of the proposed methodology, we performed experiments utilizing the results of pre-training on MSCOCO dataset which is comprised of 120,000 images and about 600,000 general captions. Additionally, according to the advice of an art therapist, about 300 pairs of 'image / expertise captions' were created, and the data was used for the experiments of expertise transplantation. As a result of the experiment, it was confirmed that the caption generated according to the proposed methodology generates captions from the perspective of implanted expertise whereas the caption generated through learning on general data contains a number of contents irrelevant to expertise interpretation. In this paper, we propose a novel approach of specialized image interpretation. To achieve this goal, we present a method to use transfer learning and generate captions specialized in the specific domain. In the future, by applying the proposed methodology to expertise transplant in various fields, we expected that many researches will be actively conducted to solve the problem of lack of expertise data and to improve performance of image captioning.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.77-97
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• 2019

The large amount of data that emerges from the initial connection environment of the Fourth Industrial Revolution is a major factor that distinguishes the Fourth Industrial Revolution from the existing production environment. This environment has two-sided features that allow it to produce data while using it. And the data produced so produces another value. Due to the massive scale of data, future information systems need to process more data in terms of quantities than existing information systems. In addition, in terms of quality, only a large amount of data, Ability is required. In a small-scale information system, it is possible for a person to accurately understand the system and obtain the necessary information, but in a variety of complex systems where it is difficult to understand the system accurately, it becomes increasingly difficult to acquire the desired information. In other words, more accurate processing of large amounts of data has become a basic condition for future information systems. This problem related to the efficient performance of the information system can be solved by building a semantic web which enables various information processing by expressing the collected data as an ontology that can be understood by not only people but also computers. For example, as in most other organizations, IT has been introduced in the military, and most of the work has been done through information systems. Currently, most of the work is done through information systems. As existing systems contain increasingly large amounts of data, efforts are needed to make the system easier to use through its data utilization. An ontology-based system has a large data semantic network through connection with other systems, and has a wide range of databases that can be utilized, and has the advantage of searching more precisely and quickly through relationships between predefined concepts. In this paper, we propose a defense ontology as a method for effective data management and decision support. In order to judge the applicability and effectiveness of the actual system, we reconstructed the existing air force munitions situation management system as an ontology based system. It is a system constructed to strengthen management and control of logistics situation of commanders and practitioners by providing real - time information on maintenance and distribution situation as it becomes difficult to use complicated logistics information system with large amount of data. Although it is a method to take pre-specified necessary information from the existing logistics system and display it as a web page, it is also difficult to confirm this system except for a few specified items in advance, and it is also time-consuming to extend the additional function if necessary And it is a system composed of category type without search function. Therefore, it has a disadvantage that it can be easily utilized only when the system is well known as in the existing system. The ontology-based logistics situation management system is designed to provide the intuitive visualization of the complex information of the existing logistics information system through the ontology. In order to construct the logistics situation management system through the ontology, And the useful functions such as performance - based logistics support contract management and component dictionary are further identified and included in the ontology. In order to confirm whether the constructed ontology can be used for decision support, it is necessary to implement a meaningful analysis function such as calculation of the utilization rate of the aircraft, inquiry about performance-based military contract. Especially, in contrast to building ontology database in ontology study in the past, in this study, time series data which change value according to time such as the state of aircraft by date are constructed by ontology, and through the constructed ontology, It is confirmed that it is possible to calculate the utilization rate based on various criteria as well as the computable utilization rate. In addition, the data related to performance-based logistics contracts introduced as a new maintenance method of aircraft and other munitions can be inquired into various contents, and it is easy to calculate performance indexes used in performance-based logistics contract through reasoning and functions. Of course, we propose a new performance index that complements the limitations of the currently applied performance indicators, and calculate it through the ontology, confirming the possibility of using the constructed ontology. Finally, it is possible to calculate the failure rate or reliability of each component, including MTBF data of the selected fault-tolerant item based on the actual part consumption performance. The reliability of the mission and the reliability of the system are calculated. In order to confirm the usability of the constructed ontology-based logistics situation management system, the proposed system through the Technology Acceptance Model (TAM), which is a representative model for measuring the acceptability of the technology, is more useful and convenient than the existing system.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.37 no.2
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• pp.74-88
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• 2019

This study developed the Assessment Indicator evaluating eco-cultural value of temple forest in Korea and applied the developed Assessment Indicator to Songgwang-sa(also known as Seungbo-sachal), one of the Three Jewels Temple. Literature reviews and the draft of Assessment Indicator were drawn from brainstorming(including 2 forest therapy experts, 1 Buddhist monk expert, 1 landscape architect, 1 forest expert, and 6 researchers). After that, the Assessment Indicator drawn from the group of experts(the 1st in-depth interview: 32 people, the 2nd in-depth interview: 30 people) was verified and revised. The final Assessment Indicator, which was composed of 4 parts and 20 items, was developed. The results are as follows. The eco-cultural Assessment Indicator of temple forest was composed of 4 parts, which were Historical Cultural value, Ecological value, Recreatory Visitational value, and Educational Useful value, and 20 items and each item had 5 points. Historical Cultural value had 5 items and its total points were 25. Ecological value had 5 items and had total 25 points. Recreatory Visitational value had 6 items, 30 total points. Educational Useful value had 4 items, 20 total points. The total points of the eco-cultural Assessment Indicator were 100 points. As a result of applying the developed Assessment Indicator to the target place, Songgwang-sa in Mt. Jogye, Historical Cultural value of temple forest was calculated as 23 points(out of 25). Ecological value was 21 point(out of 25), Recreatory Visitational value, 22 points(out of 30), and Educational Useful value, 16 points(out of 20). The total points were 82(out of 100). Consequently, this study is meaningful based on the following 5 aspects. Firstly, this study challenged the development of the eco-cultural Assessment Indicator of temple forest for the first time. It is significant because the developed Assessment Indicator can be a useful resource for the eco-cultural value of temple forest. Secondly, the result showed that Educational Useful value and Recreatory Visitational value of forest temple were very low. Therefore, the supports for leisure, tour, education, and use of temple forest are needed from Korea Forest Service, Ministry of Environment, Cultural Heritage Administration and other government agencies since they acknowledge the temple forest as the best customers in Korea. Thirdly, the excellence or for eco-cultural value of temple forest needs to be extended in a national level. It is possible to make a Korean National Bran(e.g., the Therapy at the Temple) by blending temple stay, which is only in temples, and therapy, and is also possible to be a global tour industry. Fourthly, this study suggested legal definition about the necessary of legal definition for temple forest because there is no legal definition on temple forest in the current situation. When the definition of temple forest is legally arranaged, it would be a foundation for conserving eco-cultural value of temple forest, for organizing exclusively responsible departments in governmental institutions, and further for registering temple forest as World Natural Heritage. Lastly, the developed eco-cultural Assessment Indicators of temple forest from this study would be applied to "the 7 Sansa, Buddhist Mountain Monasteries in Korea(Sansa)" and the characteristics of each 7 temple are drawn. This study would be a basic data for temples' management and use with the eco-cultural Assessment Indicator of temple forest.