• The Journal of the Korea Contents Association
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• v.21 no.4
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• pp.77-87
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• 2021

Smart Factory refers to a factory that can be controlled by itself with an intelligent factory that improves productivity, quality and customer satisfaction by combining the entire process of manufacturing and production with digital automation solutions. The manufacturing industry around the world is rapidly changing, with Germany, Europe, and the United States at the center. In order to cope with such changes, the Korean government is also implementing a policy to spread the supply of smart factories for small and medium-sized companies, and related ministries and agencies such as the Ministry of Commerce, Industry and Energy, the Ministry of SMEs and Venture Business, the Korea Institute of Technology and Information Promotion, and local technoparks, as well as large companies such as Samsung, SK and LG are actively investing in smart manufacturing projects to support smart factories[1]. Factory Automation (FA) construction has many issues regarding the connection of heterogeneous equipment. The most difficult aspect of configuring various communications from various equipment is the reason. Although it may not be known if there are standards or products made up of the same company, it is not easy to build equipment that is old, up-to-date, and different use environments through a series of communications. To solve this problem, we would like to propose a method of communication using Modbus, one of FieldBus, which is one of the many industrial devices of PLC, a representative facility control system, and is used as a communication standard.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.189-196
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• 2023

As the manufacturing paradigm shifts, various collaborative robots are creating new markets. Demand for collaborative robots is increasing in all industries for the purpose of easy operation, productivity improvement, and replacement of manpower who do simple tasks compared to existing industrial robots. However, accidents frequently occur during work caused by collaborative robots in industrial sites, threatening the safety of workers. In order to construct an industrial site through robots in a human-centered environment, the safety of workers must be guaranteed, and there is a need to develop a collaborative robot guard system that provides reliable communication without the possibility of dispatch. It is necessary to double prevent accidents that occur within the working radius of cobots and reduce the risk of safety accidents through sensors and computer vision. We build a system based on OPC UA, an international protocol for communication with various industrial equipment, and propose a collaborative robot guard system through image analysis using ultrasonic sensors and CNN (Convolution Neural Network). The proposed system evaluates the possibility of robot control in an unsafe situation for a worker.

• The Journal of the Korea Contents Association
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• v.21 no.6
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• pp.701-715
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• 2021

The drastic advance of recent deep learning technologies is heavily dependent on training datasets which are essential to train models by themselves with less human efforts. In comparison with the work to design deep learning models, preparing datasets is a long haul; at the moment, in the domain of vision intelligent, datasets are still being made by handwork requiring a lot of time and efforts, where workers need to directly make labels on each image usually with GUI-based labeling tools. In this paper, we overview the current status of vision datasets focusing on what datasets are being shared and how they are prepared with various labeling tools. Particularly, in order to relieve the repetitive and tiring labeling work, we present an interactive smart image annotating system with which the annotation work can be transformed from the direct human-only manual labeling to a correction-after-checking by means of a support of automatic labeling. In an experiment, we show that automatic labeling can greatly improve the productivity of datasets especially reducing time and efforts to specify regions of objects found in images. Finally, we discuss critical issues that we faced in the experiment to our annotation system and describe future work to raise the productivity of image datasets creation for accelerating AI technology.

• 선박안전기술공단연구보고서
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• s.4
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• pp.1-108
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• 2007

2007. 7. 23 IMO의 NAV(항해안전전문위원회)53차 회의에서는 e-Navigation을 해상에서의 안전, 보안, 해양환경보호를 목적으로 전자적인 수단에 의해 선박과 육상에서 해양정보를 수집, 교환, 표시함으로써 항구와 항구간의 항해 및 관련된 서비스를 향상시키는 것으로 정의하고 있다.2005년 11월 영국의 교통부 장관 Stephen 박사는 Royal Institute ofNavigation에서의 연설에서 해상안전과 환경보호를 위하여 선박의 항해를 감시하는 관제소 및 항행하는 선박에 유용하고 정확한 정보가 더 많이 필요함을 역설하였다. 그리고 첨단 기술에 의해 자동화된 항공 항법분야를 예로들면서, 선박의 항법 분야도 항해와 관련된 모든 시설 및 작업을 전자적 수단으로 대체하는 개념인 e-Navigation으로 전환되어야 하며 영국은 이에 필요한 작업을 주도하겠다는 의견을 피력하였다. Stephen은 e-Navigation 도입으로 얻을 수 있는 이익으로 첫째, 항해 실수로 인한 사고 확률저감, 둘째,사고 발생 시 인명 구조 및 피해 확산을 위한 효율적 대응, 셋째, 전통적인항해시설 설치 불필요로 인한 비용 저감, 넷째 선박입출항 수속의 간편화 및항로의 효율적 운용으로 인한 상업적 이익 등을 들었다. 반면에e-Navigation 체계로 전환 시 예상되는 장애로는 첫째, 체계 구축을 위한 비용(특히 개발도상국가들의 경우 어려움 예상), 둘째, e-Navigation의 성과 달성을 위하여 세계 전 해역의 모든 선박이 e-Navigation 체계에 동참하도록유도하는 문제, 셋째, 전자해도 표시 및 선교 장비들에 대한 표준화 문제, 넷째, 육상에 설치할 e-Navigation 센터의 설계 및 구축 등을 꼽았다.IMO는 2005년 81차 MSC(해사안전위원회) 회의에서 영국이 일본, 마샬아일랜드, 네덜란드, 노르웨이, 싱가포르, 미국과 공동으로 제안한 ‘e-Navigation전략 개발’ 의제를 2006년 82차 MSC 회의에서 채택하고, NAV(항해 전문위원회)를 통하여 2008년까지 e-Navigation의 구체적 개념을 정립하고 향후 개발하여야 할 전략적 비전과 정책을 수립하기로 하였다. 이어서 영국을 의장으로 e-Navigation 전략개발 통신작업반이 구성되었는데, 지난 년간 19개국, 16개 전문기관이 참여하여 아래의 작업이 수행되었다. ○ e-Navigation 개념의 정의와 목적 ○ e-Navigation에 대한 핵심 이슈 및 우선 순위 식별 ○ e-Navigation 개발에 따른 이점과 단점의 식별 ○ IMO 및 회원국 등의 역할 식별 ○ 이행계획을 포함한 추가 개발을 위한 작업계획의 작성 IMO에서 수행되고 있는 e-Navigation 전략 개발 의제 일정은 2008년까지이다. 이 전략 개발에 있어서 중요한 요소는 e-Navigation이 포함할 서비스범위, 포함하는 서비스 제공에 필요한 인프라 및 장비의 식별, 인프라 구축및 운용비용을 부담할 주체에 대한 논의, e-Navigation으로 인한 이익과 투자비용에 대한 비교 분석 등이다. 이 과정에서 정부, 선주, 항만운영자, 선원등의 입장 차이와 선진국과 개발도상국 간의 경제 수준 차이는 전략 개발에있어 큰 어려움을 줄 것이므로, 이들이 합의된 전략을 만들기 위해서는 예정된 기간보다 다소 늦어질 가능성도 있다.e-Navigation 전략 개발이 완료되면 1단계로는 해상교통 관제시스템, 선박선교 장비, 무선 통신장비 등에 대한 표준화 작업이 이루어질 것이다. 이 과정에서 각국 간에 자국 보유 기술을 표준화시키기 위한 경쟁이 치열할 것으로 예상된다. 2단계에서는 e-Navigation 체계 하에서의 다양하고 풍부한 서비스 제공을 위한 관련 소프트웨어 및 하드웨어의 개발이 이루어질 것으로전망되는데, 이는 지난 10년간 육상에서 인터넷망 설치 후 이루어진 관련 서비스 산업의 발전을 돌아보면 쉽게 짐작할 수 있을 것이다.e-Navigation 체계 하에서 선박의 항해는 현재와는 전혀 다른 패러다임으로 바뀔 것이다. 예를 들어 현재 입출항 시 요구되던 복잡한 절차는one-stop 쇼핑 형태로 단순화되고, 현재 선박 중심의 항해에서 육상e-Navigation 센터가 적극적으로 관여하는 항해 체계로 바뀔 것이며, 해상정보의 공유와 활용이 무선 인터넷을 통해 보다 광범위하게 이루어질 것이 다.e-Navigation의 잠재적 시장 규모는 선박에 새로이 탑재될 지능형 통합 항법시스템 구축과 육상 모니터링 및 지원 시스템 등 직접 시장이 약 50조원,전자해도, 통신장비, 관련 서비스 컨텐츠 등 간접 시장의 규모가 150조원으로 총 200조원으로 대략 추산하고 있다. 향후 이 거대한 시장을 차지하기 위한 전략 수립이 필요한 시점이다. 지금까지 항해 장비 관련 산업은 선진국의일부 업체들에 의해 독점되어 왔다. 우리나라는 조선과 해운에서 모두 선진국임에도 불구하고 이 분야에서는 대부분 수입에 의존해 왔다. e-Navigation체계 하에서는 전체 시장이 커지고 장비의 사양이 표준화됨에 따라 어느 소수 업체가 현재처럼 독점하기는 더 이상 어려울 것으로 예상된다. 따라서e-Navigation은 우리나라도 항해 장비 분야 시장을 차지할 수 있는 좋은 기회라고 할 수 있다. 특히 조선 1위의 장점을 적극 활용한다면 다른 나라보다우위의 경쟁력을 확보할 수도 있다. 또한, 서비스 분야의 시장은 IT 기술과밀접한 관계가 있으므로 IT 강국인 우리나라가 충분한 경쟁력을 갖고 있다고 할 수 있다.그러나, EU를 비롯한 선진국에서는 이미 e-Navigation 에 대비한 연구를10여년 전부터 수행해 왔다. 앞에서 언급한 EU의 MarNIS 사업은 현재 거의마무리 단계로 당장 실용화 할 수 있는 수준에 있는 것으로 보인다. 늦었지만 우리도 이를 따라잡기 위한 연구를 서둘러야 할 것이다. 국내에서도e-Navigation의 중요성을 깊이 인식하고, 2006년에는 관련 산학연 전문가들로 작업반을 구성하여 워크숍 등을 개최한 바 있다. 또한 해양수산부에서도e-Navigation 핵심기술 개발을 위한 연구사업을 기획 추진하고 있다.그러나 현재 항해통신장비들의 기술기준은 ITU의 전파규칙(RR)과 IMO결의 및 SOLAS 협약을 따르고 있는데 이들 규약이나 결의에 대한 국제적인 추이와 비교할 때 국내의 기술은 표준화되지 못한 부분이 많은 실정이다.본 연구에서는 e-Navigation sytem중 표준화가 필요한 요소와 전자해도,AIS 등 e-Navigation(통합전자항법시스템)관련 국내산업현황 실태조사를 통해 국내 e-Navigation기술개발 동향에 대해 조사하고자 한다.

• Journal of Intelligence and Information Systems
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• v.18 no.4
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• pp.129-143
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• 2012

Recently, the rapid progress of a number of standardized web technologies and the proliferation of web users in the world bring an explosive increase of producing and consuming information documents on the web. In addition, most companies have produced, shared, and managed a huge number of information documents that are needed to perform their businesses. They also have discretionally raked, stored and managed a number of web documents published on the web for their business. Along with this increase of information documents that should be managed in the companies, the need of a solution to locate information documents more accurately among a huge number of information sources have increased. In order to satisfy the need of accurate search, the market size of search engine solution market is becoming increasingly expended. The most important functionality among much functionality provided by search engine is to locate accurate information documents from a huge information sources. The major metric to evaluate the accuracy of search engine is relevance that consists of two measures, precision and recall. Precision is thought of as a measure of exactness, that is, what percentage of information considered as true answer are actually such, whereas recall is a measure of completeness, that is, what percentage of true answer are retrieved as such. These two measures can be used differently according to the applied domain. If we need to exhaustively search information such as patent documents and research papers, it is better to increase the recall. On the other hand, when the amount of information is small scale, it is better to increase precision. Most of existing web search engines typically uses a keyword search method that returns web documents including keywords which correspond to search words entered by a user. This method has a virtue of locating all web documents quickly, even though many search words are inputted. However, this method has a fundamental imitation of not considering search intention of a user, thereby retrieving irrelevant results as well as relevant ones. Thus, it takes additional time and effort to set relevant ones out from all results returned by a search engine. That is, keyword search method can increase recall, while it is difficult to locate web documents which a user actually want to find because it does not provide a means of understanding the intention of a user and reflecting it to a progress of searching information. Thus, this research suggests a new method of combining ontology-based search solution with core search functionalities provided by existing search engine solutions. The method enables a search engine to provide optimal search results by inferenceing the search intention of a user. To that end, we build an ontology which contains concepts and relationships among them in a specific domain. The ontology is used to inference synonyms of a set of search keywords inputted by a user, thereby making the search intention of the user reflected into the progress of searching information more actively compared to existing search engines. Based on the proposed method we implement a prototype search system and test the system in the patent domain where we experiment on searching relevant documents associated with a patent. The experiment shows that our system increases the both recall and precision in accuracy and augments the search productivity by using improved user interface that enables a user to interact with our search system effectively. In the future research, we will study a means of validating the better performance of our prototype system by comparing other search engine solution and will extend the applied domain into other domains for searching information such as portal.

• Journal of Intelligence and Information Systems
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• v.17 no.4
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• pp.175-191
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• 2011

With the MICE(Meeting, Incentive travel, Convention, Exhibition) industry coming into the spotlight, there has been a growing interest in the domestic exhibition industry. Accordingly, in Korea, various studies of the industry are being conducted to enhance exhibition performance as in the United States or Europe. Some studies are focusing particularly on analyzing visiting patterns of exhibition visitors using intelligent information technology in consideration of the variations in effects of watching exhibitions according to the exhibitory environment or technique, thereby understanding visitors and, furthermore, drawing the correlations between exhibiting businesses and improving exhibition performance. However, previous studies related to booth recommendation systems only discussed the accuracy of recommendation in the aspect of a system rather than determining changes in visitors' behavior or perception by recommendation. A booth recommendation system enables visitors to visit unplanned exhibition booths by recommending visitors suitable ones based on information about visitors' visits. Meanwhile, some visitors may be satisfied with their unplanned visits, while others may consider the recommending process to be cumbersome or obstructive to their free observation. In the latter case, the exhibition is likely to produce worse results compared to when visitors are allowed to freely observe the exhibition. Thus, in order to apply a booth recommendation system to exhibition halls, the factors affecting the performance of the system should be generally examined, and the effects of the system on visitors' unplanned visiting behavior should be carefully studied. As such, this study aims to determine the factors that affect the performance of a booth recommendation system by reviewing theories and literature and to examine the effects of visitors' perceived performance of the system on their satisfaction of unplanned behavior and intention to reuse the system. Toward this end, the unplanned behavior theory was adopted as the theoretical framework. Unplanned behavior can be defined as "behavior that is done by consumers without any prearranged plan". Thus far, consumers' unplanned behavior has been studied in various fields. The field of marketing, in particular, has focused on unplanned purchasing among various types of unplanned behavior, which has been often confused with impulsive purchasing. Nevertheless, the two are different from each other; while impulsive purchasing means strong, continuous urges to purchase things, unplanned purchasing is behavior with purchasing decisions that are made inside a store, not before going into one. In other words, all impulsive purchases are unplanned, but not all unplanned purchases are impulsive. Then why do consumers engage in unplanned behavior? Regarding this question, many scholars have made many suggestions, but there has been a consensus that it is because consumers have enough flexibility to change their plans in the middle instead of developing plans thoroughly. In other words, if unplanned behavior costs much, it will be difficult for consumers to change their prearranged plans. In the case of the exhibition hall examined in this study, visitors learn the programs of the hall and plan which booth to visit in advance. This is because it is practically impossible for visitors to visit all of the various booths that an exhibition operates due to their limited time. Therefore, if the booth recommendation system proposed in this study recommends visitors booths that they may like, they can change their plans and visit the recommended booths. Such visiting behavior can be regarded similarly to consumers' visit to a store or tourists' unplanned behavior in a tourist spot and can be understand in the same context as the recent increase in tourism consumers' unplanned behavior influenced by information devices. Thus, the following research model was established. This research model uses visitors' perceived performance of a booth recommendation system as the parameter, and the factors affecting the performance include trust in the system, exhibition visitors' knowledge levels, expected personalization of the system, and the system's threat to freedom. In addition, the causal relation between visitors' satisfaction of their perceived performance of the system and unplanned behavior and their intention to reuse the system was determined. While doing so, trust in the booth recommendation system consisted of 2nd order factors such as competence, benevolence, and integrity, while the other factors consisted of 1st order factors. In order to verify this model, a booth recommendation system was developed to be tested in 2011 DMC Culture Open, and 101 visitors were empirically studied and analyzed. The results are as follows. First, visitors' trust was the most important factor in the booth recommendation system, and the visitors who used the system perceived its performance as a success based on their trust. Second, visitors' knowledge levels also had significant effects on the performance of the system, which indicates that the performance of a recommendation system requires an advance understanding. In other words, visitors with higher levels of understanding of the exhibition hall learned better the usefulness of the booth recommendation system. Third, expected personalization did not have significant effects, which is a different result from previous studies' results. This is presumably because the booth recommendation system used in this study did not provide enough personalized services. Fourth, the recommendation information provided by the booth recommendation system was not considered to threaten or restrict one's freedom, which means it is valuable in terms of usefulness. Lastly, high performance of the booth recommendation system led to visitors' high satisfaction levels of unplanned behavior and intention to reuse the system. To sum up, in order to analyze the effects of a booth recommendation system on visitors' unplanned visits to a booth, empirical data were examined based on the unplanned behavior theory and, accordingly, useful suggestions for the establishment and design of future booth recommendation systems were made. In the future, further examination should be conducted through elaborate survey questions and survey objects.

• Journal of Intelligence and Information Systems
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• v.18 no.1
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• pp.59-69
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• 2012

As exhibition industry, which is a part of 17 new growth engines of the government, is related to other industries such as tourism, transportation and financial industries. So it has a significant ripple effect on other industries. Exhibition is a knowledge-intensive, eco-friendly and high value-added Industry. Over 13,000 exhibitions are held every year around the world which contributes to getting foreign currency. Exhibition industry is closely related with culture and tourism and could be utilized as local and national development strategies and improve national brand image as well. Many countries try various efforts to invigorate exhibition industry by arranging related laws and support system. In Korea, more than 200 exhibitions are being held every year, but only 2~3 exhibitions are hosted with over 400 exhibitors and except these exhibitions most exhibitions have few foreign exhibitors. The main reason of weakness of domestic trade show is that there are no agencies managing exhibitionrelated statistics and there is no specific and reliable evaluation. This might cause impossibility of providing buyer or seller with reliable data, poor growth of exhibitions in terms of quality and thus service quality of trade shows cannot be improved. Hosting a lot of visitors (Public/Buyer/Exhibitor) is very crucial to the development of domestic exhibition industry. In order to attract many visitors, service quality of exhibition and visitor's satisfaction should be enhanced. For this purpose, a variety of real-time customized services through digital media and the services for creating new customers and retaining existing customers should be provided. In addition, by providing visitors with personalized information services they could manage their time and space efficiently avoiding the complexity of exhibition space. Exhibition industry can have competitiveness and industrial foundation through building up exhibition-related statistics, creating new information and enhancing research ability. Therefore, this paper deals with customized service with visitor's smart-phone at the exhibition space and designing mobile framework which enables exhibition devices to interact with other devices. Mobile server framework is composed of three different systems; multi-server interaction, server, client, display device. By making knowledge pool of exhibition environment, the accumulated data for each visitor can be provided as personalized service. In addition, based on the reaction of visitors each of all information is utilized as customized information and so the cyclic chain structure is designed. Multiple interaction server is designed to have functions of event handling, interaction process between exhibition device and visitor's smart-phone and data management. Client is an application processed by visitor's smart-phone and could be driven on a variety of platforms. Client functions as interface representing customized service for individual visitors and event input and output for simultaneous participation. Exhibition device consists of display system to show visitors contents and information, interaction input-output system to receive event from visitors and input toward action and finally the control system to connect above two systems. The proposed mobile framework in this paper provides individual visitors with customized and active services using their information profile and advanced Knowledge. In addition, user participation service is suggested as well by using interaction connection system between server, client, and exhibition devices. Suggested mobile framework is a technology which could be applied to culture industry such as performance, show and exhibition. Thus, this builds up the foundation to improve visitor's participation in exhibition and bring about development of exhibition industry by raising visitor's interest.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.149-161
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• 2014

The export of domestic public services to overseas markets contains many potential obstacles, stemming from different export procedures, the target services, and socio-economic environments. In order to alleviate these problems, the business incubation platform as an open business ecosystem can be a powerful instrument to support the decisions taken by participants and stakeholders. In this paper, we propose an ontology model and its implementation processes for the business incubation platform with an open and pervasive architecture to support public service exports. For the conceptual model of platform ontology, export case studies are used for requirements analysis. The conceptual model shows the basic structure, with vocabulary and its meaning, the relationship between ontologies, and key attributes. For the implementation and test of the ontology model, the logical structure is edited using Prot$\acute{e}$g$\acute{e}$ editor. The core engine of the business incubation platform is the simulator module, where the various contexts of export businesses should be captured, defined, and shared with other modules through ontologies. It is well-known that an ontology, with which concepts and their relationships are represented using a shared vocabulary, is an efficient and effective tool for organizing meta-information to develop structural frameworks in a particular domain. The proposed model consists of five ontologies derived from a requirements survey of major stakeholders and their operational scenarios: service, requirements, environment, enterprise, and county. The service ontology contains several components that can find and categorize public services through a case analysis of the public service export. Key attributes of the service ontology are composed of categories including objective, requirements, activity, and service. The objective category, which has sub-attributes including operational body (organization) and user, acts as a reference to search and classify public services. The requirements category relates to the functional needs at a particular phase of system (service) design or operation. Sub-attributes of requirements are user, application, platform, architecture, and social overhead. The activity category represents business processes during the operation and maintenance phase. The activity category also has sub-attributes including facility, software, and project unit. The service category, with sub-attributes such as target, time, and place, acts as a reference to sort and classify the public services. The requirements ontology is derived from the basic and common components of public services and target countries. The key attributes of the requirements ontology are business, technology, and constraints. Business requirements represent the needs of processes and activities for public service export; technology represents the technological requirements for the operation of public services; and constraints represent the business law, regulations, or cultural characteristics of the target country. The environment ontology is derived from case studies of target countries for public service operation. Key attributes of the environment ontology are user, requirements, and activity. A user includes stakeholders in public services, from citizens to operators and managers; the requirements attribute represents the managerial and physical needs during operation; the activity attribute represents business processes in detail. The enterprise ontology is introduced from a previous study, and its attributes are activity, organization, strategy, marketing, and time. The country ontology is derived from the demographic and geopolitical analysis of the target country, and its key attributes are economy, social infrastructure, law, regulation, customs, population, location, and development strategies. The priority list for target services for a certain country and/or the priority list for target countries for a certain public services are generated by a matching algorithm. These lists are used as input seeds to simulate the consortium partners, and government's policies and programs. In the simulation, the environmental differences between Korea and the target country can be customized through a gap analysis and work-flow optimization process. When the process gap between Korea and the target country is too large for a single corporation to cover, a consortium is considered an alternative choice, and various alternatives are derived from the capability index of enterprises. For financial packages, a mix of various foreign aid funds can be simulated during this stage. It is expected that the proposed ontology model and the business incubation platform can be used by various participants in the public service export market. It could be especially beneficial to small and medium businesses that have relatively fewer resources and experience with public service export. We also expect that the open and pervasive service architecture in a digital business ecosystem will help stakeholders find new opportunities through information sharing and collaboration on business processes.

• Journal of Intelligence and Information Systems
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• v.21 no.2
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• pp.49-67
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• 2015

Recently, emerging the notion of big data and social media has led us to enter data's big bang. Social networking services are widely used by people around the world, and they have become a part of major communication tools for all ages. Over the last decade, as online social networking sites become increasingly popular, companies tend to focus on advanced social media analysis for their marketing strategies. In addition to social media analysis, companies are mainly concerned about propagating of negative opinions on social networking sites such as Facebook and Twitter, as well as e-commerce sites. The effect of online word of mouth (WOM) such as product rating, product review, and product recommendations is very influential, and negative opinions have significant impact on product sales. This trend has increased researchers' attention to a natural language processing, such as a sentiment analysis. A sentiment analysis, also refers to as an opinion mining, is a process of identifying the polarity of subjective information and has been applied to various research and practical fields. However, there are obstacles lies when Korean language (Hangul) is used in a natural language processing because it is an agglutinative language with rich morphology pose problems. Therefore, there is a lack of Korean natural language processing resources such as a sentiment lexicon, and this has resulted in significant limitations for researchers and practitioners who are considering sentiment analysis. Our study builds a Korean sentiment lexicon with collective intelligence, and provides API (Application Programming Interface) service to open and share a sentiment lexicon data with the public (www.openhangul.com). For the pre-processing, we have created a Korean lexicon database with over 517,178 words and classified them into sentiment and non-sentiment words. In order to classify them, we first identified stop words which often quite likely to play a negative role in sentiment analysis and excluded them from our sentiment scoring. In general, sentiment words are nouns, adjectives, verbs, adverbs as they have sentimental expressions such as positive, neutral, and negative. On the other hands, non-sentiment words are interjection, determiner, numeral, postposition, etc. as they generally have no sentimental expressions. To build a reliable sentiment lexicon, we have adopted a concept of collective intelligence as a model for crowdsourcing. In addition, a concept of folksonomy has been implemented in the process of taxonomy to help collective intelligence. In order to make up for an inherent weakness of folksonomy, we have adopted a majority rule by building a voting system. Participants, as voters were offered three voting options to choose from positivity, negativity, and neutrality, and the voting have been conducted on one of the largest social networking sites for college students in Korea. More than 35,000 votes have been made by college students in Korea, and we keep this voting system open by maintaining the project as a perpetual study. Besides, any change in the sentiment score of words can be an important observation because it enables us to keep track of temporal changes in Korean language as a natural language. Lastly, our study offers a RESTful, JSON based API service through a web platform to make easier support for users such as researchers, companies, and developers. Finally, our study makes important contributions to both research and practice. In terms of research, our Korean sentiment lexicon plays an important role as a resource for Korean natural language processing. In terms of practice, practitioners such as managers and marketers can implement sentiment analysis effectively by using Korean sentiment lexicon we built. Moreover, our study sheds new light on the value of folksonomy by combining collective intelligence, and we also expect to give a new direction and a new start to the development of Korean natural language processing.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.95-112
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• 2021

In university education, the choice of major class plays an important role in students' careers. However, in line with the changes in the industry, the fields of major subjects by department are diversifying and increasing in number in university education. As a result, students have difficulty to choose and take classes according to their career paths. In general, students choose classes based on experiences such as choices of peers or advice from seniors. This has the advantage of being able to take into account the general situation, but it does not reflect individual tendencies and considerations of existing courses, and has a problem that leads to information inequality that is shared only among specific students. In addition, as non-face-to-face classes have recently been conducted and exchanges between students have decreased, even experience-based decisions have not been made as well. Therefore, this study proposes a recommendation system model that can recommend college major classes suitable for individual characteristics based on data rather than experience. The recommendation system recommends information and content (music, movies, books, images, etc.) that a specific user may be interested in. It is already widely used in services where it is important to consider individual tendencies such as YouTube and Facebook, and you can experience it familiarly in providing personalized services in content services such as over-the-top media services (OTT). Classes are also a kind of content consumption in terms of selecting classes suitable for individuals from a set content list. However, unlike other content consumption, it is characterized by a large influence of selection results. For example, in the case of music and movies, it is usually consumed once and the time required to consume content is short. Therefore, the importance of each item is relatively low, and there is no deep concern in selecting. Major classes usually have a long consumption time because they have to be taken for one semester, and each item has a high importance and requires greater caution in choice because it affects many things such as career and graduation requirements depending on the composition of the selected classes. Depending on the unique characteristics of these major classes, the recommendation system in the education field supports decision-making that reflects individual characteristics that are meaningful and cannot be reflected in experience-based decision-making, even though it has a relatively small number of item ranges. This study aims to realize personalized education and enhance students' educational satisfaction by presenting a recommendation model for university major class. In the model study, class history data of undergraduate students at University from 2015 to 2017 were used, and students and their major names were used as metadata. The class history data is implicit feedback data that only indicates whether content is consumed, not reflecting preferences for classes. Therefore, when we derive embedding vectors that characterize students and classes, their expressive power is low. With these issues in mind, this study proposes a Net-NeuMF model that generates vectors of students, classes through network analysis and utilizes them as input values of the model. The model was based on the structure of NeuMF using one-hot vectors, a representative model using data with implicit feedback. The input vectors of the model are generated to represent the characteristic of students and classes through network analysis. To generate a vector representing a student, each student is set to a node and the edge is designed to connect with a weight if the two students take the same class. Similarly, to generate a vector representing the class, each class was set as a node, and the edge connected if any students had taken the classes in common. Thus, we utilize Node2Vec, a representation learning methodology that quantifies the characteristics of each node. For the evaluation of the model, we used four indicators that are mainly utilized by recommendation systems, and experiments were conducted on three different dimensions to analyze the impact of embedding dimensions on the model. The results show better performance on evaluation metrics regardless of dimension than when using one-hot vectors in existing NeuMF structures. Thus, this work contributes to a network of students (users) and classes (items) to increase expressiveness over existing one-hot embeddings, to match the characteristics of each structure that constitutes the model, and to show better performance on various kinds of evaluation metrics compared to existing methodologies.