Life cycle assessment (LCA) and life cycle cost (LCC) are two primary methods used to assess the environmental and economic feasibility of building construction. An estimation of the building's life span is essential to carrying out these methods. However, given the diverse factors that affect the building's life span, it was estimated typically based on its main structural type. However, different buildings have different life spans. Simply assuming that all buildings with the same structural type follow an identical life span can cause serious estimation errors. In this study, we collected 1,812,700 records describing buildings built and demolished in South Korea, analysed the actual life span of each building, and developed a building life-span prediction model using deep-learning and traditional machine learning. The prediction models examined in this study produced root mean square errors of 3.72–4.6 and the coefficients of determination of 0.932–0.955. Among those models, a deep-learning based prediction model was found the most powerful. As anticipated, the conventional method of determining a building's life expectancy using a discrete set of specific factors and associated assumptions of life span did not yield realistic results. This study demonstrates that an application of deep learning to the LCA and LCC of a building is a promising direction, effectively guiding business planning and critical decision making throughout the construction process.

Sukwon Ji; BumHo Lee; Mun Yong Yi. Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach. Building and Environment 2021, 205, 108267 .

Sukwon Ji, BumHo Lee, Mun Yong Yi. Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach. Building and Environment. 2021; 205 ():108267.

Sukwon Ji; BumHo Lee; Mun Yong Yi. 2021. "Building life-span prediction for life cycle assessment and life cycle cost using machine learning: A big data approach." Building and Environment 205, no. : 108267.

This study examines the factors affecting users' adoption of the smartphone as an innovative device. Prior studies on the acceptance of computing devices have primarily focused on the impact of the technological benefits and characteristics. Meanwhile, there is a lack of research approaching user resistance, which hinders the diffusion of an innovation. In particular, the smartphone is a highly communication-oriented device that people's attitude and evaluation critically influence its further diffusion. However, few studies have validated this link in the smartphone adoption context. Therefore, this study has attempted to build a research model that explains factors affecting user's resistance to smartphone adoption by integrating technological and social antecedents forming the resistance, and empirically analyzes the data obtained through a survey. As a result, the relative complexity and relative advantages presented in the theory of innovation diffusion had a direct impact on the user's resistance.

Jaeheung Yoo; Saesol Choi; Yujong Hwang; Mun Y. Yi. The Role of User Resistance and Social Influences on the Adoption of Smartphone. Journal of Organizational and End User Computing 2021, 33, 36 -58.

Jaeheung Yoo, Saesol Choi, Yujong Hwang, Mun Y. Yi. The Role of User Resistance and Social Influences on the Adoption of Smartphone. Journal of Organizational and End User Computing. 2021; 33 (2):36-58.

Jaeheung Yoo; Saesol Choi; Yujong Hwang; Mun Y. Yi. 2021. "The Role of User Resistance and Social Influences on the Adoption of Smartphone." Journal of Organizational and End User Computing 33, no. 2: 36-58.

Nowcasting is an important technique for weather forecasting because sudden weather changes significantly affect human life. The encoding-forecasting model, which is a state-of-the-art architecture in the field of data-driven radar extrapolation, does not particularly focus on the latest data when forecasting natural phenomena. This paper proposes a weighted broadcasting method that emphasizes the latest data of the time step to improve the nowcasting performance. This weighted broadcasting method allows the most recent rainfall patterns to have a greater impact on the forecasting network by extending the architecture of the existing encoding-forecasting model. Experimental results show that the proposed model is 1.74% and 2.20% better than the existing encoding-forecasting model in terms of mean absolute error and critical success index, respectively. In the case of heavy rainfall with an intensity of 30 mm/h or higher, the proposed model was more than 30% superior to the existing encoding-forecasting model. Therefore, applying the weighted broadcasting method, which explicitly places a high emphasis on the latest information, to the encoding-forecasting model is considered as an improvement that is applicable to the state-of-the-art implementation of data-driven radar-based precipitation nowcasting.

Chang Jeong; Wonsu Kim; Wonkyun Joo; Dongmin Jang; Mun Yi. Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step. Atmosphere 2021, 12, 261 .

Chang Jeong, Wonsu Kim, Wonkyun Joo, Dongmin Jang, Mun Yi. Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step. Atmosphere. 2021; 12 (2):261.

Chang Jeong; Wonsu Kim; Wonkyun Joo; Dongmin Jang; Mun Yi. 2021. "Enhancing the Encoding-Forecasting Model for Precipitation Nowcasting by Putting High Emphasis on the Latest Data of the Time Step." Atmosphere 12, no. 2: 261.

As the number of researchers in South Korea has grown, there is increasing dissatisfaction with the selection process for national research and development (R&D;) projects among unsuccessful applicants. In this study, we designed a system that can recommend the best possible R&D; evaluators using big data that are collected from related systems, refined, and analyzed. Our big data recommendation system compares keywords extracted from applications and from the full-text of the achievements of the evaluator candidates. Weights for different keywords are scored using the term frequency–inverse document frequency algorithm. Comparing the keywords extracted from the achievement of the evaluator candidates’, a project comparison module searches, scores, and ranks these achievements similarly to the project applications. The similarity scoring module calculates the overall similarity scores for different candidates based on the project comparison module scores. To assess the performance of the evaluator candidate recommendation system, 61 applications in three Review Board (RB) research fields (system fusion, organic biochemistry, and Korean literature) were recommended as the evaluator candidates by the recommendation system in the same manner as the RB’s recommendation. Our tests reveal that the evaluator candidates recommended by the Korean Review Board and those recommended by our system for 61 applications in different areas, were the same. However, our system performed the recommendation in less time with no bias and fewer personnel. The system requiresrevisions to reflect qualitative indicators, such as journal reputation, before it can entirely replace the current evaluator recommendation process.

Sukil Cha; Mun Y. Yi; Sekyoung Youm. Design and Implementation of a Big Data Evaluator Recommendation System Using Deep Learning Methodology. Applied Sciences 2020, 10, 8000 .

Sukil Cha, Mun Y. Yi, Sekyoung Youm. Design and Implementation of a Big Data Evaluator Recommendation System Using Deep Learning Methodology. Applied Sciences. 2020; 10 (22):8000.

Sukil Cha; Mun Y. Yi; Sekyoung Youm. 2020. "Design and Implementation of a Big Data Evaluator Recommendation System Using Deep Learning Methodology." Applied Sciences 10, no. 22: 8000.

In this paper, we propose a novel procedure designed to apply comparable sales method to the automated price estimation of real estates, in particular, that of apartments. Apartments are the most popular residential housing type in Korea. The price of a single apartment is influenced by many factors, making it hard to estimate accurately. Moreover, as an apartment is purchased for living, with a sizable amount of money, it is mostly traded infrequently. Thus, its past transaction price may not be particularly helpful to the estimation after a certain period of time. For these reasons, the up-to-date price of an apartment is commonly estimated by certified appraisers, who typically rely on comparable sales method (CSM). CSM requires comparable properties to be identified and used as references in estimating the current price of the property in question. In this research, we develop a procedure to systematically apply this procedure to the automated estimation of apartment prices and assess its applicability using nine years’ real transaction data from the capital city and the most-populated province in South Korea and multiple scenarios designed to reflect the conditions of low and high fluctuations of housing prices. The results from extensive evaluations show that the proposed approach is superior to the traditional approach of relying on real estate professionals and also to the baseline machine learning approach.

Yunjong Kim; Seungwoo Choi; Mun Yi. Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices. Sustainability 2020, 12, 5679 .

Yunjong Kim, Seungwoo Choi, Mun Yi. Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices. Sustainability. 2020; 12 (14):5679.

Yunjong Kim; Seungwoo Choi; Mun Yi. 2020. "Applying Comparable Sales Method to the Automated Estimation of Real Estate Prices." Sustainability 12, no. 14: 5679.

Gamified learning systems can enhance both learning outcomes and engagement, but research findings on the effectiveness of such systems are mixed, and there is inadequate attention to theory-grounded designs of gamified learning systems. We address these gaps by conducting a theory-grounded design, development, and evaluation of a gamified e-training system for technology learning. Called GAMESIT, this e-training system has an added gamification layer. Drawing upon Malone's theory of intrinsically motivating instruction, we choose and design gamification elements (e.g., levels, avatar evolution, and distinct visuals) to create appropriate motivational drivers, namely, challenge, curiosity, and fantasy, for learning tasks. We follow a design science framework to iteratively develop GAMESIT and evaluate its effectiveness. In a laboratory experiment, participants using GAMESIT, when compared to those using the non-gamified e-training system, showed improvement in learning outcomes, measured as learners' knowledge comprehension and task performance, and higher engagement, captured through learners' cognitive effort.

Juneyoung Park; De Liu; Mun Y. Yi; Radhika Santhanam. GAMESIT: A gamified system for information technology training. Computers & Education 2019, 142, 103643 .

Juneyoung Park, De Liu, Mun Y. Yi, Radhika Santhanam. GAMESIT: A gamified system for information technology training. Computers & Education. 2019; 142 ():103643.

Juneyoung Park; De Liu; Mun Y. Yi; Radhika Santhanam. 2019. "GAMESIT: A gamified system for information technology training." Computers & Education 142, no. : 103643.

The difficulty of designing intrinsically integrated game-based learning systems has led to alternative design strategies based on extrinsic integration. This study extends prior work on extrinsic integration design by examining the effectiveness of alternative reward structures in integrating learning and game. Specifically, a performance-contingent reward is proposed as a new integration mechanism and its effects on learning, motivation, engagement, and system perception are assessed, vis-à-vis a completion-contingent reward. A group of university students (N = 64) were involved in an empirical experiment designed to determine the effectiveness of the new reward structure in the context of English vocabulary learning and arrow-shooting gaming. The results from the experiment show that the proposed reward structure produces a statistically significant increase in the level of learning, motivation, and engagement. The results are highly encouraging for game-based learning research as the proposed approach is easily extendable, with design implications that are directly applicable.

Juneyoung Park; Seunghyun Kim; Auk Kim; Mun Y. Yi. Learning to be better at the game: Performance vs. completion contingent reward for game-based learning. Computers & Education 2019, 139, 1 -15.

Juneyoung Park, Seunghyun Kim, Auk Kim, Mun Y. Yi. Learning to be better at the game: Performance vs. completion contingent reward for game-based learning. Computers & Education. 2019; 139 ():1-15.

Juneyoung Park; Seunghyun Kim; Auk Kim; Mun Y. Yi. 2019. "Learning to be better at the game: Performance vs. completion contingent reward for game-based learning." Computers & Education 139, no. : 1-15.

The South Korean government operates human-based lyrics-rating systems to reduce adolescents’ exposure to inappropriate songs. In this study, we developed lyrics classification models for an automated lyrics-rating system for adolescents. There are two kinds of inappropriate lyrics for adolescents: (1) lyrics with inappropriate words and (2) lyrics with inappropriate content based on the semantic context. To tackle the first issue, we propose \( {\text{logCD}}_{\alpha } \) as a method for generating a lexicon of inappropriate words. It attained the highest performance among the lexicon-based filtering methods examined. Further, to deal with the second issue, we propose a hybrid classification model that combines \( {\text{logCD}}_{\alpha } \) with an RNN based model. The hybrid model composed of a ‘lexicon-checking model’ and a ‘context-checking model’ achieved the highest performance among all of the models examined, highlighting the effectiveness of combining the models to specifically target each of the two types of inappropriate lyrics.

Jayong Kim; Mun Y. Yi. A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents. Computer Vision 2019, 779 -786.

Jayong Kim, Mun Y. Yi. A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents. Computer Vision. 2019; ():779-786.

Jayong Kim; Mun Y. Yi. 2019. "A Hybrid Modeling Approach for an Automated Lyrics-Rating System for Adolescents." Computer Vision , no. : 779-786.

In this paper, we share our experience in augmenting a focused crawler of our vertical search engine designed to work with academic slides. The goal of the focused crawler was to collect Microsoft PowerPoint files from academic institutions. A previous approach based on a general web crawler can fail to collect a sufficient number of files mainly because of the robots exclusion protocol and missing hyperlinks. As a remedy to these problems, we propose a combinatory approach in which the indexing information maintained by a general web search engine such as Google is utilized for target URL list generation through our query generator, further then complemented by our URL extractor and file downloader. Because Google has already crawled billions of web pages, it will be more cost-efficient and potentially effective to systematically retrieve the desired information from Google than to redo crawling from scratch by ourselves. Our focused crawler, which we call SlideCrawler, has been used for our vertical search engine CourseShare since the fall of 2011. The capability of SlideCrawler was verified for the top-500 world wide universities. SlideCrawler collected about one million files from the top-500 universities. Further, the study results show that SlideCrawler outperforms Nutch, collecting 3.7 times more slide files.

Jae-Gil Lee; Donghwan Bae; Sansung Kim; Jungeun Kim; Mun Yong Yi. An effective approach to enhancing a focused crawler using Google. The Journal of Supercomputing 2019, 76, 8175 -8192.

Jae-Gil Lee, Donghwan Bae, Sansung Kim, Jungeun Kim, Mun Yong Yi. An effective approach to enhancing a focused crawler using Google. The Journal of Supercomputing. 2019; 76 (10):8175-8192.

Jae-Gil Lee; Donghwan Bae; Sansung Kim; Jungeun Kim; Mun Yong Yi. 2019. "An effective approach to enhancing a focused crawler using Google." The Journal of Supercomputing 76, no. 10: 8175-8192.

Clinical decision support (CDS) search is performed to retrieve key medical literature that can assist the practice of medical experts by offering appropriate medical information relevant to the medical case in hand. In this paper, we present a novel CDS search framework designed for passage retrieval from biomedical textbooks in order to support clinical decision-making using laboratory test results. The framework utilizes two unique characteristics of the textual reports derived from the test results, which are syntax variation and negation information. The proposed framework consists of three components: domain ontology, index repository, and query processing engine. We first created a domain ontology to resolve syntax variation by applying the ontology to detect medical concepts from the test results with language translation. We then preprocessed and performed indexing of biomedical textbooks recommended by clinicians for passage retrieval. We finally built the query-processing engine tailored for CDS, including translation, concept detection, query expansion, pseudo-relevance feedback at the local and global levels, and ranking with differential weighting of negation information. To evaluate the effectiveness of the proposed framework, we followed the standard information retrieval evaluation procedure. An evaluation dataset was created, including 28,581 textual reports for 30 laboratory test results and 56,228 passages from widely used biomedical textbooks, recommended by clinicians. Overall, our proposed passage retrieval framework, GPRF-NEG, outperforms the baseline by 36.2, 100.5, and 69.7 percent for MRR,R-precision, and Precision at 5, respectively. Our study results indicate that the proposed CDS search framework specifically designed for passage retrieval of biomedical literature represents a practically viable choice for clinicians as it supports their decision-making processes by providing relevant passages extracted from the sources that they prefer to refer to, with improved performances.

Keejun Han; Hyoeun Shim; Mun Y. Yi. A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting. Journal of Healthcare Engineering 2018, 2018, 1 -19.

Keejun Han, Hyoeun Shim, Mun Y. Yi. A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting. Journal of Healthcare Engineering. 2018; 2018 ():1-19.

Keejun Han; Hyoeun Shim; Mun Y. Yi. 2018. "A New Biomedical Passage Retrieval Framework for Laboratory Medicine: Leveraging Domain-specific Ontology, Multilevel PRF, and Negation Differential Weighting." Journal of Healthcare Engineering 2018, no. : 1-19.

Electrophoresis (EP) test separates protein components based on their density. Patterns exhibited by this test mostly show very close approximation, making it difficult to examine test results within a short amount of time as it has many variations of patterns and requires a significant amount of knowledge to discern them accurately. To help clinical examiners save time and produce consistent results, a new deep-learning model optimized for EP graphic images was developed. Extending recent work on capsule network, which is a state-of-the-art deep learning model, this study was carried out to develop a best-performing model in classifying abnormal and normal electrophoresis patterns. Instead of extracting features from the image, we used the whole slide image as an input to the classifier. This study used 39,484 electrophoresis 2D graph images and utilized capsule network as the foundation of the deep learning architecture to learn the images without data augmentation. The formulated models were compared for a multitude of performance metrics including accuracy, sensitivity, and specificity. Overall, the study results show that our proposed architecture EP-CapsNet, which combines capsule network with Google's inception module, is the best performing model, outperforming the baseline and alternative models in almost all comparisons.

Elizabeth Tobing; Ashraf Murtaza; Keejun Han; Mun Y. Yi. [Regular Paper] EP-CapsNet: Extending Capsule Network with Inception Module for Electrophoresis Binary Classification. 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE) 2018, 327 -333.

Elizabeth Tobing, Ashraf Murtaza, Keejun Han, Mun Y. Yi. [Regular Paper] EP-CapsNet: Extending Capsule Network with Inception Module for Electrophoresis Binary Classification. 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE). 2018; ():327-333.

Elizabeth Tobing; Ashraf Murtaza; Keejun Han; Mun Y. Yi. 2018. "[Regular Paper] EP-CapsNet: Extending Capsule Network with Inception Module for Electrophoresis Binary Classification." 2018 IEEE 18th International Conference on Bioinformatics and Bioengineering (BIBE) , no. : 327-333.

Wikipedia plays a central role in the web as one of the biggest knowledge source due to its large coverage of information that comes from various domains. However, due to the enormous number of pages and limited number of contributors to maintain all of the pages, the problem of missing information among Wikipedia articles has emerged, especially articles in multiple language versions. Several approaches have been studied to fix information gap in between cross- language Wikipedia articles. However, they can only be applied for languages that came from the same root. In this paper, we propose an approach to generate new information for Wikipedia infoboxes written in different languages with different roots by utilizing the existing DBpedia mappings. We combined mapping information from DBpedia with an instance-based method to align the existing Korean-English infobox attribute-value pairs as well as to generate new pairs from the Korean version to fill missing information in the English version. The results showed that we could expand up to 38% of the existing English Wikipedia attribute-value pairs from our datasets with 61% of accuracy.

Megawati; Saemi Jang; Mun Yong Yi. Utilization of DBpedia Mapping in Cross Lingual Wikipedia Infobox Completion. Transactions on Petri Nets and Other Models of Concurrency XV 2016, 303 -316.

Megawati, Saemi Jang, Mun Yong Yi. Utilization of DBpedia Mapping in Cross Lingual Wikipedia Infobox Completion. Transactions on Petri Nets and Other Models of Concurrency XV. 2016; ():303-316.

Megawati; Saemi Jang; Mun Yong Yi. 2016. "Utilization of DBpedia Mapping in Cross Lingual Wikipedia Infobox Completion." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 303-316.

Computerized presentation slides have become essential for many occasions such as business meetings, classroom discussions, multipurpose talks and public events. Given the tremendous increases in online resources and materials, locating high-quality slides relevant to a given task is often a formidable challenge, particularly when a user looks for superior quality slides. This study proposes a new, comprehensive framework for information quality (IQ) developed specifically for computerized presentation slides and explores the possibility of automatically detecting the IQ of slides. To determine slide-specific IQ criteria as well as their relative importances, we carried out a user study, involving 60 participants from two universities, and conducted extensive coding analysis. Further, we subsequently conducted a series of multiple experiments to examine the validity of the IQ features developed on the basis of the selected criteria from the user study. The study findings contribute to identifying key dimensions and related features that can improve effective IQ assessments of computerized presentation slides.

Seongchan Kim; Jae-Gil Lee; Mun Y. Yi. Developing information quality assessment framework of presentation slides. Journal of Information Science 2016, 43, 742 -768.

Seongchan Kim, Jae-Gil Lee, Mun Y. Yi. Developing information quality assessment framework of presentation slides. Journal of Information Science. 2016; 43 (6):742-768.

Seongchan Kim; Jae-Gil Lee; Mun Y. Yi. 2016. "Developing information quality assessment framework of presentation slides." Journal of Information Science 43, no. 6: 742-768.

The gamification of online learning has been a subject of interest lately. This study attempts to explore two things in particular, the effects of gamification on learning and the moderating effects of user characteristics. The results demonstrate that the gamification elements contribute to higher learning outcomes while two user characteristics, agreeableness and pre-training motivation, are important moderators of the links between the gamification elements and learning outcomes. The study findings indicate that a gamified system in consideration of user characteristics is an effective means to improving the efficacy of the e-learning environment.

Jincheul Jang; Jason J. Y. Park; Mun Y. Yi. Gamification of Online Learning. Cloud Computing and Security 2015, 646 -649.

Jincheul Jang, Jason J. Y. Park, Mun Y. Yi. Gamification of Online Learning. Cloud Computing and Security. 2015; ():646-649.

Jincheul Jang; Jason J. Y. Park; Mun Y. Yi. 2015. "Gamification of Online Learning." Cloud Computing and Security , no. : 646-649.

The data derived from the social tagging system, known as folksonomy, is a potentially useful source for understanding users' intentions. This study seeks to uncover some of the unexplored areas of folksonomy and examine the plausibility of new ideas for the improvement of personalized search. In particular, we challenge several state-of-the-art algorithms by exploiting folksonomy network structures used in creating user profiles that are adaptive and aware of multiple interests of a user, for the personalization of search results. The results obtained from the proposed approach shows a unanimous increase in the performance of personalization when compared to other state-of-the-art algorithms.

Keejun Han; Juneyoung Park; Mun. Y. Yi. Adaptive and multiple interest-aware user profiles for personalized search in folksonomy: A simple but effective graph-based profiling model. 2015 International Conference on Big Data and Smart Computing (BIGCOMP) 2015, 225 -231.

Keejun Han, Juneyoung Park, Mun. Y. Yi. Adaptive and multiple interest-aware user profiles for personalized search in folksonomy: A simple but effective graph-based profiling model. 2015 International Conference on Big Data and Smart Computing (BIGCOMP). 2015; ():225-231.

Keejun Han; Juneyoung Park; Mun. Y. Yi. 2015. "Adaptive and multiple interest-aware user profiles for personalized search in folksonomy: A simple but effective graph-based profiling model." 2015 International Conference on Big Data and Smart Computing (BIGCOMP) , no. : 225-231.

With the advent of smartphones, mobile phones have evolved from a simple communication tool to a multipurpose device that affects every aspect of our daily life. The expansion of the mobile application market has made it difficult for smartphone users to find applications that fit their needs. Most prior research on application recommendation provides a limited solution to the problem of application overload. These recommendation techniques, developed outside of the mobile environment, have a number of limitations such as cold start problem and domain disparity. In this paper, we propose AppTrends, which incorporates a graph-based technique for application recommendation in the Android OS environment. Our experiment results obtained from the field usage record of over 4 million applications clearly show that the proposed graph-based recommendation model is more accurate than the Slope One Model.

Donghwan Bae; Keejun Han; Juneyoung Park; Mun. Y. Yi. AppTrends: A graph-based mobile app recommendation system using usage history. 2015 International Conference on Big Data and Smart Computing (BIGCOMP) 2015, 210 -216.

Donghwan Bae, Keejun Han, Juneyoung Park, Mun. Y. Yi. AppTrends: A graph-based mobile app recommendation system using usage history. 2015 International Conference on Big Data and Smart Computing (BIGCOMP). 2015; ():210-216.

Donghwan Bae; Keejun Han; Juneyoung Park; Mun. Y. Yi. 2015. "AppTrends: A graph-based mobile app recommendation system using usage history." 2015 International Conference on Big Data and Smart Computing (BIGCOMP) , no. : 210-216.

As the media content industry is growing continuously, the content market has become very competitive. Various strategies such as advertising and Word-of-Mouth (WOM) have been used to draw people’s attention. It is hard for users to be completely free of others’ influences and thus to some extent their opinions become affected and biased. In the field of recommender systems, prior research on biased opinions has attempted to reduce and isolate the effects of external influences in recommendations. In this paper, we present a new measure to detect opinions that are distinct from the mainstream. This distinctness enables us to reduce biases formed by the majority and thus, to potentially increase the performance of recommendation results. To ensure robustness, we develop four new hybrid methods that are various mixtures of existing collaborative filtering (CF) methods and our new measure of Distinctness. In this way, the proposed methods can reflect the majority of opinions while considering distinct user opinions. We evaluate the methods using a real-life rating dataset with 5-fold cross validation. The experimental results clearly show that the proposed models outperform existing CF methods.

Grace E. Lee; Keejun Han; Mun Y. Yi. Incorporating Distinct Opinions in Content Recommender System. Transactions on Petri Nets and Other Models of Concurrency XV 2015, 109 -120.

Grace E. Lee, Keejun Han, Mun Y. Yi. Incorporating Distinct Opinions in Content Recommender System. Transactions on Petri Nets and Other Models of Concurrency XV. 2015; ():109-120.

Grace E. Lee; Keejun Han; Mun Y. Yi. 2015. "Incorporating Distinct Opinions in Content Recommender System." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 109-120.

본 연구는 내용적으로 고품질인 슬라이드를 구분하고 분류하기 위해, 슬라이드의 지식정보를 내포하는 지식구조를 이용하는 분류 방법을 제안한다. 지식구조가 슬라이드의 내용적 품질정보를 내포하는지에 대해서 분석한 후, 그 결과로부터 지식구조를 이용한 분류 방법을 개발하였고, 슬라이드의 품질별로 분류한 결과를 비교하였다. 비교를 통해 고품질군에 속하는 슬라이드일수록 높은 품질의 슬라이드 위주로 분류할 수 있다는 점을 검증하였다. 이는 품질이 높은 슬라이드 위주로 검색하거나 추천하고자 할 때, 지식구조라는 인지적 모형을 활용하여 그 효과를 높일 수 있음을 보여준다.

Wonchul Jung; Seongchan Kim; Mun Y. Yi. Proposing and Validating a Classification Method based on Knowledge Structure to Identify High-Quality Presentation Slides. KIISE Transactions on Computing Practices 2014, 20, 676 -681.

Wonchul Jung, Seongchan Kim, Mun Y. Yi. Proposing and Validating a Classification Method based on Knowledge Structure to Identify High-Quality Presentation Slides. KIISE Transactions on Computing Practices. 2014; 20 (12):676-681.

Wonchul Jung; Seongchan Kim; Mun Y. Yi. 2014. "Proposing and Validating a Classification Method based on Knowledge Structure to Identify High-Quality Presentation Slides." KIISE Transactions on Computing Practices 20, no. 12: 676-681.

Sansung Kim; Keejun Han; Mun Y. Yi; Sinhee Cho; Seongchan Kim. Exploiting Knowledge Structure for Proximity-aware Movie Retrieval Model. Proceedings of the 23rd ACM international conference on Multimedia 2014, 1847 -1850.

Sansung Kim, Keejun Han, Mun Y. Yi, Sinhee Cho, Seongchan Kim. Exploiting Knowledge Structure for Proximity-aware Movie Retrieval Model. Proceedings of the 23rd ACM international conference on Multimedia. 2014; ():1847-1850.

Sansung Kim; Keejun Han; Mun Y. Yi; Sinhee Cho; Seongchan Kim. 2014. "Exploiting Knowledge Structure for Proximity-aware Movie Retrieval Model." Proceedings of the 23rd ACM international conference on Multimedia , no. : 1847-1850.

The central assumption of Linked Data is that data providers ease the integration of Web data by setting RDF links between data sources. In addition to linking entities, Web data integration also requires the alignment of the different vocabularies that are used to describe entities as well as the resolution of data conflicts between data sources. In this chapter, we present the methods and open source tools that have been developed in the LOD2 project for supporting data publishers to set RDF links between data sources. We also introduce the tools that have been developed for translating data between different vocabularies, for assessing the quality of Web data as well as for resolving data conflicts by fusing data from multiple data sources.

Volha Bryl; Christian Bizer; Robert Isele; Mateja Verlic; Soon Gill Hong; Sammy Jang; Mun Yong Yi; Key-Sun Choi. Interlinking and Knowledge Fusion. Transactions on Petri Nets and Other Models of Concurrency XV 2014, 70 -89.

Volha Bryl, Christian Bizer, Robert Isele, Mateja Verlic, Soon Gill Hong, Sammy Jang, Mun Yong Yi, Key-Sun Choi. Interlinking and Knowledge Fusion. Transactions on Petri Nets and Other Models of Concurrency XV. 2014; ():70-89.

Volha Bryl; Christian Bizer; Robert Isele; Mateja Verlic; Soon Gill Hong; Sammy Jang; Mun Yong Yi; Key-Sun Choi. 2014. "Interlinking and Knowledge Fusion." Transactions on Petri Nets and Other Models of Concurrency XV , no. : 70-89.