• Journal of Intelligence and Information Systems
• /
• v.17 no.4
• /
• pp.19-36
• /
• 2011

Since the ambitious declaration of the vision of the Semantic Web, a growing number of studies on semantic search have recently been made. However, we recognize that our community has not so much accomplished despite those efforts. We analyze two underlying problems : a lack of a shared notion of semantic search that guides current research, and a lack of a comprehensive view that envisions future work. Based on this diagnosis, we start by defining semantic search as the process of retrieving desired information in response to user's input using semantic technologies such as ontologies. Then, we propose a classification framework in order for the community to obtain the better understanding of semantic search. The proposed classification framework consists of input processing, target source, search methodology, results ranking, and output data type. Last, we apply our proposed framework to prior studies and suggest future research directions.

• Information Systems Review
• /
• v.7 no.2
• /
• pp.23-40
• /
• 2005

Discovering customers' behavioral patterns from large data set and providing them with corresponding services or products are critical components in managing a current business. However, the diversity of customer needs coupled with the limited resources suggests that companies should make more efforts on understanding and managing specific groups of customers, not the whole customers. The key issue of this paper is based on the fact that the behavioral patterns extracted from the specific groups of customers shall be different from those from the whole customers. This paper proposes the idea of pre-segmentation before developing customer classification models. We collected three customers' demographic and transactional data sets from a credit card, a tele-communication, and an insurance company in Korea, and then segmented customers by major variables. Different churn prediction models were developed from each segments and the whole data set, respectively, using the decision tree induction approach, and compared in terms of the hit ratio and the simplicity of generated rules.

• Information Systems Review
• /
• v.25 no.2
• /
• pp.57-76
• /
• 2023

This study classifies the success of creators and YouTubers who have created channels on YouTube recently, which is the most influential digital platform. Based on the actual information disclosure of YouTubers who are in the field of science and technology category, video upload cycle, video length, number of selectable multilingual subtitles, and information from other social network channels that are being operated, the success of YouTubers using machine learning was classified and analyzed, which is the closest to the YouTube revenue structure. Our findings showed that neural network algorithm provided the best performance to predict the success or failure of YouTubers. In addition, our five factors contributed to improve the performance of the classification. This study has implications in suggesting various approaches to new individual entrepreneurs who want to start YouTube, influencers who are currently operating YouTube, and companies who want to utilize these digital platforms. We discuss the future direction of utilizing digital platforms.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.10a
• /
• pp.440-443
• /
• 2005

The wide spread of internet business recently necessitates recommendation systems which can recommend the most suitable product fur customer demands. Currently the recommendation systems use content-based filtering and/or collaborative filtering methods, which are unable both to explain the reason for the recommendation and to reflect constantly changing requirements of the users. These methods guarantee good efficiency only if there is a lot of information about users. This paper proposes an algorithm called 'demand articulate & integration' which can perceive user's continuously varying intents and recommend proper contents. A method of knowledge classification which can be applicable to this algorithm is also developed in order to disassemble knowledge into basic units and articulate indices. The algorithm provides recommendation outputs that are close to expert's opinion through the tracing of articulate index. As a case study, a knowledge base for heritage information is constructed with the expert guide's knowledge. An intelligent recommendation system that can guide heritage tour as good as the expert guider is developed.

• Information Systems Review
• /
• v.19 no.4
• /
• pp.111-134
• /
• 2017

This study compares the linkage effects and competitive advantage between ICT and automobile industry in Korea from 1996 to 2011 using input-output tables. The ICT industry is classified according to the International Standard Industry Classification. Results show that (1) the ICT industry exhibits linkage effects similar to those of automobile industry. (2) Both ICT and automobile manufacturing sectors exert significant effects on the demand and supply. Additionally, (3) ICT service and automobile sectors show linkage effects on demand and supply, respectively. The present results present the classification criteria of the ICT industry discussed to date and suggest economic effects and policy implications.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 2005.04a
• /
• pp.153-156
• /
• 2005

In this paper, a framework for implementing UFIS (Unified Fuzzy rule-based knowledge Inference System) is presented. First, fuzzy clustering and fuzzy rules deal with the presence of the knowledge in DB (DataBase) and its value is presented with a value between 0 and 1. Second, RDB (Relational DB) and SQL queries provide more flexible functionality fur knowledge management than the conventional non-fuzzy knowledge management systems. Therefore, the obtained fuzzy rules offer the user additional information to be added to the query with the purpose of guiding the search and improving the retrieval in knowledge base and/ or rule base. The framework can be used as DM (Data Mining) and ES (Expert Systems) development and easily integrated with conventional KMS (Knowledge Management Systems) and ES.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.37 no.3
• /
• pp.75-81
• /
• 2014

To establish a new creative economy, the worldwide efforts have been made to wriggle out the old economic paradigm after the financial crisis of 2008. When it comes to the limitation of the viability. the start up companies have a high risk of failure. Therefore business incubator (BI) should carry out the role to improve their viability. As for the maximization of the effect on the business incubating services as move in companies, it is important for BI to increase the level of business incubating services by the systemic and scientific measurement. This study showed that the quality of the BI center services was measured by Kano analysis and the previous research as follows. First, BI quality attribute by Timko's customer satisfaction came out into the attractive qualities on the 14 items that amount to the 70% of 20 business incubating services items. It is desirable to perform the strategy for the satisfaction. Secondly, basic business incubating services were interpreted as the one-dimensional quality like incubating spaces, parking facilities, security facilities, industry technology development funds, and incubating managers. Finally, training and educational service was recognized as indifferent quality. Futhermore, the improvement and the limitation of this study as well as the interpretation of analysis results are also provided.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.12
• /
• pp.57-66
• /
• 2023

In this study, we propose a novel research framework for the recommendation system that can estimate the user's emotional state and reflect it in the recommendation process by applying deep learning techniques and emotion AI (artificial intelligence). To this end, we build an emotion classification model that classifies each of the seven emotions of angry, disgust, fear, happy, sad, surprise, and neutral, respectively, and propose a model that can reflect this result in the recommendation process. However, in the general emotion classification data, the difference in distribution ratio between each label is large, so it may be difficult to expect generalized classification results. In this study, since the number of emotion data such as disgust in emotion image data is often insufficient, correction is made through augmentation. Lastly, we propose a method to reflect the emotion prediction model based on data through image augmentation in the recommendation systems.

• Journal of Intelligence and Information Systems
• /
• v.28 no.3
• /
• pp.23-43
• /
• 2022

In order to apply AI (Artificial Intelligence) in various industries, interest in algorithm selection is increasing. Algorithm selection is largely determined by the experience of a data scientist. However, in the case of an inexperienced data scientist, an algorithm is selected through meta-learning based on dataset characteristics. However, since the selection process is a black box, it was not possible to know on what basis the existing algorithm recommendation was derived. Accordingly, this study uses k-means cluster analysis to classify types according to data set characteristics, and to explore suitable classification algorithms and methods for resolving class imbalance. As a result of this study, four types were derived, and an appropriate class imbalance resolution method and classification algorithm were recommended according to the data set type.

• Journal of Korean Society of Archives and Records Management
• /
• v.6 no.2
• /
• pp.57-85
• /
• 2006

There should be an integration between work management and records management in order to document the work processes thoroughly. It's proper to establish a records classification system to have the work classification and record classification table integrated for that purpose. But the conventional procedures and methodology used for records classification system development lack specific features to be used as voluntary guidelines of a common organization or group and to conduct analysis. Recognizing the problems, this study suggested the specific methods of records classification system development to link work management and records management organically. First, the functional classification was chosen as the principle of classification for records classification system development. Then concrete methods of records classification system development were suggested. Analysis and comparison were made for the DIRKS(Designing and Implementing Recordkeeping Systems), which is the standard records management and work analysis of Australia, and AS 5090. The results were used to suggest specific methods of records classification system development in conjunction with the research into the methodology employed for work analysis in information engineering and business administration to compensate for its weakness. The significance of the study can be found in that it suggested the methods of typical records classification system development in connection with records classification, and that it applied them to the Presidential Committee for the Inspection of Collaborations for Japanese Imperialism and tested them.