• Journal of the Korea Computer Industry Society
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• v.2 no.2
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• pp.123-130
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• 2001

As the growth of database volume, it has required a need and an opportunity of data analysis and extracting knowledge from database. Data mining method is the representative example. The size of most minable data set is huge, and stored in a database. To implement effective mining function, we must extract minable data set to be analyzed from existing relational database, and it must be managed with its generalized information. In this paper, the new mining operator is defined in a similar manner to the existing SQL operators and SQL is extended to extract data subset from relations and to generalize it using domain-oriented method. The background knowledge includes attribute values, which will be mind and generalized information, and it is managed as the same structure with a relation in relational database. These functions are implemented by defining some SQL - like operators and aggregated functions, and we describe the expressive powers of these new operators and functions through examples.

• The Journal of the Korea Contents Association
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• v.10 no.10
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• pp.479-493
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• 2010

Along with the transformation of the knowledge-based environment, e-learning has become a main teaching and learning method, prompting various research efforts to be conducted in this field. One major research area in e-learning involves adaptive learning systems that provide personalized learning content according to each learner's characteristics by taking into consideration a variety of learning circumstances. Active research on ontology-based adaptive learning systems has recently been conducted to provide more efficient and adaptive learning content. In this paper, we design and propose an adaptive learning system based on the concept lattice of Formal Concept Analysis (FCA) with the same objectives as those of ontology approaches. However, we are in pursuit of a system that is suitable for learning of specific domains and one that allows users to more freely and easily build their own adaptive learning systems. The proposed system automatically classifies the learning objects and concepts of an evolved domain in the structure of a concept lattice based on the relationships between the objects and concepts. In addition, the system adaptively constructs and presents the learning structure of the concept lattice according to each student's level of knowledge, learning style, learning preference and the learning state of each concept.

• Journal of KIISE:Computing Practices and Letters
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• v.13 no.2
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• pp.100-109
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• 2007

Object recognition of service robots is very important for most of services such as delivery, and errand. Conventional methods are based on the geometric models in static industrial environments, but they have limitations in indoor environments where the condition is changable and the movement of service robots occur because the interesting object can be occluded or small in the image according to their location. For solving these uncertain situations, in this paper, we propose the method that exploits observed objects as context information for predicting interesting one. For this, we propose the method for modeling domain knowledge in probabilistic frame by adopting Bayesian networks and ontology together, and creating knowledge model dynamically to extend reasoning models. We verify the performance of our method through the experiments and show the merit of inductive reasoning in the probabilistic model

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.1
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• pp.530-536
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• 2017

The purpose of this study is to specify the procedures in problem design to employ BPBL and to design problems for learning subject content. The design of problems is crucial for the effectiveness of BPBL. Ineffective PBL problems could affect whether students acquire sufficient domain knowledge, activate appropriate prior knowledge, and properly direct their own learning. The procedures for designing good problems are composed of selection of educational content, figuring out the learner's characteristics, finding problems, setting roles and situations, and writing down problems. Using the procedures, we designed five integration problems covering the content of an Internet marketing subject. We can foster talent needed on a current industrial site with BPBL, not whole-class learning in a Mongolian engineering college. We made a plan for the Internet marketing subject based on BPBL in the Mongolian engineering college, and focused on the process of designing problems.

• Journal of KIISE:Software and Applications
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• v.35 no.3
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• pp.189-196
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• 2008

To build autonomous agents who can make a decision on behalf of humans in time-critical complex environments, the formulation of operational knowledge base could be essential. This paper proposes the methodology of how to formulate the knowledge base and evaluates it in a practical application domain. We analyze threat data received from the multiple sensors of Aircraft Survivability Equipment(ASE) for Korean helicopters, and integrate the threat data into the inductive model through compilation technique which extracts features of the threat data and relations among them. The compiled protocols of state-action rules can be implemented as the brain of the ASE. They can reduce the amounts of reasoning, and endow the autonomous agents with reactivity and flexibility. We report experimental results that demonstrate the distinctive and predictive patterns of threats in simulated battlefield settings, and show the potential of compilation methods for the successful detection of threat systems.

• Journal of Family Resource Management and Policy Review
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• v.14 no.4
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• pp.39-56
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• 2010

The purpose of this research was to analyze the relationship between environment-friendly housing behaviors and the influential variables among consumers by focusing on housing life. This research carried out a questionnaire survey with housewives living in Seoul and the Metropolitan area using a questionnaire that was composed based on previous research. The housewives' environment-friendly housing attitudes and their resource-saving knowledge level, along with environmental education-related variables, house ownership, and the housewives' employment appeared to be significant variables in explaining the generic environment-friendly housing behaviors found in this research. Three regression equations, classified into purchasing, utilization, and disposal behaviors, were employed. The empirical results were relatively similar to those for the generic environment-friendly housing behaviors; however, each model provided somewhat different results in some specific aspects. All the environmental education-related variables appeared to be significant in explaining environment-friendly housing utilization behaviors, and were similar to the results obtained from the generic environment-friendly housing behaviors. However, the variables were limited in explaining environment-friendly housing-related disposal behaviors. The convenience, recycling ease, and family-structure variables appeared to be significant influences on environment-friendly housing-related disposal behaviors. Most empirical results of this research were consistent with those of previous studies. However, the explanatory independent variables varied, depending on the types of each domain of environment-friendly housing behaviors studied.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.5
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• pp.2122-2140
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• 2020

Proper and precise embedding of commercial ads within Webpages requires Ad-hoc analysis and understanding of their content. By the successful implementation of this step, both publishers and advertisers gain mutual benefits through increasing their revenues on the one hand, and improving user experience on the other. In this research work, we propose a novel multi-level context-based ads serving approach through which ads will be served at generic publisher websites based on their contextual relevance. In the proposed approach, knowledge encoded in domain-specific and generic semantic repositories is exploited in order to analyze and segment Webpages into sets of contextually-relevant segments. Semantically-enhanced indexes are also constructed to index ads based on their textual descriptions provided by advertisers. A modified cosine similarity matching algorithm is employed to embed each ad from the Ads repository into one or more contextually-relevant segments. In order to validate our proposal, we have implemented a prototype of an ad serving system with two datasets that consist of (11429 ads and 93 documents) and (11000 documents and 15 ads), respectively. To demonstrate the effectiveness of the proposed techniques, we experimentally tested the proposed method and compared the produced results against five baseline metrics that can be used in the context of ad serving systems. In addition, we compared the results produced by our system with other state-of-the-art models. Findings demonstrate that the accuracy of conventional ad matching techniques has improved by exploiting the proposed semantically-enhanced context-based ad serving model.

• The Journal of Korean Association of Computer Education
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• v.9 no.6
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• pp.29-39
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• 2006

The Web is evolving quantitatively into an explosive development. However, users usually have heavy burden of searching information because of the absence of contextual meaning on the Web. Due to an enormous amount of information, users have to endure for finding strong cohesive keywords by themselves and read each of the documents with enduring effort. This paper proposes an efficient method of searching more relative documents than current KEM-based searching systems on the Web by using contextual meaning. We designed a domain ontology on computer hardware, and a searching system which was searching those e-Learning objects. Owing to the Ontology-applied search system, information such as educational materials and related multimedia can be easily provided to the users. Further, learners could be informed of relationship of knowledge, e.g., class hierarchy, properties and values, and so on. The request results are semantically related to users' needs, and thus the system provides a learner-centered searching.

• Journal of Intelligence and Information Systems
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• v.14 no.4
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• pp.1-18
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• 2008

By using previous similar case plans, the case-based planning (CBP) systems can generate efficiently plans for new problems. However, most existing CBP systems show limited functionalities for case retrieval and case generalization. Moreover, they do not allow their users to participate in the process of plan generation. To support efficient memory use and case retrieval, the proposed case-based planning system, JCBP, groups the set of cases sharing the same goal in each domain into individual case bases and maintains indexes to these individual case bases. The system applies the heuristic knowledge automatically extracted from the problem model to the case adaptation phase. It provides a sort of case generalization through goal regression. Also JCBP can operate in an interactive mode to support a mixed-initiative planning. Since it considers and utilizes user's preference and knowledge for solving the given planning problems, it can generate solution plans satisfying more user's needs and reduce the complexity of plan generation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.683-688
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• 2018

In the 21stcentury, teachers must welcome new technology to ensure the best learning in virtual classrooms, aside from the physical classroom. Google Classroom provides a vital chance to promote blended learning and professional development. The purpose of this study is to specify the procedures in problem design when employing blended problem-based learning (BPBL) and to design problems for learning the contents of the subject. The design of problems is crucial for effective BPBL. The underlying theory of BPBL is that learning is most effectively initiated and facilitated by posing and solving real-life problems that interest the learner, because working on such problems makes learning meaningful and motivates students. Ineffective problem-based learning (PBL) could affect students when acquiring sufficient domain knowledge, activating appropriate prior knowledge, and properly directing their own learning. The procedures for designing good problems are composed of the selection of educational content, figuring out the learner's characteristics, finding problems, setting up roles and situations, and writing down problems. Using these procedures, we designed five integration problems covering the contents of management subjects. Planned management subjects based on BPBL in a Mongolian university focuses on the process of designing problems.