• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.10
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• pp.3230-3255
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• 2022

Causality mining in NLP is a significant area of interest, which benefits in many daily life applications, including decision making, business risk management, question answering, future event prediction, scenario generation, and information retrieval. Mining those causalities was a challenging and open problem for the prior non-statistical and statistical techniques using web sources that required hand-crafted linguistics patterns for feature engineering, which were subject to domain knowledge and required much human effort. Those studies overlooked implicit, ambiguous, and heterogeneous causality and focused on explicit causality mining. In contrast to statistical and non-statistical approaches, we present Bidirectional Encoder Representations from Transformers (BERT) integrated with Multi-level Feature Networks (MFN) for causality recognition, called BERT+MFN for causality recognition in noisy and informal web datasets without human-designed features. In our model, MFN consists of a three-column knowledge-oriented network (TC-KN), bi-LSTM, and Relation Network (RN) that mine causality information at the segment level. BERT captures semantic features at the word level. We perform experiments on Alternative Lexicalization (AltLexes) datasets. The experimental outcomes show that our model outperforms baseline causality and text mining techniques.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.884-888
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• 2003

In this paper, we introduce the hybrid data mining mechanism based in association rule and fuzzy neural networks (FNN). Most of data mining mechanisms are depended in the association rule extraction algorithm. However, the basic association rule-based data mining has not the learning ability. In addition, sequential patterns of association rules could not represent the complicate fuzzy logic. To resolve these problems, we suggest the hybrid mechanism using association rule-based data mining, and fuzzy neural networks. Our hybrid data mining mechanism was consisted of four phases. First, we used general association rule mining mechanism to develop the initial rule-base. Then, in the second phase, we used the fuzzy neural networks to learn the past historical patterns embedded in the database. Third, fuzzy rule extraction algorithm was used to extract the implicit knowledge from the FNN. Fourth, we combine the association knowledge base and fuzzy rules. Our proposed hybrid data mining mechanism can reflect both association rule-based logical inference and complicate fuzzy logic.

• Korean Journal of Human Ecology
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• v.20 no.1
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• pp.39-55
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• 2011

The purpose of this study was to investigate the implicit knowledge of creativity and education practice of creativity perceived by undergraduate students. Participants were 425 undergraduate students from around the greater metropolitan area of Seoul. The results of this study were as follows: (1) Most undergraduate students considered creativity as creative thinking or creative product rather than creative personality and creative environment. Undergraduate students placed originality as the most important subfactor of creativity. Scientists were ranked as the most creative people, followed by executives, and then artists. Interestingly contemporary Korean undergraduate students recognized and evaluated creativity as positive. (2) Most undergraduate students recognized the needs and importance of creativity-fostered education. These aspects of education have meaningful differences according to gender, as female students viewed creativity-fostered education more important. (3) Undergraduate students considered creative persons to be imaginative, independent, and confident. The most important part of developing undergraduate students' creativity was to make more creative environments. It has been suggested that the benefit of creative environments should be taken into consideration when developing creativity-enhancing programs and education for undergraduate students more generally.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.16 no.5
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• pp.205-212
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• 2016

Recently, researches for the integration and standardization of clinical data in the Western medicine and Korean medicine is in progress. If an integration of similar clinical data as well as heterogeneous clinical data is possible based on one standardization, we can able to derive implicit medical knowledge from integrated clinical data. In this paper, we implemented Korean clinical database based on internationally known CDISC standardization to efficiently store Korean clinical data and constructed E-CRF system for convenient data input in clinical sites. Furthermore, we showed example of an integration of Korean clinical data from 4 clinical sites. The results of our study will help to establish the foundation for the extraction of implicit medical knowledge from integrated clinical data. Also, our results may support efficient management through data integration, prevention of repetitive or unnecessary clinical trials, facilitation of collaborative study and convenient research through the distribution of refined clinical data.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.11
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• pp.307-316
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• 2021

This research aims to investigate the relationship between moral deficiency and moral consumption. Consumers' moral values cannot be separated from their consumption activities. In other words, consumers' spending preferences may be an expression of their beliefs about what is right and wrong. A less explored concept within moral consumption behavior theory is 'moral deficiency'. To the best of our knowledge, this is the first research effort to integrate green purchasing and religious purchasing under the banner of moral consumption behavior. There are two studies: Study 1 aimed to measure the moral deficiency of participants through moral scenarios (implicit) and then test its relationship with the green purchase and religious purchase, two proxies of moral consumption. A total of 121 universities were chosen via the nonprobability sampling method. To improve the results of the prior study, Study 2 aimed to measure the moral deficiency of participants through moral deficiency self-report (explicit) and then test its effects on green purchase and religious purchase. A total of 208 participants from the general public were recruited via the nonprobability sampling method. The findings of the two studies suggest that participants with high moral deficiency showed more intention to engage in moral consumption behavior.

• Journal of Science and Technology Studies
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• v.2 no.1 s.3
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• pp.157-189
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• 2002

Science and technology policy a lot of implicit usjustified assumptions. These assumption without being reflected may cause various social problems. In this paper, lit is shown that philosophy of science could make contribution to resolving these problems. In epistemological viewpoints, theory of science and technology policy has been analyzed. I argue that social kinds, social entities appeared in social science should be interpreted realisticaly. Realizing this realistic interpretation of social kinds, as one field of social sciences, theory of science and technology policy can deal with the causal relation among social entities and the causal influence of science and technology policy more objectively- scientific knowledge has two components. One belongs to coded knowledge and the other belong to tacit knowledge which cannot be coded. I analyze the content and characters of tacit knowledge appeling to Michael Polany. One of the important function of science and technology policy is to make tacit knowledge more fruitful. I argue that philosophy of science fit well this function. Finally I claim that philosophy of science can help science and technology policy to reduce the ethical problems caused by science and technology.

• Industrial Engineering and Management Systems
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• v.10 no.1
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• pp.43-53
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• 2011

Currently, knowledge within the field of software development is largely implicit and is not formally disseminated and shared. This means that there is little improvement and regeneration of processes, and knowledge gained from previous projects is not necessarily applied to new ones. In order to turn this situation around it is necessary to take an organized approach to sharing job-related information. For this study, the authors constructed "Amalab-Project Planning Navigation System, or A-PPNS", a system for organizing accumulated knowledge related to the field of software development. More specifically, A-PPNS is a business process monitoring system and consists of the following four elements: (i) Optimized estimate support subsystem, (ii) Schedule monitoring system, (iii) QCD optimization diagnostic system, and (iv) Strategic technology accumulation system. The effectiveness of this system has been demonstrated and verified at Company A, a system integration company.

• The Journal of Information Systems
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• v.17 no.3
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• pp.205-233
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• 2008

This study suggests the dynamic value chain model, that will be able to not only show changing processes to organization's significant capital by integrating an individual, implicit, and explicit knowledge which affect organizational decision making, but also distinguish the key driver for raising organizational competitive power because it makes possible to analyze sensitivity of performance along with decision making alternatives and policy changes from dynamic view by connecting knowledge management capability, knowledge management activity, and relations with organizational performance with specific strategic map. Recently, a lot of organizations show interest in measuring and evaluating their performance synthetically. In organizations taking knowledge management, they introduce effective value chain model like a dynamic balanced scorecard (DBSC), and therefore they can reflect their knowledge management condition as well as show their changes by checking performance of established vision and strategy periodically. Furthermore, they can ask for their inner members' understanding and participation by communicating with and inspiring their members with awareness that members are one of their group, present a base of benchmarking, and offer significant information for later decision making. The BSC has been a successful framework for measuring an organization's performance in various perspectives through translating an organization's vision and strategy into an interrelated set of key performance indicators and specific actions. The BSC, while having significant strengths over traditional performance measurement methods, however, has its own limitations, due to its static nature, such as overlooking two-way causation between performance indicators and neglecting the impact of delayed feedback flowing from the adoption of new strategies or policy changes. To overcome these limitations, this study employs SD, a methodology for understanding complex systems where dynamic feedback among the interrelated system components significantly impact on the system outcomes. The SD simulation model in the form of DBSC would serve as a useful strategic teaming tool for facilitating an organization's communication process through various scenario analyses as well as predicting the dynamic behavior pattern of their key performance measures over a future time frame. For the demonstration purpose, this study applied the DBSC model to Prototype of Korea manufacturing and service firm.

• Journal of KIISE:Databases
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• v.28 no.3
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• pp.301-314
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• 2001

Data mining refers to a set of techniques for discovering implicit and useful knowledge from large database. Many studies on data mining have been pursued and some of them have involved issues of temporal data mining for discovering knowledge from temporal database, such as sequential pattern, similar time sequence, cyclic and temporal association rules, etc. However, all of the works treat problems for discovering temporal pattern from data which are stamped with time points and do not consider problems for discovering knowledge from temporal interval data. For example, there are many examples of temporal interval data that it can discover useful knowledge from. These include patient histories, purchaser histories, web log, and so on. Allen introduces relationships between intervals and operators for reasoning about relations between intervals. We present a new data mining technique that can discover temporal relation rules in temporal interval data by using the Allen's theory. In this paper, we present two new algorithms for discovering algorithm for generating temporal relation rules, discovers rules from temporal interval data. This technique can discover more useful knowledge in compared with conventional data mining techniques.

• Journal of KIISE:Databases
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• v.32 no.6
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• pp.567-584
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• 2005

Temporal data mining, the incorporation of temporal semantics to existing data mining techniques, refers to a set of techniques for discovering implicit and useful temporal knowledge from temporal data. Association rules and classification are applied to various applications which are the typical data mining problems. However, these approaches do not consider temporal attribute and have been pursued for discovering knowledge from static data although a large proportion of data contains temporal dimension. Also, data mining researches from temporal data treat problems for discovering knowledge from data stamped with time point and adding time constraint. Therefore, these do not consider temporal semantics and temporal relationships containing data. This paper suggests that temporal associative classification technique based on temporal class association rules. This temporal classification applies rules discovered by temporal class association rules which extends existing associative classification by containing temporal dimension for generating temporal classification rules. Therefore, this technique can discover more useful knowledge in compared with typical classification techniques.