• Journal of Korean Library and Information Science Society
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• v.46 no.2
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• pp.257-287
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• 2015

This study proposes a methodology for using facet analysis in ontology modeling and investigates how facet analysis could be utilized in ontology design processes. We applied facet analysis methods developed by Ranganathan and CRG (Classification Research Group) to modeling Smartphone ontology. Utilizing the guiding principles of Ranganathan and CRG, main facets, such as Type, Spec, Function, Operation, Product, Person, Agent, Space and Time, are derived for Smartphone ontology. It is hoped that the methods and procedures employed in identifying and categorizing facets and ontology in this study will provide insights into designing future ontologies.

• Journal of Korea Society of Industrial Information Systems
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• v.29 no.1
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• pp.135-144
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• 2024

In this study, we propose an automatic classification model for quantitative multidimensional analysis based on facet theory to understand public opinions and demands on major issues through big data analysis. Civil complaints, as a form of public feedback, are generated by various individuals on multiple topics repeatedly and continuously in real-time, which can be challenging for officials to read and analyze efficiently. Specifically, our research introduces a new classification framework that utilizes facet theory and political analysis models to analyze the characteristics of citizen complaints and apply them to the policy-making process. Furthermore, to reduce administrative tasks related to complaint analysis and processing and to facilitate citizen policy participation, we employ deep learning to automatically extract and classify attributes based on the facet analysis framework. The results of this study are expected to provide important insights into understanding and analyzing the characteristics of big data related to citizen complaints, which can pave the way for future research in various fields beyond the public sector, such as education, industry, and healthcare, for quantifying unstructured data and utilizing multidimensional analysis. In practical terms, improving the processing system for large-scale electronic complaints and automation through deep learning can enhance the efficiency and responsiveness of complaint handling, and this approach can also be applied to text data processing in other fields.

• Journal of Korean Society of Archives and Records Management
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• v.19 no.4
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• pp.63-86
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• 2019

UNESCO clearly has a good user interface compared to other sites. However, it does not have a structure in which user-centric knowledge curating is employed by users. As such, the knowledge structure should be expressed differently in advance for users to enjoy such benefits. At present, almost all current information systems are lacking with semantic and contextual information. Moreover, these systems are deemed insufficient of interlinking various kinds of thoughts in our minds. Thus, it is necessary to model in advance what users are likely to think and provide an interface that they can easily utilize based on that modeling. Furthermore, there is a need for a new structural theory based on semantic technology that can make that possible. Therefore, in this proposal, theoretical and practical insights were presented for user interface implementation to which relational facet navigation based on the structural theory is applied. Moreover, this proposal intends to suggest a "thinking expansion platform" that allows users' ideation of different concepts, including those unfamiliar to them.