• Journal of the Korean Society for information Management
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• v.27 no.2
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• pp.75-94
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• 2010

In the current information environment, metadata interoperability has become the predominant way of organizing and managing resources. However, current approaches to metadata interoperability focus on the superficial mapping between labels of metadata elements without considering semantics of each element. This research applied facet analysis to address these difficulties in achieving metadata interoperability. By categorizing metadata elements according to these semantic and functional similarities, this research identified different types of facets: basic, conceptual, and relational. Through these different facets, a faceted framework was constructed to mediate semantic, syntactical, and structural differences across heterogeneous metadata standards.

• Journal of Korean Library and Information Science Society
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• v.41 no.3
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• pp.269-290
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• 2010

Applying one specific thesaurus might cause several problems because each thesaurus has its own characteristics inherited from its construction process. Therefore, integration of thesauri can be an appropriate approach to overcome the difficulties. This current research selected physics as a domain and two thesauri in the domain: PACS and PIRA. By integrating these two heterogeneous thesauri, this research could construct a conceptual structure that covers the whole concepts related to physics. By constructing the conceptual structure with the use of facet analysis from integrated thesaurus, it provides knowledge base with hierarchical structure and clear relationships between concepts. It can be an alternate approach to effective and efficient information retrieval and knowledge discovery.

• 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.