• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.227-237
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• 2022

With the growing interest in convergence, there have been various attempts to measure convergence, but the definition of convergence is ambiguous and consensus on appropriate indicators has not been reached, so measurement of convergence is still at a rudimentary stage. In this study, using the KSIC-IPC linkage table developed by the Korean Intellectual Property Office to analyze the correlation and impact of patents, industry, economy, and population, we propose a new evaluation model that can evaluate industry convergence from patent data. In addition, it was verified whether the industry convergence derived from this properly reflects the corporate convergence characteristics. As a result of classifying the convergence of 39,740 patents owned by global major automobile companies, and evaluating the degree of convergence of each company, it was confirmed that the industry convergence derived using the KSIC-IPC linkage table better reflects the corporate convergence characteristics than the technology convergence classified by IPC co-classification. Therefore, the industry convergence data of automotive sector derived from the new industry convergence evaluation model using the KSIC-IPC linkage table is expected to be widely used for future convergence research.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.221-241
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• 2018

As we enter the knowledge society, the importance of information as a new form of capital is being emphasized. The importance of information classification is also increasing for efficient management of digital information produced exponentially. In this study, we tried to automatically classify and provide tailored information that can help companies decide to make technology commercialization. Therefore, we propose a method to classify information based on Korea Standard Industry Classification (KSIC), which indicates the business characteristics of enterprises. The classification of information or documents has been largely based on machine learning, but there is not enough training data categorized on the basis of KSIC. Therefore, this study applied the method of calculating similarity between documents. Specifically, a method and a model for presenting the most appropriate KSIC code are proposed by collecting explanatory texts of each code of KSIC and calculating the similarity with the classification object document using the vector space model. The IPC data were collected and classified by KSIC. And then verified the methodology by comparing it with the KSIC-IPC concordance table provided by the Korean Intellectual Property Office. As a result of the verification, the highest agreement was obtained when the LT method, which is a kind of TF-IDF calculation formula, was applied. At this time, the degree of match of the first rank matching KSIC was 53% and the cumulative match of the fifth ranking was 76%. Through this, it can be confirmed that KSIC classification of technology, industry, and market information that SMEs need more quantitatively and objectively is possible. In addition, it is considered that the methods and results provided in this study can be used as a basic data to help the qualitative judgment of experts in creating a linkage table between heterogeneous classification systems.