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• W4225149230 endingPage "121718" @default.
• W4225149230 startingPage "121718" @default.
• W4225149230 abstract "To capture emerging technologies in the fast-changing technology market, use of information concerning new technology-based firms (NTBFs) is strongly encouraged, in addition to the information about the technology itself. Especially, NTBFs rapidly respond to technological change, and their investment information is a significant criterion of technology valuation. Therefore, this study proposes a new technology opportunity discovery (TOD) framework that exploits text mining by deep learning and a knowledge graph (KG) by using three data sources: technology, NTBF, and investor data. First, a technology-classification model was developed using technical text data acquired using Doc2vec and logistic regression, and then this model assigned highly-relevant technology fields to NTBFs using NTBFs’ investor relation text data. Next, a KG that considers technology, NTBF, and NTBF’s investor was constructed to represent their relations for TOD by using the results of previous steps. Lastly, considering inter-connectivities of such factors, a TOD index that measures the potential of technologies was proposed. The accuracy and validity of the methods were demonstrated empirically, and an evaluation of emerging technologies identified by the analysis was provided. Our framework will be of great significance as a useful alternative to provide new insights for emerging technologies in the industry and market." @default.
• W4225149230 created "2022-05-01" @default.
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• W4225149230 date "2022-07-01" @default.
• W4225149230 modified "2023-09-30" @default.
• W4225149230 title "Technology Opportunity Discovery using Deep Learning-based Text Mining and a Knowledge Graph" @default.
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• W4225149230 doi "https://doi.org/10.1016/j.techfore.2022.121718" @default.
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