Journal of Information Technology Services (한국IT서비스학회지)

Korea Society of IT Services (한국IT서비스학회)
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Technology-Focused Business Diversification Support Methodology Using Item Network

아이템 네트워크를 활용한 기술 중심 사업 다각화 기회 탐색 지원 방법론

  • 배국진 (한국과학기술정보연구원(KISTI) 기술사업화센터) ;
  • 김지은 (국민대학교 비즈니스IT전문대학원) ;
  • 김남규 (국민대학교 경영정보학부)
  • Received : 2020.05.08
  • Accepted : 2020.05.21
  • Published : 2020.06.30
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Abstract

Recently, various attempts have been made to discover promising items and technologies. However, there are very few data-driven approaches to support business diversification by companies with specific technologies. Therefore, there is a need for a methodology that can detect items related to a specific technology and recommend highly marketable items among them as business diversification targets. In this paper, we devise Labeled Item Network for Business Diversification Consulting Support System. Our research is performed with three sub-studies. In Sub-study 1, we find the proper source documents to build the item network and construct item dictionary. In Sub-study 2, we derive the Labeled Item Network and devise four index for item evaluation. Finally, we introduce the application scenario of our methodology and describe the result of real-case analysis in Sub-study 3. The Labeled Item Network, one of the main outcome of this study, can identify the relationships between items as well as the meaning of the relationship. We expect that more specific business item diversification opportunities can be found with the Labeled Item Network. The proposed methodology can help many SMEs diversify their business on the basis of their technology.

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References

  1. 곽기영, "소셜 네트워크 분석", 서울, 도서출판 청람, 2017.
  2. 노한성, "특허 네트워크 분석을 통한 자동차 산업의 기술융합과 유망기술에 대한 연구 : 토요타 사례", 미래학회 추계학술대회 자료집, 2018, 267-275.
  3. 이용호, 권오진, 고병열, "기업 보유역량 기반의 잠재유망 기술-제품 포트폴리오 도출에 관한 연구", 기술혁신학회지, 제14권, 2011, 1187-1208.
  4. 이우형, 석영철 박준철, "소셜 네트워크 분석을 통한 유망기술 탐색에 관한 연구 : 차세대 이동통신분야를 중심으로", 정보시스템연구, 제21권, 제4호, 2012, 109-132.
  5. 주성현, 하성호, 박상현, "기술 키워드 네트워크와 인지지도 분석을 통한 무인항공기 비행체산업의 유망기술 도출 연구", 한국산업정보학회논문지, 제21권, 제5호, 2018, 55-72. https://doi.org/10.9723/jksiis.2016.21.5.055
  6. 중소벤처기업부, "중소기업 기술로드맵 2018-2020", 중소벤처기업부, 2018.
  7. 최호창, 곽기영, 김남규, "기술 성숙도 및 의존도의 네트워크 분석을 통한 유망 융합 기술 발굴 방법론", 지능정보연구, 제24권, 제1호, 2018, 101-124. https://doi.org/10.13088/jiis.2018.24.1.101
  8. Alexandre, K., I. Titov, and B. Bhattarai, "Inducing Crosslingual Distributed Representations of Words", Proceedings of the 24th International Conference on Computational Linguistics, 2012, 1459-1474.
  9. Borgatti, S.P., M.G. Everett, and J.C. Johnson, "Analyzing Social Networks", SAGE Publications Ltd., 2013.
  10. Eric, H.H., R. Socher, C.D. Manning, and A.Y. Ng, "Improving Word Representations via Global Context and Multiple Word Prototypes", Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics : Long Papers, Vol.1, 2012, 873-882.
  11. Guy, L., Y. Mao, and J. Dillon, "The Locally Weighted Bag of Words Framework for Document Representation", Journal of Machine Learning Research, Vol.8, No.10, 2007, 2405-2441.
  12. Jacob, D., M. Chang, K. Lee, and K. Toutanova, "BERT : Pre-training of Deep Bidirectional Transformers for Language Understanding", Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics : Human Language Technologies, 2019, 4171-4186.
  13. Jin, W., P. Liu, M.F.H. She, S. Nahavandi, and A. Kouzani, "Bag-of-words Representation for Biomedical Time Series Classification", Biomedical Signal Processing and Control, Vol.8, No.6, 2013, 634-644. https://doi.org/10.1016/j.bspc.2013.06.004
  14. John, B., K.Q. Weinberger, L.K. Saul, and F.C.N. Pereira, "Hierarchical Distributed Representations for Statistical Language Modeling", Proceedings of the 17th International Conference on Neural Information Processing Systems, 2004, 185-192.
  15. Joseph, T., L. Ratinov, and Y. Bengio, "Word Representations : A Simple and General Method for Semi-supervised Learning", Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, 2010, 384-394.
  16. Lei, W., S.C.H. Hoi, and N. Yu, "Semanticspreserving Bag-of-Words Models and Applications", IEEE Transactions on Image Processing, Vol.19, No.7, 2010, 1908-1920. https://doi.org/10.1109/TIP.2010.2045169
  17. Marcos, Z., "Using Bag-of-Words to Distinguish Similar Languages : How Efficient Are They", Proceedings of the 2013 IEEE 14th International Symposium on Computational Intelligence and Informatics, 2013, 37-41.
  18. Matthew, P.E., M. Neumann, M. Iyyer, M. Gardner, C. Clark, K. Lee, and L. Zettlemoyer, "Deep Contextualized Word Representations", Proceedings of the North American Chapter of the Association for Computational Linguistics, 2018, 2227-2237.
  19. Mikolov, T., K. Chen, G. Corrado, and J. Dean, "Efficient Estimation of Word Representation in Vector Space", arXiv Preprint : arXiv : 1301.3781, 2013.
  20. Quoc, L. and T. Mikolov, "Distributed Representations of Sentences and Documents", Proceedings of the 31st International Conference on Machine Learning, 2014, 1188-1196.
  21. Stephan, G., Y. Bengio, and G. Corrado, "Bil-BOWA : Fast Bilingual Distributed Representations without Word Alignments", Proceedings of the 32nd International Conference on Machine Learning, 2015, 748-756.