Abstract
Assessment of new research topics and emerging technologies in any branch of knowledge is important for researchers, universities and research institutes, research investors, industry sectors, and scientific policymakers for a variety of reasons. The basic premise of this research is that the topics of interest for academic research are those that are yet underdeveloped, but are relatively well sponsored by investors. This paper proposes a method to identify and evaluate topics for their research, industrial and commercial potential based on development, investment and investment-to-development ratio (investment appeal). Since the target audience of this paper is researchers in all fields of knowledge who are mostly unfamiliar with scientometric schemes, the proposed method is aimed to be simple, based on meta-databases with easy access, without any need to clustering on keywords. The development index is defined as the keyword link strength obtained from the keyword co-occurrence network, and investment is introduced as the number of sponsors associated with each keyword. From the qualitative analysis of the development-investment diagram, six sets of keywords, entitled as: for Research, for Industry, for Commerce, Matured, Academic and Chaotic, are identified. Due to uncertain membership of research topics to these sets and their relative overlapping, they are defined as fuzzy sets. A fuzzy model, called as Fuzzy Research Ranking System (FRRS), is designed to characterize the fuzzy behavior of research topics and their potential assessment, the output of which is the membership of keywords to any of the six predefined fuzzy sets. The proposed method has been implemented for a sample knowledge domain, Geo-Engineering, which is an interdisciplinary field with significant technological capacity. Expert review of the results shows that the method is relatively well qualified for its ability to identify research topics with technological and industrial perspectives from purely scientific keywords, and may efficiently introduce a ranked list of research topics to the researchers.
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References
Bellacicco, A. (1976). Fyzzy classification. Synthese, 33(1), 273–281.
Burrough, P. A. (1989). Fuzzy mathematical methods for soil survey and land evaluation. Journal of Soil Science, 40, 477–492.
Campbell, R. S. (1983). Patent trends as a technological forecasting tool. World Patent Information, 5(3), 137–143.
Chen, G., & Xiao, L. (2016). Selecting publication keywords for domain analysis in bibliometrics: A comparison of three methods. Journal of Informetrics, 10(1), 212–223.
Committee on Geological and Geotechnical Engineering in the New Millennium. (2006). Opportunities for Research and Technological Innovation. Committee on Geological and Geotechnical Engineering National Research Council.
Cozzens, S., Gatchair, S., Kang, J., Kim, K., Lee, H., Ordonez, G., & Portor, A. (2010). Emerging technologies: Quantitative identification and measurement. Technology Analysis and Strategic Management, 22, 361–376.
Cui, Zh., & Guangming, Z. G. (2010). A novel medical image dynamic fuzzy classification model based on ridgelet transform. Journal of Software, 5(5), 458–465.
Daim, T. U., Rueda, G., Martin, H., & Gerdsri, P. (2006). Forecasting emerging technologies: Use of bibliometrics and patent analysis. Technological Forecasting and Social Change, 73, 981–1012.
Dutu, L. C., Mauris, G., & Bolon, P. (2018). A fast and accurate rule-base generation method for Mamdani fuzzy systems. IEEE Transactions on Fuzzy Systems, Institute of Electrical and Electronics Engineers, 2018, 715–733.
Eastman, J. R. (1999). Guide to GIS and image processing (Vol. 2). Clark Laboratories.
Freeman, L. C. (1979). Centrality in social networks conceptual clarification. Social Networks, 1(3), 215–239.
García, J. A., Rodriguez-Sánchez, R., Fdez-Valdivia, J., & Martinez-Baena, L. (2012). On first quartile journals which are not of highest impact. Scientometrics, 90, 925–943.
Glänzel, W. (2012). Bibliometric methods for detecting and analyzing emerging research topics. Profesional De La Informacion, 21(2), 194–201.
Gumma, M., Thenkabail, P., & Nelson, A. (2011). Mapping irrigated areas using MODIS 250 meter time-series data: A study on krishna river basin (India). Water, 3(1), 113–131.
Guo, D., Zhu, S. H., Wei, J. (2019). Research on Vehicle identification based on high resolution satellite remote sensing image. In 2019 International Conference on Intelligent Transportation, Big Data & Smart City (ICITBS).
Hasanzadeh, S., Fakhrahmad, S. M., & Taheri, M. (2021). A fuzzy approach to review-based recommendation: Design and optimization of a fuzzy classification scheme based on implicit features of textual reviews. Iranian Journal of Fuzzy Systems, 18(6), 83–99.
Hedge, S. (2003). Modeling land cover change: A fuzzy approach. International Institute for Geo-Information Science and Earth Observation.
Ho, J. C., Saw, E. C., Lu, L. Y. Y., & Liu, J. S. (2014). Technological barriers and research trends in fuel cell technologies: A citation network analysis. Technological Forecasting and Social Change, 82, 66–79.
Hu, K., Wu, H., Qi, K., Yu, J., Yang, S., Yu, T., Zheng, J., & Liu, B. (2017). A domain keyword analysis approach extending Term Frequency-Keyword Active Index with Google Word2Vec model. Scientometrics, 114, 1031–1068.
Islam, A., & Inkpen, D. (2008). Semantic text similarity using corpus-based word similarity and string similarity. ACM Transactions on Knowledge Discovery from Data, 2(2), 1–25.
Joung, J., & Kim, K. (2016). Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technological Forecasting and Social Change, 114, 281–292.
Kajikawa, Y., Yoshikawa, J., Takeda, Y., & Matsushima, K. (2008). Tracking emerging technologies in energy research: Toward a roadmap for sustainable energy. Technological Forecasting and Social Change, 75(6), 771–782.
Kavita, N., Siva Kumar, A. P., & Chidananda K. (2021). An extensible framework for sentiment analysis based on opinion disambiguation. In Proceedings—5th International Conference on Computing Methodologies and Communication, ICCMC (pp. 1108–1111).
Kim, B. J., Jeong, S., & Chung, J. B. (2021). Research trends in vulnerability studies from 2000 to 2019: Findings from a bibliometric analysis. International Journal of Disaster Risk Reduction, 56, 102141.
Lark, R. M., & Bolam, H. C. (1997). Uncertainty in prediction and interpretation of spatially variable data on soils. Geoderma, 77, 263–282.
Lee, C., Kang, B., & Shin, J. (2014). Novelty-focused patent mapping for technology opportunity analysis. Technological Forecasting and Social Change, 90, 355–365.
Li, J., Goerlandt, F., & Reniers, G. (2021). An overview of scientometric mapping for the safety science community: Methods, tools, and framework. Safety Science, 134, 105093.
Li, X., Zhao, Y., Chen, B., & Xue, J. (2009). Approach to dim and small target detection based on fuzzy classification. Guangxue Jingmi Gongcheng/Optics and Precision Engineering, 17(9), 2311–2320.
Lima, J., Soares, C., Silva, S., Fonseca, A., Pajehu, L., & Medauar, C. (2021). Fuzzy classification in mapping the nutritional status of coffea canephora. Communications in Soil Science and Plant Analysis., 52, 2304–2317.
Liu, H., Chen, H., Hong, R., Liu, H., & You, W. (2020). Mapping knowledge structure and research trends of emergency evacuation studies. Safety Science, 121, 348–361.
Liu, J., Chen, Y., & Chen, Y. (2021). Emergency and disaster management-crowd evacuation research. Journal of Industrial Information Integration, 21, 100191.
Ma, T., Porter, A. L., Guo, Y., Ready, J., Xu, C., & Gao, L. (2014). A technology opportunities analysis model: Applied to dye-sensitized solar cells for China. Technology Analysis and Strategic Management, 26(1), 87–104.
Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International Journal of Man-Machine Studies, 7(1), 1–13.
Meier, A., & Werro, N. (2007). A fuzzy classification model for online customers. Informatica, 31(2), 175–182.
Mercan, Ö., & Kılıç, V. (2020). Fuzzy classifier based colorimetric quantification using a smartphone. Advances in Intelligent Systems and Computing, 1197, 1276–1283.
Mikaeil, R., Bakhavar, E., Hosseini, Sh., & Jafarpour, A. (2022). Fuzzy classification of rock engineering indices using rock texture characteristics. Bulletin of Engineering Geology and the Environment, 81(8), 1–16.
Mikova, N., & Sokolova, A. (2014). Global technology trends monitoring: Theoretical frameworks and best practices. Foresight-Russia, 8, 64–83.
Miller, G. A. (1995). WordNet: A lexical database for English. Communications of the ACM, 38, 39–41.
Mokeddem, A. (2018). A fuzzy classification model for myocardial infarction risk assessment. Applied Intelligence, 48(5), 1233–12501.
Narin, F. (1994). Patent bibliometrics. Scientometrics, 30(1), 147–155.
Newman, M. E. (2008). The mathematics of networks. The New Palgrave Encyclopedia of Economics, 2, 1–12.
Nezar, I. S., Muntaser, A., & Rabah, N. (2022). A comparative study of multiband Mamdani fuzzy classification methods for west of Iraq satellite image. Bulletin of Electrical Engineering and Informatics, 11(3), 1624–1632.
Ogawa, T., & Kajikawa, Y. (2015). Assessing the industrial opportunity of academic research with patent relatedness: A case study on polymer electrolyte fuel cells. Technological Forecasting and Social Change, 90, 469–475.
Oh, N., & Lee, J. (2020). Changing landscape of emergency management research: A systematic review with bibliometric analysis. International Journal of Disaster Risk Reduction, 49, 101658.
Petrovich, E. (2020). Science Mapping, Encyclopedia of Knowledge Organization (ISKO). Retrieved March 2, 2021, from https://www.isko.org/cyclo/science_mapping
Pradeepthi K. V., & Kannan A. (2018). Detection of Botnet traffic by using Neuro-fuzzy based Intrusion Detection. 2018 10th International Conference on Advanced Computing, ICoAC 2018 (pp. 118–123).
Praveena, M. D. A., Christy, A., Helen, L. S., Jancy, S., & UshaNandini, D. A. (2020). Fuzzy based technique for pattern recognition & classification. International Conference on Mathematical Sciences (ICMS 2020). Journal of Physics: Conference Series, 1770, 012020.
Quoniam, L., Balme, F., Rostaing, H., Giraud, E., & Dou, J. M. (1998). Bibliometric law used for information retrieval. Scientometrics, 41, 83–91.
Rai, S., Chakraverty, S., & Tayal, D. K. (2017). Identifying Metaphors Using Fuzzy Conceptual Features. In S. Kaushik, D. Gupta, L. Kharb, & D. Chahal (Eds.), Information, Communication and Computing Technology. ICICCT 2017. Communications in Computer and Information Science. (Vol. 750). Springer.
Ross, T. J. (2017). Fuzzy logic with engineering applications (4th ed.). Wiley.
Salton, G., & Buckley, C. (1988). Term-weighting approaches in automatic text retrieval. Information Processing and Management, 24(5), 513–523.
Saraswat, M., & Chakraverty, S. (2017). Leveraging movie recommendation using fuzzy emotion features. Communications in Computer and Information Science, 799, 475–483.
Shen, Y. C., Wang, M., & Yang, Y. (2020). Discovering the potential opportunities of scientific advancement and technological innovation: A case study of smart health monitoring technology. Technological Forecasting and Social Change, 160, 120225.
Shibata, N., Kajikawa, Y., & Sakata, I. (2010). Extracting the commercialization gap between science and technology: Case study of a solar cell. Technological Forecasting and Social Change, 77, 1147–1155.
Shibata, N., Kajikawa, Y., Takeda, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scientific publications. Technovation, 28(11), 758–775.
Silvera-Tawil, D., Hussain, M. S., & Li, J. (2019). Emerging Technologies for Precision Health: An insight into sensing technologies for health and wellbeing. Smart Health, 15, 100100.
Small, H., Boyack, K., & Klavans, R. (2014). Identifying emerging topics in science and technology. Research Policy, 43, 1450–1467.
Sohrabi, B., Khalili, J. A., & Roodi, A. (2018). Discover the properties of emerging research areas using meta-synthesis method. Journal of Science and Technology Policy, 9(4), 15–30. in Persian.
Su, H. N., & Lee, P. C. (2010). Mapping knowledge structure by keyword co-occurrence: A first look at journal papers in technology foresight. Scientometrics, 85(1), 65–79.
Szłapczyński, R., & Niksa-Rynkiewicz, T. (2018). A Framework of a ship domain-based near-miss detection method using Mamdani neuro-fuzzy classification. Polish Maritime Research, 25(s1), 14–21.
Takagi, T., & Sugeno, M. (1985). Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 1, 116–132.
Vasilyeva I., & Lukin V. (2020). Methods for predicting multichannel images classification efficiency. In 2020 IEEE International Conference on Problems of Infocommunications Science and Technology, PIC S and T 2020: Proceedings 9468002 (pp. 101–106).
Wang, L. X., & Mendel, J. M. (1992). Generating fuzzy rules by learning from examples. IEEE Transactions on Systems, Man and Cybernetics, 22(6), 1414–1427.
Wang, M. Y., Fang, S. C., & Chang, Y. H. (2015). Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels. Technological Forecasting and Social Change, 92, 182–195.
Wang, Q. (2017). A Bibliometric model for identifying emerging research topics. Journal of the Association for Information Science and Technology, 69(2), 290–304.
Yoon, B., Park, I., & Coh, B. (2014). Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining. Technological Forecasting and Social Change, 86, 287–303.
Yoon, B., & Park, Y. (2005). A systematic approach for identifying technology opportunities: Keyword-based morphology analysis. Technological Forecasting and Social Change, 72(2), 145–160.
Zhao, R., & Wang, J. (2010). Visualizing the research on pervasive and ubiquitous computing. Scientometrics, 86, 593–612.
Zumstein, D., & Kaufmann, M. (2009). A fuzzy web analytics model for web mining, Proceedings of the IADIS European Conference on Data Mining 2009, ECDM'09 Part of the IADIS Multi Conference on Computer Science and Information Systems, MCCSIS 2009 (pp. 59–66).
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Masoumi, N., Khajavi, R. A fuzzy classifier for evaluation of research topics by using keyword co-occurrence network and sponsors information. Scientometrics 128, 1485–1512 (2023). https://doi.org/10.1007/s11192-022-04618-w
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DOI: https://doi.org/10.1007/s11192-022-04618-w