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Recommendation system for technology convergence opportunities based on self-supervised representation learning

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Abstract

We show how a deep neural network can be designed to learn meaningful representations from high-dimensional and heterogeneous categorical features in patent data using self-supervised learning. Based on each firm’s technology portfolio and each patent’s co-classification information, we propose a novel recommendation system for firms seeking new convergence opportunities through representations of convergence items and firms. The results of this work are expected to recommend convergence opportunities in multiple technology fields by considering the target firm’s potential preference. First, we create a technology portfolio consisting of a set of patents owned by each firm. Then, we train a neural network to extract latent representations of firms and technology convergence items. Despite a lack of indicators related to a firm’s latent preference for a convergence item, a self-supervised neural network can capture the similarity with semantic information of firm’s latent preference that is implicitly present in patent’s co-classification information in each firm’s technology portfolio. We then calculate the similarity between the vector of a target firm and convergence items for recommendation. The top N similar convergence items that have the highest scores are recommended as the new convergence items for the target firm. We apply our framework to the dataset of patents granted by the United States Patent and Trademark Office between 2011 and 2015. The results indicate that the recent development in theories and empirical studies of deep representation learning can shed new light on extracting valuable information from the structured part of patent data.

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References

  • Achille, A., & Soatto, S. (2018). Emergence of invariance and disentanglement in deep representations. The Journal of Machine Learning Research, 19(1), 1947–1980.

    MathSciNet  MATH  Google Scholar 

  • Allarakhia, M., & Walsh, S. (2012). Analyzing and organizing nanotechnology development: Application of the institutional analysis development framework to nanotechnology consortia. Technovation, 32(3–4), 216–226.

    Google Scholar 

  • An, J., Kim, K., Mortara, L., & Lee, S. (2018). Deriving technology intelligence from patents: Preposition-based semantic analysis. Journal of Informetrics, 12(1), 217–236.

    Google Scholar 

  • Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., & Saunshi N. (2019). A theoretical analysis of contrastive unsupervised representation learning. In International Conference on Machine Learning, (pp. 5628–5637).

  • Bengio, Y. (2009). Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1), 1–127.

    MathSciNet  MATH  Google Scholar 

  • Bengio, Y., Lamblin, P., Popovici, D., & Larochelle, H. (2007). Greedy layer-wise training of deep networks. In Advances in neural information processing systems (pp. 153–160).

  • Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798–1828.

    Google Scholar 

  • Bigliardi, B., Ivo Dormio, A., & Galati, F. (2012). The adoption of open innovation within the telecommunication industry. European Journal of Innovation Management, 15(1), 27–54.

    Google Scholar 

  • Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research policy, 32(1), 69–87.

    Google Scholar 

  • Caviggioli, F. (2016). Technology fusion: Identification and analysis of the drivers of technology convergence using patent data. Technovation, 55, 22–32.

    Google Scholar 

  • Cerda, P., Varoquaux, G., & Kégl, B. (2018). Similarity encoding for learning with dirty categorical variables. Machine Learning, 107(8–10), 1477–1494.

    MathSciNet  MATH  Google Scholar 

  • Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020). A simple framework for contrastive learning of visual representations. arXiv preprint arXiv:2002.05709.

  • Cheng, H. T., Koc, L., Harmsen, J., Shaked, T., Chandra, T., Aradhye, H., & Anil, R. (2016, September). Wide & deep learning for recommender systems. In Proceedings of the 1st workshop on deep learning for recommender systems, (pp. 7–10).

  • Cho, C., Yoon, B., Coh, B. Y., & Lee, S. (2016). An empirical analysis on purposes, drivers and activities of technology opportunity discovery: The case of Korean SMEs in the manufacturing sector. R&D Management, 46(1), 13–35.

    Google Scholar 

  • Choi, C., Kim, S., & Park, Y. (2007). A patent-based cross impact analysis for quantitative estimation of technological impact: The case of information and communication technology. Technological Forecasting and Social Change, 74(8), 1296–1314.

    Google Scholar 

  • Choromanska, A., Henaff, M., Mathieu, M., Arous, G. B., & LeCun, Y. (2015, February). The loss surfaces of multilayer networks. In Artificial intelligence and statistics, (pp. 192–204).

  • Chung, P., & Sohn, S. Y. (2020). Early detection of valuable patents using a deep learning model: Case of semiconductor industry. Technological Forecasting and Social Change, 158, 120146.

    Google Scholar 

  • Covington, P., Adams, J., & Sargin, E. (2016, September). Deep neural networks for Youtube recommendations. In Proceedings of the 10th ACM conference on recommender systems (pp. 191–198).

  • Curran, C. S., & Leker, J. (2011). Patent indicators for monitoring convergence–examples from NFF and ICT. Technological Forecasting and Social Change, 78(2), 256–273.

    Google Scholar 

  • Dai, A. M., Olah, C., & Le, Q. V. (2015). Document embedding with paragraph vectors. arXiv preprint arXiv:1507.07998.

  • Dean, J., Corrado, G., Monga, R., Chen, K., Devin, M., Mao, M., & Le, Q. V. (2012). Large scale distributed deep networks. In Advances in neural information processing systems (pp. 1223–1231).

  • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

  • Dosovitskiy, A., Fischer, P., Springenberg, J. T., Riedmiller, M., & Brox, T. (2015). Discriminative unsupervised feature learning with exemplar convolutional neural networks. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(9), 1734–1747.

    Google Scholar 

  • Gambardella, A., & Torrisi, S. (1998). Does technology convergence imply convergence in markets? Evidence from the electronics industry. Research Policy, 27(5), 445–463.

    Google Scholar 

  • Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., et al. (2016). Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1), 2096–2030.

    MathSciNet  MATH  Google Scholar 

  • Gidaris, S., Singh, P., & Komodakis, N. (2018). Unsupervised representation learning by predicting image rotations. arXiv preprint arXiv:1803.07728.

  • Glorot, X., Bordes, A., & Bengio, Y. (2011, January). Domain adaptation for large-scale sentiment classification: A deep learning approach. In ICML.

  • Goodfellow, I., Lee, H., Le, Q. V., Saxe, A., & Ng, A. Y. (2009). Measuring invariances in deep networks. In Advances in neural information processing systems (pp. 646–654).

  • Goyal, P., Mahajan, D., Gupta, A., & Misra, I. (2019). Scaling and benchmarking self-supervised visual representation learning. In Proceedings of the IEEE International Conference on Computer Vision (pp. 6391–6400).

  • Guo, C., & Berkhahn, F. (2016). Entity embeddings of categorical variables. arXiv preprint arXiv:1604.06737.

  • Hacklin, F., Raurich, V., & Marxt, C. (2004, October). How incremental innovation becomes disruptive: The case of technology convergence. In Engineering Management Conference, Proceedings. 2004 IEEE International (Vol. 1, pp. 32–36).

  • Han, E. J., & Sohn, S. Y. (2016). Technology convergence in standards for information and communication technologies. Technological forecasting and social change, 106, 1–10.

    Google Scholar 

  • He, J., Zhuo, H. H., & Law, J. (2017). Distributed-representation based hybrid recommender system with short item descriptions. arXiv preprint arXiv:1703.04854.

  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770–778).

  • He, X., Liao, L., Zhang, H., Nie, L., Hu, X., & Chua, T. S. (2017). Neural collaborative filtering. In Proceedings of the 26th international conference on world wide web (pp. 173–182).

  • Hinton, G. E. (1989). Deterministic Boltzmann learning performs steepest descent in weight-space. Neural computation, 1(1), 143–150.

    Google Scholar 

  • Hinton, G. E., & Salakhutdinov, R. R. (2006). Reducing the dimensionality of data with neural networks. Science, 313(5786), 504–507.

    MathSciNet  MATH  Google Scholar 

  • Hsieh, C. K., Yang, L., Cui, Y., Lin, T. Y., Belongie, S., & Estrin, D. (2017, April). Collaborative metric learning. In Proceedings of the 26th international conference on world wide web (pp. 193–201).

  • Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700–4708).

  • Hwang, I. (2020). The effect of collaborative innovation on ICT-based technology convergence: A patent-based analysis. PLoS ONE, 15(2), e0228616.

    Google Scholar 

  • Jaffe, A. B. (1986). Technological opportunity and spillovers of R&D: evidence from firms' patents, profits and market value (No. w1815). National Bureau of Economic Research.

  • Jeong, S., & Lee, S. (2015). What drives technology convergence? Exploring the influence of technological and resource allocation contexts. Journal of Engineering and Technology Management, 36, 78–96.

    Google Scholar 

  • Jin, J. H., Park, S. C., & Pyon, C. U. (2011). Finding research trend of convergence technology based on Korean R&D network. Expert Systems with Applications, 38(12), 15159–15171.

    Google Scholar 

  • Jing, L., & Tian, Y. (2020). Self-supervised visual feature learning with deep neural networks: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence.

  • Karvonen, M., & Kässi, T. (2013). Patent citations as a tool for analysing the early stages of convergence. Technological Forecasting and Social Change, 80(6), 1094–1107.

    Google Scholar 

  • Kim, B., Gazzola, G., Lee, J. M., Kim, D., Kim, K., & Jeong, M. K. (2014). Inter-cluster connectivity analysis for technology opportunity discovery. Scientometrics, 98(3), 1811–1825.

    Google Scholar 

  • Kim, M. S., & Kim, C. (2012). On a patent analysis method for technology convergence. Procedia-Social and Behavioral Sciences, 40, 657–663.

    Google Scholar 

  • Kim, D. H., Lee, H., & Kwak, J. (2017). Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network. Research Policy, 46(7), 1234–1254.

    Google Scholar 

  • Kim, J., Kim, S., & Lee, C. (2019). Anticipating technology convergence: Link prediction using Wikipedia hyperlinks. Technovation, 79, 25–34.

    Google Scholar 

  • Kim, K., Jung, S., & Hwang, J. (2019). Technology convergence capability and firm innovation in the manufacturing sector: An approach based on patent network analysis. R&D Management, 49(4), 595–606.

    Google Scholar 

  • Kim, N., Lee, H., Kim, W., Lee, H., & Suh, J. H. (2015). Dynamic patterns of industry convergence: Evidence from a large amount of unstructured data. Research Policy, 44(9), 1734–1748.

    Google Scholar 

  • Kim, T. S., & Sohn, S. Y. (2020). (2020) Machine-learning-based deep semantic analysis approach for forecasting new technology convergence. Technological Forecasting & Social Change, 157, 1–10.

    Google Scholar 

  • Klevorick, A. K., Levin, R. C., Nelson, R. R., & Winter, S. G. (1995). On the sources and significance of interindustry differences in technological opportunities. Research Policy, 24(2), 185–205.

    Google Scholar 

  • Ko, N., Yoon, J., & Seo, W. (2014). Analyzing interdisciplinarity of technology fusion using knowledge flows of patents. Expert Systems with Applications, 41(4), 1955–1963.

    Google Scholar 

  • Kodama, F. (1986). Japanese innovation in mechatronics technology. Science and Public Policy, 13(1), 44–51.

    Google Scholar 

  • Kolesnikov, A., Zhai, X., & Beyer, L. (2019). Revisiting self-supervised visual representation learning. In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (pp. 1920–1929).

  • Kose, T., & Sakata, I. (2019). Identifying technology convergence in the field of robotics research. Technological Forecasting and Social Change, 146, 751–766.

    Google Scholar 

  • Kwon, Y. I., & Jeong, D. H. (2014). Technology relevance analysis between wind power energy-fuel cell-green car using network analysis, IPC map. Collnet Journal of Scientometrics and Information Management, 8(1), 109–121.

    Google Scholar 

  • Le, Q. V., Marc’Aurelio Ranzato, R. M., Devin, M., Chen, K., Corrado, G. S., Dean, J., & Ng, A. Y. (2012). Building high-level features using large scale unsupervised learning'. In International Conference on Machine Learning.

  • Le, Q., & Mikolov, T. (2014, January). Distributed representations of sentences and documents. In International Conference on Machine Learning (pp. 1188–1196).

  • LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.

    Google Scholar 

  • Lee, C., Kogler, D. F., & Lee, D. (2019). Capturing information on technology convergence, international collaboration, and knowledge flow from patent documents: A case of information and communication technology. Information Processing & Management, 56(4), 1576–1591.

    Google Scholar 

  • Lee, H., Ekanadham, C., & Ng, A. Y. (2008). Sparse deep belief net model for visual area V2. In Advances in neural information processing systems (pp. 873–880).

  • Lee, H., Ge, R., Ma, T., Risteski, A., & Arora, S. (2017). On the ability of neural nets to express distributions. arXiv preprint arXiv:1702.07028.

  • Lee, J., & Sohn, S. Y. (2017). What makes the first forward citation of a patent occur earlier? Scientometrics, 113(1), 279–298.

    Google Scholar 

  • Lee, M., & Lee, S. (2017). Identifying new business opportunities from competitor intelligence: An integrated use of patent and trademark databases. Technological Forecasting and Social Change, 119, 170–183.

    Google Scholar 

  • Lee, W. S., Han, E. J., & Sohn, S. Y. (2015). Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents. Technological Forecasting and Social Change, 100, 317–329.

    Google Scholar 

  • Lee, Y., Kim, S. Y., Song, I., Park, Y., & Shin, J. (2014). Technology opportunity identification customized to the technological capability of SMEs through two-stage patent analysis. Scientometrics, 100(1), 227–244.

    Google Scholar 

  • Liu, X., Zhang, F., Hou, Z., Wang, Z., Mian, L., Zhang, J., & Tang, J. (2020). Self-supervised Learning: Generative or Contrastive. arXiv preprint arXiv:2006.08218.

  • Martin, B. R., Nightingale, P., & Yegros-Yegros, A. (2012). Science and technology studies: Exploring the knowledge base. Research Policy, 41(7), 1182–1204.

    Google Scholar 

  • Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013). Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781.

  • Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems (pp. 3111–3119).

  • Montúfar, G. F., & Morton, J. (2015). When does a mixture of products contain a product of mixtures? SIAM Journal on Discrete Mathematics, 29(1), 321–347.

    MathSciNet  MATH  Google Scholar 

  • Montúfar, G. F., Pascanu, R., Cho, K., & Bengio, Y. (2014). On the number of linear regions of deep neural networks. In Advances in neural information processing systems (pp. 2924–2932).

  • Nemet, G. F., & Johnson, E. (2012). Do important inventions benefit from knowledge originating in other technological domains? Research Policy, 41(1), 190–200.

    Google Scholar 

  • Noroozi, M., Vinjimoor, A., Favaro, P., & Pirsiavash, H. (2018). Boosting self-supervised learning via knowledge transfer. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 9359–9367).

  • Olsson, O. (2005). Technological opportunity and growth. Journal of Economic Growth, 10(1), 31–53.

    MATH  Google Scholar 

  • Oord, A. V. D., Li, Y., & Vinyals, O. (2018). Representation learning with contrastive predictive coding. arXiv preprint arXiv:1807.03748.

  • Park, C., Lee, J., & Sohn, S. Y. (2019). Recommendation of feeder bus routes using neural network embedding-based optimization. Transportation Research Part A: Policy and Practice, 126, 329–341.

    Google Scholar 

  • Park, I., & Yoon, B. (2018). Technological opportunity discovery for technology convergence based on the prediction of technology knowledge flow in a citation network. Journal of Informetrics, 12(4), 1199–1222.

    Google Scholar 

  • Park, Y., & Yoon, J. (2017). Application technology opportunity discovery from technology portfolios: Use of patent classification and collaborative filtering. Technological Forecasting and Social Change, 118, 170–183.

    Google Scholar 

  • Ren, S., He, K., Girshick, R., & Sun, J. (2015). Faster r-cnn: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems (pp. 91–99).

  • Ridder, H. G. (2017). The theory contribution of case study research designs. Business Research, 10(2), 281–305.

    Google Scholar 

  • Riesenhuber, M., & Poggio, T. (1999). Hierarchical models of object recognition in cortex. Nature Neuroscience, 2(11), 1019–1025.

    Google Scholar 

  • Rosenberg, N. (1963). Technological change in the machine tool industry 1840-1910. Journal of Economic History. https://doi.org/10.1017/S0022050700109155.

    Article  Google Scholar 

  • Schoen, A., Villard, L., Laurens, P., Cointet, J. P., Heimeriks, G., & Alkemade, F. (2012, September). The network structure of technological developments; Technological distance as a walk on the technology map. In Science & Technology Indicators (STI) Conference.

  • Schumpeter, J. (1934). The theory of economic development. Cambridge, MA: Harvard University Press.

  • Seo, W., Yoon, J., Park, H., Coh, B. Y., Lee, J. M., & Kwon, O. J. (2016). Product opportunity identification based on internal capabilities using text mining and association rule mining. Technological Forecasting and Social Change, 105, 94–104.

    Google Scholar 

  • Shin, D. H. (2005). Technology convergence and regulatory challenge: A case from Korean digital media broadcasting. Info, 7(3), 47–58.

    Google Scholar 

  • Song, C. H., Elvers, D., & Leker, J. (2017). Anticipation of converging technology areas—A refined approach for the identification of attractive fields of innovation. Technological Forecasting and Social Change, 116, 98–115.

    Google Scholar 

  • Song, G., Huang, X., Cao, G., Liu, W., Zhang, J., & Yang, L. (2019, May). Enhanced deep feature representation for patent image classification. In Tenth International Conference on Graphics and Image Processing (ICGIP 2018) (Vol. 11069, p. 110690P). International Society for Optics and Photonics.

  • Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. In Advances in neural information processing systems (pp. 3104–3112).

  • Tan, M., & Le, Q. (2019, May). EfficientNet: Rethinking model scaling for convolutional neural networks. In International Conference on Machine Learning (pp. 6105–6114).

  • Tang, Y., Lou, X., Chen, Z., & Zhang, C. (2020). A Study on dynamic patterns of technology convergence with IPC co-occurrence-based analysis: The case of 3D printing. Sustainability, 12(7), 2655.

    Google Scholar 

  • Trappey, A. J., Trappey, C. V., Govindarajan, U. H., & Sun, J. J. (2019). Patent value analysis using deep learning models—the case of iot technology mining for the manufacturing industry. IEEE Transactions on Engineering Management.

  • Tsai, Y. H. H., Wu, Y., Salakhutdinov, R., & Morency, L. P. (2020). Demystifying self-supervised learning: An information-theoretical framework. arXiv preprint arXiv:2006.05576.

  • van Zeebroeck, N., & van de la Pottelsberghe Potterie, B. (2011). The vulnerability of patent value determinants. Economics of innovation and new technology, 20(3), 283–308.

    Google Scholar 

  • Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. In Advances in neural information processing systems (pp. 5998–6008).

  • Wang, P., Li, S., & Pan, R. (2018, April). Incorporating GAN for negative sampling in knowledge representation learning. In Thirty-Second AAAI Conference on Artificial Intelligence.

  • Xia, B., Baoan, L. I., & Lv, X. (2016, November). Research on patent document classification based on deep learning. In 2016 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE 2016). Atlantis Press.

  • Yang, Y., Akers, L., Klose, T., & Yang, C. B. (2008). Text mining and visualization tools–impressions of emerging capabilities. World Patent Information, 30(4), 280–293.

    Google Scholar 

  • Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R. R., & Le, Q. V. (2019). Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in neural information processing systems (pp. 5753–5763).

  • Yasunaga, Y., Watanabe, M., & Korenaga, M. (2009). Application of technology roadmaps to governmental innovation policy for promoting technology convergence. Technological Forecasting and Social Change, 76(1), 61–79.

    Google Scholar 

  • Yoon, J., & Kim, K. (2011). Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics, 88(1), 213–228.

    Google Scholar 

  • Yoon, J., Park, H., & Kim, K. (2013). Identifying technological competition trends for R&D planning using dynamic patent maps: SAO-based content analysis. Scientometrics, 94(1), 313–331.

    Google Scholar 

  • Yoon, J., Park, H., Seo, W., Lee, J. M., Coh, B. Y., & Kim, J. (2015). Technology opportunity discovery (TOD) from existing technologies and products: A function-based TOD framework. Technological Forecasting and Social Change, 100, 153–167.

    Google Scholar 

  • Zhai, X., Oliver, A., Kolesnikov, A., & Beyer, L. (2019). S4l: Self-supervised semi-supervised learning. In Proceedings of the IEEE international conference on computer vision (pp. 1476–1485).

  • Zhang, S., Yao, L., Sun, A., & Tay, Y. (2019). Deep learning-based recommender system: A survey and new perspectives. ACM Computing Surveys (CSUR), 52(1), 1–38.

    Google Scholar 

  • Zhang, Y., Shang, L., Huang, L., Porter, A. L., Zhang, G., Lu, J., et al. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Infometrics, 10(4), 1108–1130.

    Google Scholar 

  • Zhong, G., Wang, L. N., Ling, X., & Dong, J. (2016). An overview on data representation learning: From traditional feature learning to recent deep learning. The Journal of Finance and Data Science, 2(4), 265–278.

    Google Scholar 

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Acknowledgement

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (2016R1A2A1A05005270) and (MSIT) (2020R1A2C2005026). Jungpyo Lee is currently working as an AI Research Engineer in Mobigen, Korea.

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  1. Department of Information and Industrial Engineering, Yonsei University, 134 Shinchon-dong, Seoul, 120-749, Republic of Korea

    Jungpyo Lee & So Young Sohn

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Lee, J., Sohn, S.Y. Recommendation system for technology convergence opportunities based on self-supervised representation learning. Scientometrics 126, 1–25 (2021). https://doi.org/10.1007/s11192-020-03731-y

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