{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T07:40:59Z","timestamp":1777102859202,"version":"3.51.4"},"reference-count":59,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T00:00:00Z","timestamp":1774224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["72374103"],"award-info":[{"award-number":["72374103"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"China Society of Indexers","award":["CSI24C10"],"award-info":[{"award-number":["CSI24C10"]}]},{"name":"Jiangsu Provincial Federation of Philosophy and Social Sciences","award":["24SYC-023"],"award-info":[{"award-number":["24SYC-023"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Scientometrics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s11192-026-05578-1","type":"journal-article","created":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T16:38:18Z","timestamp":1774283898000},"page":"1999-2023","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Technology opportunity prediction based on SAO representation learning"],"prefix":"10.1007","volume":"131","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7581-1850","authenticated-orcid":false,"given":"Jinzhu","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xuan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialu","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingxia","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,3,23]]},"reference":[{"key":"5578_CR1","doi-asserted-by":"publisher","unstructured":"Bala\u017eevi\u0107, I., Allen, C., & Hospedales, T. M. (2019). Tucker: Tensor factorization for knowledge graph completion. arXiv preprint arXiv. https:\/\/doi.org\/10.48550\/arXiv.1901.09590","DOI":"10.48550\/arXiv.1901.09590"},{"key":"5578_CR2","volume-title":"Recent Developments in Acoustics","author":"S Bhatt","year":"2021","unstructured":"Bhatt, S., Jain, A., & Dev, A. (2021). Continuous Speech Recognition Technologies\u2014A Review. Recent Developments in Acoustics. Springer."},{"key":"5578_CR3","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., & Yakhnenko, O. (2013). Translating embeddings for modeling multi-relational data. Proceedings of the 26th International Conference on Neural Information Processing Systems, Lake Tahoe, Nevada."},{"key":"5578_CR4","doi-asserted-by":"publisher","DOI":"10.1007\/s11192-023-04812-4","author":"X Cao","year":"2023","unstructured":"Cao, X., Chen, X., Huang, L., Deng, L., Cai, Y., & Ren, H. (2023). Detecting technological recombination using semantic analysis and dynamic network analysis. Scientometrics. https:\/\/doi.org\/10.1007\/s11192-023-04812-4","journal-title":"Scientometrics"},{"key":"5578_CR5","volume-title":"Advanced Intelligent Computing Technology and Applications","author":"Y Chen","year":"2024","unstructured":"Chen, Y., Yin, Z., Tan, H., & Lin, X. (2024). Knowledge Completion Method Based on Relational Embedding with GNN. Advanced Intelligent Computing Technology and Applications. Springer."},{"issue":"10","key":"5578_CR6","doi-asserted-by":"publisher","first-page":"9129","DOI":"10.1016\/j.eswa.2012.02.041","volume":"39","author":"S Choi","year":"2012","unstructured":"Choi, S., Kang, D., Lim, J., & Kim, K. (2012a). A fact-oriented ontological approach to SAO-based function modeling of patents for implementing function-based technology database. Expert Systems with Applications, 39(10), 9129\u20139140. https:\/\/doi.org\/10.1016\/j.eswa.2012.02.041","journal-title":"Expert Systems with Applications"},{"issue":"13","key":"5578_CR7","doi-asserted-by":"publisher","first-page":"11443","DOI":"10.1016\/j.eswa.2012.04.014","volume":"39","author":"S Choi","year":"2012","unstructured":"Choi, S., Park, H., Kang, D., Lee, J. Y., & Kim, K. (2012b). An SAO-based text mining approach to building a technology tree for technology planning. Expert Systems with Applications, 39(13), 11443\u201311455. https:\/\/doi.org\/10.1016\/j.eswa.2012.04.014","journal-title":"Expert Systems with Applications"},{"issue":"3","key":"5578_CR8","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1007\/s11192-011-0420-z","volume":"88","author":"S Choi","year":"2011","unstructured":"Choi, S., Yoon, J., Kim, K., Lee, J. Y., & Kim, C.-H. (2011). SAO network analysis of patents for technology trends identification: A case study of polymer electrolyte membrane technology in proton exchange membrane fuel cells. Scientometrics, 88(3), 863\u2013883. https:\/\/doi.org\/10.1007\/s11192-011-0420-z","journal-title":"Scientometrics"},{"key":"5578_CR9","doi-asserted-by":"publisher","DOI":"10.1016\/j.wpi.2020.101964","volume":"61","author":"NS Clarke","year":"2020","unstructured":"Clarke, N. S., J\u00fcrgens, B., & Herrero-Solana, V. (2020). Blockchain patent landscaping: An expert based methodology and search query. World Patent Information, 61, Article 101964. https:\/\/doi.org\/10.1016\/j.wpi.2020.101964","journal-title":"World Patent Information"},{"key":"5578_CR10","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v32i1.11573","author":"T Dettmers","year":"2018","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018). Convolutional 2D knowledge graph embeddings. Proceedings of the AAAI Conference on Artificial Intelligence, (pp. 1811\u20131818). https:\/\/doi.org\/10.1609\/aaai.v32i1.11573","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"1","key":"5578_CR11","doi-asserted-by":"publisher","first-page":"136","DOI":"10.3390\/su12010136","volume":"12","author":"L Feng","year":"2020","unstructured":"Feng, L., Niu, Y., Liu, Z., Wang, J., & Zhang, K. (2020). Discovering technology opportunity by keyword-based patent analysis: A hybrid approach of morphology analysis and USIT. Sustainability, 12(1), 136. https:\/\/doi.org\/10.3390\/su12010136","journal-title":"Sustainability"},{"issue":"11","key":"5578_CR12","doi-asserted-by":"publisher","DOI":"10.3390\/ph15111357","volume":"15","author":"L Feng","year":"2022","unstructured":"Feng, L., Zhao, W., Wang, J., Lin, K.-Y., Guo, Y., & Zhang, L. (2022). Data-driven technology roadmaps to identify potential technology opportunities for hyperuricemia drugs. Pharmaceuticals, 15(11), Article 1357. https:\/\/doi.org\/10.3390\/ph15111357","journal-title":"Pharmaceuticals"},{"key":"5578_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104133","volume":"132","author":"Z Gao","year":"2022","unstructured":"Gao, Z., Ding, P., & Xu, R. (2022). KG-Predict: A knowledge graph computational framework for drug repurposing. Journal of Biomedical Informatics, 132, Article 104133. https:\/\/doi.org\/10.1016\/j.jbi.2022.104133","journal-title":"Journal of Biomedical Informatics"},{"issue":"9","key":"5578_CR14","doi-asserted-by":"publisher","first-page":"5459","DOI":"10.1007\/s11192-022-04280-2","volume":"127","author":"G Garechana","year":"2022","unstructured":"Garechana, G., R\u00edo-Belver, R., Zarrabeitia, E., & Alvarez-Meaza, I. (2022). TeknoAssistant\u202f: A domain specific tech mining approach for technical problem-solving support. Scientometrics, 127(9), 5459\u20135473. https:\/\/doi.org\/10.1007\/s11192-022-04280-2","journal-title":"Scientometrics"},{"issue":"5","key":"5578_CR15","doi-asserted-by":"publisher","first-page":"1288","DOI":"10.1109\/TEM.2019.2939175","volume":"68","author":"X Han","year":"2021","unstructured":"Han, X., Zhu, D., Wang, X., Li, J., & Qiao, Y. (2021). Technology opportunity analysis: Combining SAO networks and link prediction. IEEE Transactions on Engineering Management, 68(5), 1288\u20131298. https:\/\/doi.org\/10.1109\/TEM.2019.2939175","journal-title":"IEEE Transactions on Engineering Management"},{"issue":"4","key":"5578_CR16","doi-asserted-by":"publisher","first-page":"1199","DOI":"10.1016\/j.joi.2018.09.007","volume":"12","author":"BY Inchae Park","year":"2018","unstructured":"Inchae Park, B. Y. (2018). Technological opportunity discovery for technological convergence based on the prediction of technology knowledge flow in a citation network. Journal of Informetrics, 12(4), 1199\u20131222. https:\/\/doi.org\/10.1016\/j.joi.2018.09.007","journal-title":"Journal of Informetrics"},{"issue":"13","key":"5578_CR17","doi-asserted-by":"publisher","first-page":"5314","DOI":"10.1016\/j.eswa.2013.03.038","volume":"40","author":"JL Jaemin Cho","year":"2013","unstructured":"Jaemin Cho, J. L. (2013). Development of a new technology product evaluation model for assessing commercialization opportunities using Delphi method and fuzzy AHP approach. Expert Systems with Applications, 40(13), 5314\u20135330. https:\/\/doi.org\/10.1016\/j.eswa.2013.03.038","journal-title":"Expert Systems with Applications"},{"key":"5578_CR18","first-page":"687","volume":"1","author":"G Ji","year":"2015","unstructured":"Ji, G., He, S., Xu, L., Liu, K., & Zhao, J. (2015). Knowledge graph embedding via dynamic mapping matrix. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing, (pp. 687\u2013696).","journal-title":"Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing"},{"issue":"1","key":"5578_CR19","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/s11192-018-2962-9","volume":"118","author":"K Kim","year":"2019","unstructured":"Kim, K., Park, K., & Lee, S. (2019). Investigating technology opportunities: The use of SAOx analysis. Scientometrics, 118(1), 45\u201370. https:\/\/doi.org\/10.1007\/s11192-018-2962-9","journal-title":"Scientometrics"},{"key":"5578_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.compind.2020.103379","volume":"125","author":"S Kim","year":"2021","unstructured":"Kim, S., & Yoon, B. (2021). Patent infringement analysis using a text mining technique based on SAO structure. Computers in Industry, 125, Article 103379. https:\/\/doi.org\/10.1016\/j.compind.2020.103379","journal-title":"Computers in Industry"},{"key":"5578_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.120746","volume":"168","author":"J Lee","year":"2021","unstructured":"Lee, J., Ko, N., Yoon, J., & Son, C. (2021). An approach for discovering firm-specific technology opportunities: Application of link prediction to F-term networks. Technological Forecasting and Social Change, 168, Article 120746. https:\/\/doi.org\/10.1016\/j.techfore.2021.120746","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR22","doi-asserted-by":"publisher","first-page":"317","DOI":"10.1016\/j.techfore.2015.07.022","volume":"100","author":"WS Lee","year":"2015","unstructured":"Lee, W. S., H, 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\u2013329. https:\/\/doi.org\/10.1016\/j.techfore.2015.07.022","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR23","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2023.122353","volume":"189","author":"X Li","year":"2023","unstructured":"Li, X., Wu, Y., Cheng, H., Xie, Q., & Daim, T. (2023a). Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology. Technological Forecasting and Social Change, 189, Article 122353. https:\/\/doi.org\/10.1016\/j.techfore.2023.122353","journal-title":"Technological Forecasting and Social Change"},{"issue":"4","key":"5578_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2023.103348","volume":"60","author":"Z Li","year":"2023","unstructured":"Li, Z., Zhang, Q., Zhu, F., Li, D., Zheng, C., & Zhang, Y. (2023b). Knowledge graph representation learning with simplifying hierarchical feature propagation. Information Processing & Management, 60(4), Article 103348. https:\/\/doi.org\/10.1016\/j.ipm.2023.103348","journal-title":"Information Processing & Management"},{"key":"5578_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/act13100410","author":"H Lin","year":"2024","unstructured":"Lin, H., Bao, J., Hu, N., Zhao, Z., Bai, W., & Li, D. (2024). Knowledge graph completion for High-Speed Railway Turnout Switch Machine Maintenance Based on the Multi-Level KBGC Model. Actuators. https:\/\/doi.org\/10.3390\/act13100410","journal-title":"Actuators"},{"key":"5578_CR26","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v29i1.9491","author":"Y Lin","year":"2015","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., & Zhu, X. (2015). Learning entity and relation embeddings for knowledge graph completion. Proceedings of the AAAI Conference on Artificial Intelligence, (pp. 2181\u20132187). https:\/\/doi.org\/10.1609\/aaai.v29i1.9491","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"issue":"4","key":"5578_CR27","doi-asserted-by":"publisher","first-page":"1975","DOI":"10.1007\/s11192-024-04961-0","volume":"129","author":"P Liu","year":"2024","unstructured":"Liu, P., Zhou, W., Feng, L., Wang, J., Lin, K.-Y., Wu, X., & Zhang, D. (2024). Mapping and comparing the technology evolution paths of scientific papers and patents: An integrated approach for forecasting technology trends. Scientometrics, 129(4), 1975\u20132005. https:\/\/doi.org\/10.1007\/s11192-024-04961-0","journal-title":"Scientometrics"},{"key":"5578_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2023.122565","volume":"192","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Feng, J., & Uden, L. (2023a). From technology opportunities to ideas generation via cross-cutting patent analysis: Application of generative topographic mapping and link prediction. Technological Forecasting and Social Change, 192, Article 122565. https:\/\/doi.org\/10.1016\/j.techfore.2023.122565","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.technovation.2023.102872","volume":"128","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Feng, J., & Uden, L. (2023b). Technology opportunity analysis using hierarchical semantic networks and dual link prediction. Technovation, 128, Article 102872. https:\/\/doi.org\/10.1016\/j.technovation.2023.102872","journal-title":"Technovation"},{"key":"5578_CR30","doi-asserted-by":"publisher","first-page":"767","DOI":"10.1016\/j.techfore.2018.08.002","volume":"146","author":"J Ma","year":"2019","unstructured":"Ma, J., Abrams, N. F., Porter, A. L., Zhu, D., & Farrell, D. (2019). Identifying translational indicators and technology opportunities for nanomedical research using tech mining: The case of gold nanostructures. Technological Forecasting and Social Change, 146, 767\u2013775. https:\/\/doi.org\/10.1016\/j.techfore.2018.08.002","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2021.121159","volume":"173","author":"T Ma","year":"2021","unstructured":"Ma, T., Zhou, X., Liu, J., Lou, Z., Hua, Z., & Wang, R. (2021). Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies. Technological Forecasting and Social Change, 173, Article 121159. https:\/\/doi.org\/10.1016\/j.techfore.2021.121159","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR32","first-page":"3104482","volume":"11","author":"M Nickel","year":"2011","unstructured":"Nickel, M., Tresp, V., & Kriegel, H. P. (2011). A three-way model for collective learning on multi-relational data. International Conference on Machine Learning, (pp. 809\u2013816).","journal-title":"International Conference on Machine Learning"},{"key":"5578_CR33","doi-asserted-by":"publisher","unstructured":"Nils Reimers, I. G. (2019). Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks. arXiv preprint arXiv. https:\/\/doi.org\/10.48550\/arXiv.1908.10084","DOI":"10.48550\/arXiv.1908.10084"},{"key":"5578_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2022.121934","volume":"183","author":"M Park","year":"2022","unstructured":"Park, M., & Geum, Y. (2022). Two-stage technology opportunity discovery for firm-level decision making: GCN-based link-prediction approach. Technological Forecasting and Social Change, 183, Article 121934. https:\/\/doi.org\/10.1016\/j.techfore.2022.121934","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR35","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2020.120176","volume":"159","author":"AL Porter","year":"2020","unstructured":"Porter, A. L., Chiavetta, D., & Newman, N. C. (2020). Measuring tech emergence: A contest. Technological Forecasting and Social Change, 159, Article 120176. https:\/\/doi.org\/10.1016\/j.techfore.2020.120176","journal-title":"Technological Forecasting and Social Change"},{"issue":"3","key":"5578_CR36","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1016\/0040-1625(95)00022-3","volume":"49","author":"AL Porter","year":"1995","unstructured":"Porter, A. L., & Detampel, M. J. (1995). Technology opportunities analysis. Technological Forecasting and Social Change, 49(3), 237\u2013255. https:\/\/doi.org\/10.1016\/0040-1625(95)00022-3","journal-title":"Technological Forecasting and Social Change"},{"key":"5578_CR37","doi-asserted-by":"publisher","first-page":"855","DOI":"10.1109\/TEM.2025.3543354","volume":"72","author":"Y Qiao","year":"2025","unstructured":"Qiao, Y., Zhang, S., & Chen, L. (2025). Discovering potential application areas for technologies using function-based SAO semantic analysis: A systematic framework. IEEE Transactions on Engineering Management, 72, 855\u2013872. https:\/\/doi.org\/10.1109\/TEM.2025.3543354","journal-title":"IEEE Transactions on Engineering Management"},{"key":"5578_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.technovation.2020.102196","volume":"101","author":"H Ren","year":"2021","unstructured":"Ren, H., & Zhao, Y. (2021). Technology opportunity discovery based on constructing, evaluating, and searching knowledge networks. Technovation, 101, Article 102196. https:\/\/doi.org\/10.1016\/j.technovation.2020.102196","journal-title":"Technovation"},{"key":"5578_CR39","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"European semantic web conference","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M., Kipf, T. N., Bloem, P., Van Den Berg, R., Titov, I., & Welling, M. (2018). Modeling relational data with graph convolutional networks. European semantic web conference (pp. 593\u2013607)."},{"key":"5578_CR40","first-page":"2071","volume":"48","author":"T Trouillon","year":"2016","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., & Bouchard, G. (2016). Complex embeddings for simple link prediction. International Conference on Machine Learning, 48, 2071\u20132080.","journal-title":"International Conference on Machine Learning"},{"key":"5578_CR41","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., & Talukdar, P. P. (2019). Composition-based Multi-Relational Graph Convolutional Networks. ArXiv, abs\/1911.03082."},{"key":"5578_CR42","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., & Talukdar, P. (2020). Composition-based multi-relational graph convolutional networks 8th International Conference on Learning Representations, ICLR 2020, Addis Ababa, Ethiopia."},{"key":"5578_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.resourpol.2022.102636","volume":"77","author":"C Wang","year":"2022","unstructured":"Wang, C., Geng, H., Sun, R., & Song, H. (2022). Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining. Resources Policy, 77, Article 102636. https:\/\/doi.org\/10.1016\/j.resourpol.2022.102636","journal-title":"Resources Policy"},{"key":"5578_CR44","doi-asserted-by":"publisher","DOI":"10.1016\/j.techfore.2023.122481","volume":"191","author":"J Wang","year":"2023","unstructured":"Wang, J., Zhang, Z., Feng, L., Lin, K.-Y., & Liu, P. (2023). Development of technology opportunity analysis based on technology landscape by extending technology elements with BERT and TRIZ. Technological Forecasting and Social Change, 191, Article 122481. https:\/\/doi.org\/10.1016\/j.techfore.2023.122481","journal-title":"Technological Forecasting and Social Change"},{"issue":"1","key":"5578_CR45","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11192-017-2260-y","volume":"111","author":"X Wang","year":"2017","unstructured":"Wang, X., Ma, P., Huang, Y., Guo, J., Zhu, D., Porter, A. L., & Wang, Z. (2017). Combining SAO semantic analysis and morphology analysis to identify technology opportunities. Scientometrics, 111(1), 3\u201324. https:\/\/doi.org\/10.1007\/s11192-017-2260-y","journal-title":"Scientometrics"},{"issue":"1","key":"5578_CR46","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11192-019-03191-z","volume":"121","author":"X Wang","year":"2019","unstructured":"Wang, X., Ren, H., Chen, Y., Liu, Y., Qiao, Y., & Huang, Y. (2019). Measuring patent similarity with SAO semantic analysis. Scientometrics, 121(1), 1\u201323. https:\/\/doi.org\/10.1007\/s11192-019-03191-z","journal-title":"Scientometrics"},{"key":"5578_CR47","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v28i1.8870","author":"Z Wang","year":"2014","unstructured":"Wang, Z., Zhang, J., Feng, J., & Chen, Z. (2014). Knowledge graph embedding by translating on hyperplanes. Proceedings of the AAAI Conference on Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v28i1.8870","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"5578_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2022.119053","volume":"213","author":"Y Wu","year":"2023","unstructured":"Wu, Y., J, Y., & Gu, Fu. (2023). Identifying firm-specific technology opportunities in a supply chain: Link prediction analysis in multilayer networks. Expert Systems with Applications, 213, Article 119053. https:\/\/doi.org\/10.1016\/j.eswa.2022.119053","journal-title":"Expert Systems with Applications"},{"key":"5578_CR49","doi-asserted-by":"publisher","unstructured":"Yang, B., Yih, W.-t., He, X., Gao, J., & Deng, L. (2014). Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv. https:\/\/doi.org\/10.48550\/arXiv.1412.6575","DOI":"10.48550\/arXiv.1412.6575"},{"issue":"1","key":"5578_CR50","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.joi.2018.01.006","volume":"12","author":"C Yang","year":"2018","unstructured":"Yang, C., Huang, C., & Su, J. (2018). An improved SAO network-based method for technology trend analysis: A case study of graphene. Journal of Informetrics, 12(1), 271\u2013286. https:\/\/doi.org\/10.1016\/j.joi.2018.01.006","journal-title":"Journal of Informetrics"},{"key":"5578_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2024.112155","volume":"300","author":"R Yang","year":"2024","unstructured":"Yang, R., Zhu, J., Man, J., Fang, L., & Zhou, Y. (2024). Enhancing text-based knowledge graph completion with zero-shot large language models: A focus on semantic enhancement. Knowledge-Based Systems, 300, Article 112155. https:\/\/doi.org\/10.1016\/j.knosys.2024.112155","journal-title":"Knowledge-Based Systems"},{"issue":"1","key":"5578_CR52","doi-asserted-by":"publisher","first-page":"124","DOI":"10.1016\/j.eswa.2007.06.022","volume":"35","author":"B Yoon","year":"2008","unstructured":"Yoon, B. (2008). On the development of a technology intelligence tool for identifying technology opportunity. Expert Systems with Applications, 35(1), 124\u2013135. https:\/\/doi.org\/10.1016\/j.eswa.2007.06.022","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"5578_CR53","doi-asserted-by":"publisher","first-page":"445","DOI":"10.1007\/s11192-011-0543-2","volume":"90","author":"J Yoon","year":"2012","unstructured":"Yoon, J., & Kim, K. (2012). Detecting signals of new technological opportunities using semantic patent analysis and outlier detection. Scientometrics, 90(2), 445\u2013461. https:\/\/doi.org\/10.1007\/s11192-011-0543-2","journal-title":"Scientometrics"},{"key":"5578_CR54","doi-asserted-by":"publisher","first-page":"153","DOI":"10.1016\/j.techfore.2015.04.012","volume":"100","author":"J Yoon","year":"2015","unstructured":"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\u2013167. https:\/\/doi.org\/10.1016\/j.techfore.2015.04.012","journal-title":"Technological Forecasting and Social Change"},{"issue":"3","key":"5578_CR55","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1108\/AJIM-01-2022-0031","volume":"75","author":"C Yu","year":"2023","unstructured":"Yu, C., Zhang, Z., An, L., & Li, G. (2023). A knowledge graph completion model integrating entity description and network structure. Aslib Journal of Information Management, 75(3), 500\u2013522. https:\/\/doi.org\/10.1108\/AJIM-01-2022-0031","journal-title":"Aslib Journal of Information Management"},{"issue":"4","key":"5578_CR56","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2022.102993","volume":"59","author":"S Yun","year":"2022","unstructured":"Yun, S., Cho, W., Kim, C., & Lee, S. (2022). Technological trend mining: Identifying new technology opportunities using patent semantic analysis. Information Processing & Management, 59(4), Article 102993. https:\/\/doi.org\/10.1016\/j.ipm.2022.102993","journal-title":"Information Processing & Management"},{"issue":"1","key":"5578_CR57","doi-asserted-by":"publisher","first-page":"551","DOI":"10.1007\/s11192-020-03641-z","volume":"125","author":"J Zhang","year":"2020","unstructured":"Zhang, J., & Yu, W. (2020). Early detection of technology opportunity based on analogy design and phrase semantic representation. Scientometrics, 125(1), 551\u2013576. https:\/\/doi.org\/10.1007\/s11192-020-03641-z","journal-title":"Scientometrics"},{"issue":"1","key":"5578_CR58","doi-asserted-by":"publisher","DOI":"10.1016\/j.ipm.2024.103894","volume":"62","author":"X Zhang","year":"2025","unstructured":"Zhang, X., Dang, J., Wang, Y., & Li, S. (2025). Feature enhancement based on hierarchical reconstruction framework for inductive prediction on sparse graphs. Information Processing & Management, 62(1), Article 103894. https:\/\/doi.org\/10.1016\/j.ipm.2024.103894","journal-title":"Information Processing & Management"},{"key":"5578_CR59","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2024.127857","volume":"594","author":"D Zhu","year":"2024","unstructured":"Zhu, D. (2024). PRGNN: Modeling high-order proximity with relational graph neural network for knowledge graph completion. Neurocomputing, 594, Article 127857. https:\/\/doi.org\/10.1016\/j.neucom.2024.127857","journal-title":"Neurocomputing"}],"container-title":["Scientometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-026-05578-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11192-026-05578-1","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-026-05578-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,25]],"date-time":"2026-04-25T06:56:30Z","timestamp":1777100190000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11192-026-05578-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,23]]},"references-count":59,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["5578"],"URL":"https:\/\/doi.org\/10.1007\/s11192-026-05578-1","relation":{},"ISSN":["0138-9130","1588-2861"],"issn-type":[{"value":"0138-9130","type":"print"},{"value":"1588-2861","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,23]]},"assertion":[{"value":"6 February 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interests"}}]}}