{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T15:00:23Z","timestamp":1770908423888,"version":"3.50.1"},"reference-count":51,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T00:00:00Z","timestamp":1710115200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"Key-Area Research and Development Program of Guangdong Province","award":["2021B0101420004"],"award-info":[{"award-number":["2021B0101420004"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["71801229"],"award-info":[{"award-number":["71801229"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Scientometrics"],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.1007\/s11192-024-04966-9","type":"journal-article","created":{"date-parts":[[2024,3,11]],"date-time":"2024-03-11T07:01:31Z","timestamp":1710140491000},"page":"2181-2203","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Knowledge graph enhanced citation recommendation model for patent examiners"],"prefix":"10.1007","volume":"129","author":[{"given":"Yonghe","family":"Lu","sequence":"first","affiliation":[]},{"given":"Xinyu","family":"Tong","sequence":"additional","affiliation":[]},{"given":"Xin","family":"Xiong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6843-9795","authenticated-orcid":false,"given":"Hou","family":"Zhu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,11]]},"reference":[{"issue":"4","key":"4966_CR1","doi-asserted-by":"publisher","first-page":"774","DOI":"10.1162\/rest.88.4.774","volume":"88","author":"J Alc\u00e1cer","year":"2006","unstructured":"Alc\u00e1cer, J., & Gittelman, M. (2006). Patent citations as a measure of knowledge flows: The influence of examiner citations. The Review of Economics and Statistics, 88(4), 774\u2013779.","journal-title":"The Review of Economics and Statistics"},{"issue":"2","key":"4966_CR2","doi-asserted-by":"publisher","first-page":"101135","DOI":"10.1016\/j.joi.2021.101135","volume":"15","author":"X An","year":"2021","unstructured":"An, X., Li, J., Xu, S., Chen, L., & Sun, W. (2021). An improved patent similarity measurem-ent based on entities and semantic relations. Journal of Informetrics, 15(2), 101135. https:\/\/doi.org\/10.1016\/j.joi.2021.101135","journal-title":"Journal of Informetrics"},{"issue":"1","key":"4966_CR3","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1002\/smj.2699","volume":"39","author":"S Arts","year":"2018","unstructured":"Arts, S., Cassiman, B., & Gomez, J. C. (2018). Text matching to measure patent similarity. Strategic Management Journal, 39(1), 62\u201384. https:\/\/doi.org\/10.1002\/smj.2699","journal-title":"Strategic Management Journal"},{"issue":"2","key":"4966_CR4","doi-asserted-by":"publisher","first-page":"104144","DOI":"10.1016\/j.respol.2020.104144","volume":"50","author":"S Arts","year":"2021","unstructured":"Arts, S., Hou, J., & Gomez, J. C. (2021). Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures. Research Policy, 50(2), 104144. https:\/\/doi.org\/10.1016\/j.respol.2020.104144","journal-title":"Research Policy"},{"issue":"4","key":"4966_CR5","doi-asserted-by":"publisher","first-page":"223","DOI":"10.1002\/asi.4630360402","volume":"36","author":"TA Brooks","year":"1985","unstructured":"Brooks, T. A. (1985). Private acts and public objects: An investigation of citer motivations. Journal of the American Society for Information Science, 36(4), 223\u2013229. https:\/\/doi.org\/10.1002\/asi.4630360402","journal-title":"Journal of the American Society for Information Science"},{"issue":"1","key":"4966_CR6","doi-asserted-by":"publisher","first-page":"2586","DOI":"10.1038\/s41598-023-28766-y","volume":"13","author":"H Chen","year":"2023","unstructured":"Chen, H., & Deng, W. (2023). Interpretable patent recommendation with knowledge graph and deep learning. Scientific Reports, 13(1), 2586. https:\/\/doi.org\/10.1038\/s41598-023-28766-y","journal-title":"Scientific Reports"},{"issue":"3","key":"4966_CR7","doi-asserted-by":"publisher","first-page":"2091","DOI":"10.1007\/s11192-020-03666-4","volume":"125","author":"J Chen","year":"2020","unstructured":"Chen, J., Chen, J., Zhao, S., Zhang, Y., & Tang, J. (2020). Exploiting word embedding for he-erogeneous topic model towards patent recommendation. Scientometrics, 125(3), 2091\u20132108. https:\/\/doi.org\/10.1007\/s11192-020-03666-4","journal-title":"Scientometrics"},{"issue":"1","key":"4966_CR8","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1016\/j.joi.2016.04.018","volume":"11","author":"L Chen","year":"2017","unstructured":"Chen, L. (2017). Do patent citations indicate knowledge linkage? The evidence from text simil-arities between patents and their citations. Journal of Informetrics, 11(1), 63\u201379. https:\/\/doi.org\/10.1016\/j.joi.2016.04.018","journal-title":"Journal of Informetrics"},{"key":"4966_CR9","volume-title":"Guidelines for patent examination 2010","author":"China National Intellectual Property Office (CNIPO)","year":"2010","unstructured":"China National Intellectual Property Office (CNIPO). (2010). Guidelines for patent examination 2010. Intellectual Property Publishing House Co., Ltd."},{"key":"4966_CR10","unstructured":"China National Intellectual Property Office (CNIPO). (2020). Process of patent application examination and approval. Retrieved December 10, 2023, from https:\/\/www.cnipa.gov.cn\/art\/2020\/6\/5\/art_1517_92471.html"},{"issue":"2","key":"4966_CR11","doi-asserted-by":"publisher","first-page":"101286","DOI":"10.1016\/j.joi.2022.101286","volume":"16","author":"J Choi","year":"2022","unstructured":"Choi, J., & Yoon, J. (2022). Measuring knowledge exploration distance at the patent level: Application of network embedding and citation analysis. Journal of Informetrics, 16(2), 101286. https:\/\/doi.org\/10.1016\/j.joi.2022.101286","journal-title":"Journal of Informetrics"},{"issue":"11","key":"4966_CR12","doi-asserted-by":"publisher","first-page":"6615","DOI":"10.1007\/s11192-022-04301-0","volume":"127","author":"J Choi","year":"2022","unstructured":"Choi, J., Lee, J., Yoon, J., Jang, S., Kim, J., & Choi, S. (2022a). A two-stage deep learning- based system for patent citation recommendation. Scientometrics, 127(11), 6615\u20136636. https:\/\/doi.org\/10.1007\/s11192-022-04301-0","journal-title":"Scientometrics"},{"key":"4966_CR13","doi-asserted-by":"publisher","first-page":"121413","DOI":"10.1016\/j.techfore.2021.121413","volume":"175","author":"S Choi","year":"2022","unstructured":"Choi, S., Lee, H., Park, E., & Choi, S. (2022b). Deep learning for patent landscaping using transformer and graph embedding. Technological Forecasting and Social Change, 175, 121413. https:\/\/doi.org\/10.1016\/j.techfore.2021.121413","journal-title":"Technological Forecasting and Social Change"},{"issue":"10","key":"4966_CR14","doi-asserted-by":"publisher","first-page":"104360","DOI":"10.1016\/j.respol.2021.104360","volume":"50","author":"CAW DeGrazia","year":"2021","unstructured":"DeGrazia, C. A. W., Pairolero, N. A., & Teodorescu, M. H. M. (2021). Examination incentives, learning, and patent office outcomes: The use of examiner\u2019s amendments at the USPTO. Research Policy, 50(10), 104360. https:\/\/doi.org\/10.1016\/j.respol.2021.104360","journal-title":"Research Policy"},{"issue":"4","key":"4966_CR15","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.1007\/s10660-021-09471-2","volume":"22","author":"W Deng","year":"2022","unstructured":"Deng, W., & Ma, J. (2022). A knowledge graph approach for recommending patents to companies. Electronic Commerce Research, 22(4), 1435\u20131466. https:\/\/doi.org\/10.1007\/s10660-021-09471-2","journal-title":"Electronic Commerce Research"},{"key":"4966_CR16","doi-asserted-by":"publisher","unstructured":"Devlin, J., Chang, M.W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. In J. Burstein, C. Doran, & T. Solorio (Eds.), Proceedings of the 2019 conference of the North American chapter of the association for computational linguistics: human language technologies, volume 1 (long and short papers) (pp. 4171\u20134186). https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"4966_CR17","doi-asserted-by":"publisher","unstructured":"Dietz, L., Kotov, A., & Meij, E. (2018). Utilizing knowledge graphs for text-centric information retrieval. In: The 41st international ACM SIGIR conference on research & development in information retrieval (pp. 1387\u20131390). https:\/\/doi.org\/10.1145\/3209978.3210187","DOI":"10.1145\/3209978.3210187"},{"key":"4966_CR18","unstructured":"European Patent Office (EPO). (2023). Guidelines for examination. Retrieved December 10, 2023, from https:\/\/www.epo.org\/en\/legal\/guidelines-epc\/2023\/index.html"},{"key":"4966_CR19","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1007\/s00799-020-00288-2","volume":"21","author":"M F\u00e4rber","year":"2020","unstructured":"F\u00e4rber, M., & Jatowt, A. (2020). Citation recommendation: Approaches and datasets. International Journal on Digital Libraries, 21, 375\u2013405. https:\/\/doi.org\/10.1007\/s00799-020-00288-2","journal-title":"International Journal on Digital Libraries"},{"key":"4966_CR20","doi-asserted-by":"publisher","unstructured":"Fu, T.Y., Lei, Z., & Lee, W.C. (2015). Patent Citation Recommendation for Examiners. In Proceedings of the 2015 IEEE international conference on data mining (ICDM) (pp. 751\u2013756). https:\/\/doi.org\/10.1109\/ICDM.2015.151","DOI":"10.1109\/ICDM.2015.151"},{"key":"4966_CR21","doi-asserted-by":"publisher","first-page":"121559","DOI":"10.1016\/j.techfore.2022.121559","volume":"177","author":"DS Hain","year":"2022","unstructured":"Hain, D. S., Jurowetzki, R., Buchmann, T., & Wolf, P. (2022). A text-embedding-based approach to measuring patent-to-patent technological similarity. Technological Forecasting and Social Change, 177, 121559. https:\/\/doi.org\/10.1016\/j.techfore.2022.121559","journal-title":"Technological Forecasting and Social Change"},{"issue":"12","key":"4966_CR22","doi-asserted-by":"publisher","first-page":"1969","DOI":"10.1287\/mnsc.1090.1069","volume":"55","author":"D Harhoff","year":"2009","unstructured":"Harhoff, D., & Wagner, S. (2009). The duration of patent examination at the European patent office. Management Science, 55(12), 1969\u20131984. https:\/\/doi.org\/10.1287\/mnsc.1090.1069","journal-title":"Management Science"},{"key":"4966_CR23","unstructured":"Japan Patent Office (JPO). (2015). Examination guidelines for patent and utility model in Japan. Retrieved December 10, 2023, from https:\/\/www.jpo.go.jp\/e\/system\/laws\/rule\/guideline\/patent\/tukujitu_kijun\/index.html"},{"issue":"2","key":"4966_CR24","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2022","unstructured":"Ji, S., Pan, S., Cambria, E., Marttinen, P., & Yu, P. S. (2022). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494\u2013514. https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843","journal-title":"IEEE Transactions on Neural Networks and Learning Systems"},{"issue":"5","key":"4966_CR25","doi-asserted-by":"publisher","first-page":"1005","DOI":"10.1016\/j.respol.2017.03.007","volume":"46","author":"YK Kim","year":"2017","unstructured":"Kim, Y. K., & Oh, J. B. (2017). Examination workloads, grant decision bias and examination quality of patent office. Research Policy, 46(5), 1005\u20131019. https:\/\/doi.org\/10.1016\/j.respol.2017.03.007","journal-title":"Research Policy"},{"key":"4966_CR26","doi-asserted-by":"publisher","first-page":"102035","DOI":"10.1016\/j.wpi.2021.102035","volume":"65","author":"R Krestel","year":"2021","unstructured":"Krestel, R., Chikkamath, R., Hewel, C., & Risch, J. (2021). A survey on deep learning for patent analysis. World Patent Information, 65, 102035. https:\/\/doi.org\/10.1016\/j.wpi.2021.102035","journal-title":"World Patent Information"},{"issue":"3","key":"4966_CR27","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.wpi.2021.102035","volume":"13","author":"JM Kuhn","year":"2011","unstructured":"Kuhn, J. M. (2011). Information overload at the U.S. patent and trademark office: Reframing the duty of disclosure in patent law as a search and filter problem. Journal of Law and Technology, 13(3), 90\u2013139. https:\/\/doi.org\/10.1016\/j.wpi.2021.102035","journal-title":"Journal of Law and Technology"},{"issue":"1","key":"4966_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s11192-020-03731-y","volume":"126","author":"J Lee","year":"2021","unstructured":"Lee, J., & Sohn, S. Y. (2021). Recommendation system for technology convergence opportunit-ies based on self-supervised representation learning. Scientometrics, 126(1), 1\u201325. https:\/\/doi.org\/10.1007\/s11192-020-03731-y","journal-title":"Scientometrics"},{"issue":"12","key":"4966_CR29","first-page":"150","volume":"35","author":"D Lin","year":"2016","unstructured":"Lin, D., Sun, J., Hao, T., & Wang, C. (2016). Research on the applicability of patent citation in patent value evaluation. Journal of Intelligence, 35(12), 150\u2013154.","journal-title":"Journal of Intelligence"},{"issue":"03","key":"4966_CR30","doi-asserted-by":"publisher","first-page":"2901","DOI":"10.1609\/aaai.v34i03.5681","volume":"34","author":"W Liu","year":"2020","unstructured":"Liu, W., Zhou, P., Zhao, Z., Wang, Z., Ju, Q., Deng, H., & Wang, P. (2020). K-BERT: Enabling language representation with knowledge graph. Proceedings of the AAAI Conference on Artificial Intelligence, 34(03), 2901\u20132908. https:\/\/doi.org\/10.1609\/aaai.v34i03.5681","journal-title":"Proceedings of the AAAI Conference on Artificial Intelligence"},{"key":"4966_CR31","doi-asserted-by":"publisher","first-page":"813","DOI":"10.1007\/s11192-020-03385-w","volume":"123","author":"Y Lu","year":"2020","unstructured":"Lu, Y., Xiong, X., Zhang, W., Liu, J., & Zhao, R. (2020). Research on classification and simi-larity of patent citation based on deep learning. Scientometrics, 123, 813\u2013839. https:\/\/doi.org\/10.1007\/s11192-020-03385-w","journal-title":"Scientometrics"},{"issue":"1","key":"4966_CR32","doi-asserted-by":"publisher","first-page":"93","DOI":"10.1023\/A:1005613325648","volume":"49","author":"M Meyer","year":"2000","unstructured":"Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49(1), 93\u2013123. https:\/\/doi.org\/10.1023\/A:1005613325648","journal-title":"Scientometrics"},{"issue":"6","key":"4966_CR33","first-page":"20","volume":"8","author":"G Ou","year":"2022","unstructured":"Ou, G., Pang, N., & Wu, J. (2022). Influencing factors of patent examination cycle: Case study of artificial intelligence in China. Data Analysis and Knowledge Discovery, 8(6), 20\u201330.","journal-title":"Data Analysis and Knowledge Discovery"},{"key":"4966_CR34","volume-title":"Knowledge graphs: An information retrieval perspective","author":"R Ridho","year":"2020","unstructured":"Ridho, R., Edgar, M., & Maarten, D. R. (2020). Knowledge graphs: An information retrieval perspective. Now Foundations and Trends."},{"issue":"2","key":"4966_CR35","doi-asserted-by":"publisher","first-page":"631","DOI":"10.1007\/s10115-018-1322-7","volume":"61","author":"W Shalaby","year":"2019","unstructured":"Shalaby, W., & Zadrozny, W. (2019). Patent retrieval: A literature review. Knowledge and Information Systems, 61(2), 631\u2013660. https:\/\/doi.org\/10.1007\/s10115-018-1322-7","journal-title":"Knowledge and Information Systems"},{"issue":"2","key":"4966_CR36","doi-asserted-by":"publisher","first-page":"2019","DOI":"10.1007\/s40747-022-00898-0","volume":"9","author":"K Shi","year":"2023","unstructured":"Shi, K., Cai, X., Yang, L., & Zhao, J. (2023). Enriched entity representation of knowledge graph for text generation. Complex & Intelligent Systems, 9(2), 2019\u20132030. https:\/\/doi.org\/10.1007\/s40747-022-00898-0","journal-title":"Complex & Intelligent Systems"},{"issue":"1","key":"4966_CR37","doi-asserted-by":"publisher","first-page":"101467","DOI":"10.1016\/j.joi.2023.101467","volume":"18","author":"H Teng","year":"2024","unstructured":"Teng, H., Wang, N., Zhao, H., Hu, Y., & Jin, H. (2024). Enhancing semantic text similarity with functional semantic knowledge (FOP) in patents. Journal of Informetrics, 18(1), 101467. https:\/\/doi.org\/10.1016\/j.joi.2023.101467","journal-title":"Journal of Informetrics"},{"issue":"3","key":"4966_CR38","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.respol.2018.01.002","volume":"47","author":"TW Tong","year":"2018","unstructured":"Tong, T. W., Zhang, K., He, Z., & Zhang, Y. (2018). What determines the duration of patent examination in China? An outcome-specific duration analysis of invention patent applications at SIPO. Research Policy, 47(3), 583\u2013591. https:\/\/doi.org\/10.1016\/j.respol.2018.01.002","journal-title":"Research Policy"},{"key":"4966_CR39","unstructured":"United States Patent Office (USPTO). (2020). Understanding the patent examination process. Retrieved December 10, 2023, from https:\/\/www.uspto.gov\/sites\/default\/files\/documents\/InventionCon2020_Understanding_the_Patent_Examination_Process.pdf"},{"key":"4966_CR40","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Polosukhin, I. (2017). Attention is all you need. In Proceedings of the 31st international conference on neural information processing systems (pp. 6000\u20136010). Curran Associates Inc"},{"issue":"2","key":"4966_CR41","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1007\/s11192-018-2885-5","volume":"117","author":"T Wada","year":"2018","unstructured":"Wada, T. (2018). The choice of examiner patent citations for refusals: Evidence from the trilateral offices. Scientometrics, 117(2), 825\u2013843. https:\/\/doi.org\/10.1007\/s11192-018-2885-5","journal-title":"Scientometrics"},{"issue":"1","key":"4966_CR42","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":"4966_CR43","doi-asserted-by":"publisher","DOI":"10.1177\/01655515221106651","author":"Z Wang","year":"2022","unstructured":"Wang, Z., & Liu, Y. (2022). SEA-PS: Semantic embedding with attention to measuring patent similarity by leveraging various text fields. Journal of Information Science. https:\/\/doi.org\/10.1177\/01655515221106651","journal-title":"Journal of Information Science"},{"key":"4966_CR44","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-95408-6_11","volume-title":"Advanced data mining and applications","author":"J Wu","year":"2022","unstructured":"Wu, J., Li, B., Ji, Y., Tian, J., & Xiang, Y. (2022). Text-enhanced knowledge graph representation model in hyperbolic space. In J. Jiang & W. Chen (Eds.), Advanced data mining and applications. Springer. https:\/\/doi.org\/10.1007\/978-3-030-95408-6_11"},{"key":"4966_CR45","doi-asserted-by":"publisher","first-page":"106722","DOI":"10.1016\/j.engappai.2023.106722","volume":"126","author":"Y Xiao","year":"2023","unstructured":"Xiao, Y., Li, C., & Th\u00fcrer, M. (2023). A patent recommendation method based on KG repres-entation learning. Engineering Applications of Artificial Intelligence, 126, 106722. https:\/\/doi.org\/10.1016\/j.engappai.2023.106722","journal-title":"Engineering Applications of Artificial Intelligence"},{"key":"4966_CR46","volume-title":"Advances in artificial intelligence: From theory to practice","author":"B Xu","year":"2017","unstructured":"Xu, B., Xu, Y., Liang, J., Xie, C., Liang, B., Cui, W., Xu, B., & Xiao, Y. (2017). CN-DBpedia: A never-ending Chinese knowledge extraction system. In S. Benferhat, K. Tabia, & M. Ali (Eds.), Advances in artificial intelligence: From theory to practice. Springer."},{"issue":"8","key":"4966_CR47","doi-asserted-by":"publisher","first-page":"1601","DOI":"10.1016\/j.respol.2015.05.003","volume":"44","author":"I Yamauchi","year":"2015","unstructured":"Yamauchi, I., & Nagaoka, S. (2015). Does the outsourcing of prior art search increase the efficiency of patent examination? Evidence from Japan. Research Policy, 44(8), 1601\u20131614. https:\/\/doi.org\/10.1016\/j.respol.2015.05.003","journal-title":"Evidence from Japan. Research Policy"},{"key":"4966_CR48","doi-asserted-by":"publisher","first-page":"122031","DOI":"10.1016\/j.eswa.2023.122031","volume":"238","author":"MJ Yin","year":"2024","unstructured":"Yin, M. J., Wang, B., & Ling, C. (2024). A fast local citation recommendation algorithm scal-able to multi-topics. Expert Systems with Applications, 238, 122031. https:\/\/doi.org\/10.1016\/j.eswa.2023.122031","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"4966_CR50","doi-asserted-by":"publisher","first-page":"1108","DOI":"10.1016\/j.joi.2016.09.006","volume":"10","author":"Y Zhang","year":"2016","unstructured":"Zhang, Y., Shang, L., Huang, L., Porter, A. L., Zhang, G., Lu, J., & Zhu, D. (2016). A hybrid similarity measure method for patent portfolio analysis. Journal of Informetrics, 10(4), 1108\u20131130. https:\/\/doi.org\/10.1016\/j.joi.2016.09.006","journal-title":"Journal of Informetrics"},{"key":"4966_CR51","doi-asserted-by":"publisher","unstructured":"Zhao, Z., Chen, H., Zhang, J., Zhao, X., Liu, T., Lu, W., Du, X. (2019). UER: An open-source toolkit for pre-training models. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP): System demonstrations (pp. 241\u2013246). https:\/\/doi.org\/10.18653\/v1\/D19-3041","DOI":"10.18653\/v1\/D19-3041"},{"issue":"7","key":"4966_CR52","first-page":"28","volume":"40","author":"Y Zhao","year":"2017","unstructured":"Zhao, Y., & Wen, T. (2017). Motivation analysis of patent citation. Information Studies Theory & Application, 40(7), 28\u201332.","journal-title":"Information Studies Theory & Application"}],"container-title":["Scientometrics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-024-04966-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11192-024-04966-9\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11192-024-04966-9.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T10:08:07Z","timestamp":1715767687000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11192-024-04966-9"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,11]]},"references-count":51,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["4966"],"URL":"https:\/\/doi.org\/10.1007\/s11192-024-04966-9","relation":{},"ISSN":["0138-9130","1588-2861"],"issn-type":[{"value":"0138-9130","type":"print"},{"value":"1588-2861","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,3,11]]},"assertion":[{"value":"23 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"11 March 2024","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 declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}