{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,8]],"date-time":"2025-11-08T13:29:16Z","timestamp":1762608556820,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030930455"},{"type":"electronic","value":"9783030930462"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-93046-2_10","type":"book-chapter","created":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T05:30:01Z","timestamp":1641015001000},"page":"111-122","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Knowledge Powered Cooperative Semantic Fusion for\u00a0Patent Classification"],"prefix":"10.1007","author":[{"given":"Zhe","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Le","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yichao","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hui","family":"Xiong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enhong","family":"Chen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"10_CR1","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"474","DOI":"10.1007\/978-3-319-91458-9_29","volume-title":"Database Systems for Advanced Applications","author":"H Lin","year":"2018","unstructured":"Lin, H., Wang, H., Du, D., Wu, H., Chang, B., Chen, E.: Patent quality valuation with deep learning models. In: Pei, J., Manolopoulos, Y., Sadiq, S., Li, J. (eds.) DASFAA 2018. LNCS, vol. 10828, pp. 474\u2013490. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-91458-9_29"},{"key":"10_CR2","doi-asserted-by":"crossref","unstructured":"Fujii, A.: Enhancing patent retrieval by citation analysis. In: Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 793\u2013794 (2007)","DOI":"10.1145\/1277741.1277912"},{"key":"10_CR3","doi-asserted-by":"crossref","unstructured":"Liu, Q., Wu, H., Ye, Y., Zhao, H., Liu, C., Du, D.: Patent litigation prediction: a convolutional tensor factorization approach. In: IJCAI, pp. 5052\u20135059 (2018)","DOI":"10.24963\/ijcai.2018\/701"},{"key":"10_CR4","doi-asserted-by":"crossref","unstructured":"Risch, J., Krestel, R.: Domain-specific word embeddings for patent classification. Data Technol. Appl. (2019)","DOI":"10.1108\/DTA-01-2019-0002"},{"key":"10_CR5","doi-asserted-by":"crossref","unstructured":"Tang, P., Jiang, M., (Ning) Xia, B., Pitera, J.W., Welser, J., Chawla, N.V.: Multi-label patent categorization with non-local attention-based graph convolutional network. In: AAAI, pp. 9024\u20139031 (2020)","DOI":"10.1609\/aaai.v34i05.6435"},{"key":"10_CR6","doi-asserted-by":"crossref","unstructured":"D\u2019hondt, E., Verberne, S., Koster, C., Boves, L.: Text representations for patent classification. Comput. Linguist. 39(3), 755\u2013775 (2013)","DOI":"10.1162\/COLI_a_00149"},{"issue":"4","key":"10_CR7","doi-asserted-by":"publisher","first-page":"1164","DOI":"10.1016\/j.asoc.2009.11.033","volume":"10","author":"W Chih-Hung","year":"2010","unstructured":"Chih-Hung, W., Ken, Y., Huang, T.: Patent classification system using a new hybrid genetic algorithm support vector machine. Appl. Soft Comput. 10(4), 1164\u20131177 (2010)","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"10_CR8","doi-asserted-by":"publisher","first-page":"721","DOI":"10.1007\/s11192-018-2905-5","volume":"117","author":"S Li","year":"2018","unstructured":"Li, S., Jie, H., Cui, Y., Jianjun, H.: DeepPatent: patent classification with convolutional neural networks and word embedding. Scientometrics 117(2), 721\u2013744 (2018)","journal-title":"Scientometrics"},{"key":"10_CR9","doi-asserted-by":"crossref","unstructured":"Lee, J.-S., Hsiang, J.: Patent classification by fine-tuning BERT language model. World Patent Inf. 61, 101965 (2020)","DOI":"10.1016\/j.wpi.2020.101965"},{"key":"10_CR10","doi-asserted-by":"crossref","unstructured":"Milne, D., Witten, I.H.: Learning to link with Wikipedia. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 509\u2013518 (2008)","DOI":"10.1145\/1458082.1458150"},{"key":"10_CR11","doi-asserted-by":"crossref","unstructured":"Sil, A., Yates, A.: Re-ranking for joint named-entity recognition and linking. In: Proceedings of the 22nd ACM International Conference on Information & Knowledge Management, pp. 2369\u20132374 (2013)","DOI":"10.1145\/2505515.2505601"},{"key":"10_CR12","unstructured":"Kipf, T.N., Welling, M.: Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016)"},{"key":"10_CR13","doi-asserted-by":"crossref","unstructured":"Fall, C.J., T\u00f6rcsv\u00e1ri, A., Benzineb, K., Karetka, G.: Automated categorization in the international patent classification. In: ACM SIGIR Forum, vol. 37, pp. 10\u201325. ACM, New York (2003)","DOI":"10.1145\/945546.945547"},{"key":"10_CR14","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"10_CR15","doi-asserted-by":"crossref","unstructured":"Cho, K., Van Merri\u00ebnboer, B., Bahdanau, D., Bengio, Y.: On the properties of neural machine translation: encoder-decoder approaches. arXiv preprint arXiv:1409.1259 (2014)","DOI":"10.3115\/v1\/W14-4012"},{"key":"10_CR16","unstructured":"Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)"},{"key":"10_CR17","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.neucom.2019.08.080","volume":"386","author":"X Jingyun","year":"2020","unstructured":"Jingyun, X., et al.: Incorporating context-relevant concepts into convolutional neural networks for short text classification. Neurocomputing 386, 42\u201353 (2020)","journal-title":"Neurocomputing"},{"key":"10_CR18","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1007\/978-3-030-61244-3_9","volume-title":"Knowledge Engineering and Knowledge Management","author":"M Alam","year":"2020","unstructured":"Alam, M., Bie, Q., T\u00fcrker, R., Sack, H.: Entity-based short text classification using convolutional neural networks. In: Keet, C.M., Dumontier, M. (eds.) EKAW 2020. LNCS (LNAI), vol. 12387, pp. 136\u2013146. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-61244-3_9"},{"key":"10_CR19","doi-asserted-by":"crossref","unstructured":"Wang, J., Wang, Z., Zhang, D., Yan, J.: Combining knowledge with deep convolutional neural networks for short text classification. In: IJCAI, vol. 350 (2017)","DOI":"10.24963\/ijcai.2017\/406"},{"key":"10_CR20","doi-asserted-by":"crossref","unstructured":"Chen, J., Yizhou, H., Liu, J., Xiao, Y., Jiang, H.: Deep short text classification with knowledge powered attention. In: Proceedings of the AAAI Conference on Artificial Intelligence vol. 33, pp. 6252\u20136259 (2019)","DOI":"10.1609\/aaai.v33i01.33016252"},{"key":"10_CR21","doi-asserted-by":"crossref","unstructured":"Linmei, H., Yang, T., Shi, C., Ji, H., Li, X.: Heterogeneous graph attention networks for semi-supervised short text classification. 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), pp. 4823\u20134832 (2019)","DOI":"10.18653\/v1\/D19-1488"},{"key":"10_CR22","doi-asserted-by":"crossref","unstructured":"Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from Wikipedia. Semantic Web 6(2), 167\u2013195 (2015)","DOI":"10.3233\/SW-140134"},{"key":"10_CR23","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Neural Information Processing Systems (NIPS), pp. 1\u20139 (2013)"},{"key":"10_CR24","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G.S., Dean, J.: Distributed representations of words and phrases and their compositionality. In: Advances in Neural Information Processing Systems, vol. 26, pp. 3111\u20133119 (2013)"},{"key":"10_CR25","unstructured":"Bahdanau, D., Cho, K., Bengio, Y.: Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473 (2014)"},{"key":"10_CR26","unstructured":"Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, vol. 25, pp. 1097\u20131105 (2012)"},{"key":"10_CR27","doi-asserted-by":"crossref","unstructured":"Huang, W., et al.: Hierarchical multi-label text classification: an attention-based recurrent network approach. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1051\u20131060 (2019)","DOI":"10.1145\/3357384.3357885"},{"key":"10_CR28","doi-asserted-by":"crossref","unstructured":"Prabhu, Y., Varma, M.: FastXML: a fast, accurate and stable tree-classifier for extreme multi-label learning. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 263\u2013272 (2014)","DOI":"10.1145\/2623330.2623651"},{"key":"10_CR29","unstructured":"Kingma, D.P., Adam, J.B.: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"issue":"1","key":"10_CR30","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"10_CR31","doi-asserted-by":"crossref","unstructured":"Joulin, A., Grave, E., Bojanowski, P., Mikolov, T.: Bag of tricks for efficient text classification. arXiv preprint arXiv:1607.01759 (2016)","DOI":"10.18653\/v1\/E17-2068"},{"key":"10_CR32","unstructured":"Lin, Z., et al.: A structured self-attentive sentence embedding. arXiv preprint arXiv:1703.03130 (2017)"}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-93046-2_10","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,18]],"date-time":"2022-06-18T08:04:23Z","timestamp":1655539463000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-93046-2_10"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030930455","9783030930462"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-93046-2_10","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"1 January 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"CAAI International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hangzhou","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"5 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cicai2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/cicai.caai.cn\/#\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"307","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"105","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3.2","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5.3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}