{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T14:49:00Z","timestamp":1773154140526,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":56,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,10,26]],"date-time":"2021-10-26T00:00:00Z","timestamp":1635206400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"S&T Program of Hebei","award":["20310101D"],"award-info":[{"award-number":["20310101D"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U20B20536 2002007"],"award-info":[{"award-number":["U20B20536 2002007"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["208AAA0101100"],"award-info":[{"award-number":["208AAA0101100"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"RGC","award":["RIF (R6020-19 and R6021-20) and GRF (16211520)"],"award-info":[{"award-number":["RIF (R6020-19 and R6021-20) and GRF (16211520)"]}]},{"name":"ITC","award":["MHKJFS (MHP\/001\/19)"],"award-info":[{"award-number":["MHKJFS (MHP\/001\/19)"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,26]]},"DOI":"10.1145\/3459637.3482252","type":"proceedings-article","created":{"date-parts":[[2021,10,30]],"date-time":"2021-10-30T18:34:11Z","timestamp":1635618851000},"page":"1416-1425","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":77,"title":["Differentially Private Federated Knowledge Graphs Embedding"],"prefix":"10.1145","author":[{"given":"Hao","family":"Peng","sequence":"first","affiliation":[{"name":"Beihang University, Beijing, China"}]},{"given":"Haoran","family":"Li","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology & Peng Cheng Laboratory, Hong Kong, Hong Kong"}]},{"given":"Yangqiu","family":"Song","sequence":"additional","affiliation":[{"name":"Hong Kong University of Science and Technology & Peng Cheng Laboratory, Hong Kong, Hong Kong"}]},{"given":"Vincent","family":"Zheng","sequence":"additional","affiliation":[{"name":"Webank Co., Ltd, Shenzhen, China"}]},{"given":"Jianxin","family":"Li","sequence":"additional","affiliation":[{"name":"Beihang University, Beijing, China"}]}],"member":"320","published-online":{"date-parts":[[2021,10,30]]},"reference":[{"key":"e_1_3_2_2_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/2533888.2533938"},{"key":"e_1_3_2_2_2_1","volume-title":"Knowledge Graph Embeddings and Explainable AI. arXiv preprint arXiv:2004.14843","author":"Bianchi Federico","year":"2020","unstructured":"Federico Bianchi , Gaetano Rossiello , Luca Costabello , Matteo Palmonari , and Pasquale Minervini . 2020. Knowledge Graph Embeddings and Explainable AI. arXiv preprint arXiv:2004.14843 ( 2020 ). Federico Bianchi, Gaetano Rossiello, Luca Costabello, Matteo Palmonari, and Pasquale Minervini. 2020. Knowledge Graph Embeddings and Explainable AI. arXiv preprint arXiv:2004.14843 (2020)."},{"key":"e_1_3_2_2_3_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_2_2_4_1","doi-asserted-by":"publisher","DOI":"10.5555\/2999792.2999923"},{"key":"e_1_3_2_2_5_1","unstructured":"Yixin Cao Zhiyuan Liu Chengjiang Li Zhiyuan Liu Juanzi Li and Tat-Seng Chua. 2019. Multi-Channel Graph Neural Network for Entity Alignment. In ACL (1). 1452--1461.  Yixin Cao Zhiyuan Liu Chengjiang Li Zhiyuan Liu Juanzi Li and Tat-Seng Chua. 2019. Multi-Channel Graph Neural Network for Entity Alignment. In ACL (1). 1452--1461."},{"key":"e_1_3_2_2_6_1","unstructured":"Nicholas Carlini Florian Tramer Eric Wallace Matthew Jagielski Ariel Herbert-Voss Katherine Lee Adam Roberts Tom Brown Dawn Song Ulfar Erlingsson etal 2020. Extracting Training Data from Large Language Models. arXiv preprint arXiv:2012.07805 (2020).  Nicholas Carlini Florian Tramer Eric Wallace Matthew Jagielski Ariel Herbert-Voss Katherine Lee Adam Roberts Tom Brown Dawn Song Ulfar Erlingsson et al. 2020. Extracting Training Data from Large Language Models. arXiv preprint arXiv:2012.07805 (2020)."},{"key":"e_1_3_2_2_7_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304326"},{"key":"e_1_3_2_2_8_1","doi-asserted-by":"publisher","DOI":"10.5555\/3172077.3172097"},{"key":"e_1_3_2_2_9_1","volume-title":"FedE: Embedding Knowledge Graphs in Federated Setting. arXiv preprint arXiv:2010.12882","author":"Chen Mingyang","year":"2020","unstructured":"Mingyang Chen , Wen Zhang , Zonggang Yuan , Yantao Jia , and Huajun Chen . 2020. FedE: Embedding Knowledge Graphs in Federated Setting. arXiv preprint arXiv:2010.12882 ( 2020 ). Mingyang Chen, Wen Zhang, Zonggang Yuan, Yantao Jia, and Huajun Chen. 2020. FedE: Embedding Knowledge Graphs in Federated Setting. arXiv preprint arXiv:2010.12882 (2020)."},{"key":"e_1_3_2_2_10_1","volume-title":"Proceedings of ICLR.","author":"Conneau Alexis","year":"2018","unstructured":"Alexis Conneau , Guillaume Lample , Marc'Aurelio Ranzato , Ludovic Denoyer , and Herv\u00e9 J\u00e9gou . 2018 . Word Translation Without Parallel Data . Proceedings of ICLR. Alexis Conneau, Guillaume Lample, Marc'Aurelio Ranzato, Ludovic Denoyer, and Herv\u00e9 J\u00e9gou. 2018. Word Translation Without Parallel Data. Proceedings of ICLR."},{"key":"e_1_3_2_2_11_1","doi-asserted-by":"publisher","DOI":"10.3233\/SW-150171"},{"key":"e_1_3_2_2_12_1","doi-asserted-by":"publisher","DOI":"10.5555\/1791834.1791836"},{"key":"e_1_3_2_2_13_1","doi-asserted-by":"crossref","unstructured":"C. Dwork and A. Roth. 2014. The Algorithmic Foundations of Differential Privacy. In The Algorithmic Foundations of Differential Privacy. 19--20.  C. Dwork and A. Roth. 2014. The Algorithmic Foundations of Differential Privacy. In The Algorithmic Foundations of Differential Privacy. 19--20.","DOI":"10.1561\/9781601988195"},{"key":"e_1_3_2_2_14_1","volume-title":"Demos, SuCCESS) 48","author":"Ehrlinger Lisa","year":"2016","unstructured":"Lisa Ehrlinger and Wolfram W\u00f6\u00df . 2016. Towards a Definition of Knowledge Graphs. SEMANTiCS (Posters , Demos, SuCCESS) 48 ( 2016 ), 1--4. Lisa Ehrlinger and Wolfram W\u00f6\u00df. 2016. Towards a Definition of Knowledge Graphs. SEMANTiCS (Posters, Demos, SuCCESS) 48 (2016), 1--4."},{"key":"e_1_3_2_2_15_1","volume-title":"Differentially Private Feder-ated Learning: A Client Level Perspective. In NIPS Workshop on Machine Learning on the Phone and other Consumer Devices.","author":"Geyer Robin C.","year":"2017","unstructured":"Robin C. Geyer , Tassilo Klein , and Moin Nabi . 2017 . Differentially Private Feder-ated Learning: A Client Level Perspective. In NIPS Workshop on Machine Learning on the Phone and other Consumer Devices. Robin C. Geyer, Tassilo Klein, and Moin Nabi. 2017. Differentially Private Feder-ated Learning: A Client Level Perspective. In NIPS Workshop on Machine Learning on the Phone and other Consumer Devices."},{"key":"e_1_3_2_2_16_1","doi-asserted-by":"publisher","DOI":"10.5555\/2969033.2969125"},{"key":"e_1_3_2_2_17_1","volume-title":"Proceedings of NeurIPS.","author":"Grammenos Andreas","year":"2020","unstructured":"Andreas Grammenos , Rodrigo Mendoza Smith , Jon Crowcroft , and Cecilia Mas-colo. 2020 . Federated Principal Component Analysis . In Proceedings of NeurIPS. Andreas Grammenos, Rodrigo Mendoza Smith, Jon Crowcroft, and Cecilia Mas-colo. 2020. Federated Principal Component Analysis. In Proceedings of NeurIPS."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D18-2024"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1067"},{"key":"e_1_3_2_2_20_1","volume-title":"A survey on knowledge graphs: Representation, acquisition, and applications","author":"Ji Shaoxiong","year":"2021","unstructured":"Shaoxiong Ji , Shirui Pan , Erik Cambria , Pekka Marttinen , and S Yu Philip . 2021. A survey on knowledge graphs: Representation, acquisition, and applications . IEEE Transactions on Neural Networks and Learning Systems ( 2021 ). Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and S Yu Philip. 2021. A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems (2021)."},{"key":"e_1_3_2_2_21_1","volume-title":"Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arXiv preprint arXiv:1610.02527","author":"Konecn\u00fd Jakub","year":"2016","unstructured":"Jakub Konecn\u00fd , H. Brendan McMahan , Daniel Ramage , and Peter Richt\u00e1rik . 2016 . Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arXiv preprint arXiv:1610.02527 (2016). Jakub Konecn\u00fd, H. Brendan McMahan, Daniel Ramage, and Peter Richt\u00e1rik. 2016. Federated Optimization: Distributed Machine Learning for On-Device Intelligence. arXiv preprint arXiv:1610.02527 (2016)."},{"key":"e_1_3_2_2_22_1","volume-title":"Federated Learning: Strategies for Improving Communication Efficiency. In NIPS Workshop on Private Multi-Party Machine Learning.","author":"Kone\u010dn\u00fd Jakub","year":"2016","unstructured":"Jakub Kone\u010dn\u00fd , H. Brendan McMahan , Felix X. Yu , Peter Richtarik , Ananda Theertha Suresh , and Dave Bacon . 2016 . Federated Learning: Strategies for Improving Communication Efficiency. In NIPS Workshop on Private Multi-Party Machine Learning. Jakub Kone\u010dn\u00fd, H. Brendan McMahan, Felix X. Yu, Peter Richtarik, Ananda Theertha Suresh, and Dave Bacon. 2016. Federated Learning: Strategies for Improving Communication Efficiency. In NIPS Workshop on Private Multi-Party Machine Learning."},{"key":"e_1_3_2_2_23_1","volume-title":"Tara Javidi, and Farinaz Koushanfar.","author":"Lalitha Anusha","year":"2019","unstructured":"Anusha Lalitha , Osman Cihan Kilinc , Tara Javidi, and Farinaz Koushanfar. 2019 . Peer-to-peer Federated Learning on Graphs . arXiv preprint arXiv:1901.11173 (2019). Anusha Lalitha, Osman Cihan Kilinc, Tara Javidi, and Farinaz Koushanfar. 2019. Peer-to-peer Federated Learning on Graphs. arXiv preprint arXiv:1901.11173 (2019)."},{"key":"e_1_3_2_2_24_1","volume-title":"Modeling relation paths for knowledge base completion via joint adversarial training. Knowledge-Based Systems 201","author":"Li Chen","year":"2020","unstructured":"Chen Li , Xutan Peng , Shanghang Zhang , Hao Peng , S Yu Philip , Min He , Lin-feng Du, and Lihong Wang . 2020. Modeling relation paths for knowledge base completion via joint adversarial training. Knowledge-Based Systems 201 ( 2020 ). Chen Li, Xutan Peng, Shanghang Zhang, Hao Peng, S Yu Philip, Min He, Lin-feng Du, and Lihong Wang. 2020. Modeling relation paths for knowledge base completion via joint adversarial training. Knowledge-Based Systems 201 (2020)."},{"key":"e_1_3_2_2_25_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886624"},{"key":"e_1_3_2_2_26_1","doi-asserted-by":"crossref","first-page":"6418","DOI":"10.1609\/aaai.v35i7.16796","article-title":"KG-BART: Knowl-edge Graph-Augmented BART for Generative Commonsense Reasoning","volume":"35","author":"Liu Ye","year":"2021","unstructured":"Ye Liu , Yao Wan , Lifang He , Hao Peng , and S Yu Philip . 2021 . KG-BART: Knowl-edge Graph-Augmented BART for Generative Commonsense Reasoning . In Proceedings of the AAAI , Vol. 35. 6418 -- 6425 . Ye Liu, Yao Wan, Lifang He, Hao Peng, and S Yu Philip. 2021. KG-BART: Knowl-edge Graph-Augmented BART for Generative Commonsense Reasoning. In Proceedings of the AAAI, Vol. 35. 6418--6425.","journal-title":"Proceedings of the AAAI"},{"key":"e_1_3_2_2_27_1","first-page":"2524","article-title":"Towards fair and privacy-preserving federated deep models","volume":"31","author":"Lyu Lingjuan","year":"2020","unstructured":"Lingjuan Lyu , Jiangshan Yu , Karthik Nandakumar , Yitong Li , Xingjun Ma , Jiong Jin , Han Yu , and Kee Siong Ng . 2020 . Towards fair and privacy-preserving federated deep models . IEEE TPDS 31 , 11 (2020), 2524 -- 2541 . Lingjuan Lyu, Jiangshan Yu, Karthik Nandakumar, Yitong Li, Xingjun Ma, Jiong Jin, Han Yu, and Kee Siong Ng. 2020. Towards fair and privacy-preserving federated deep models. IEEE TPDS 31, 11 (2020), 2524--2541.","journal-title":"IEEE TPDS"},{"key":"e_1_3_2_2_28_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.future.2020.07.041"},{"key":"e_1_3_2_2_29_1","volume-title":"Proceedings of the AISTATS. 1273--1282","author":"McMahan Brendan","year":"2017","unstructured":"Brendan McMahan , Eider Moore , Daniel Ramage , Seth Hampson , and Blaise Aguera y Arcas . 2017 . Communication-Efficient Learning of Deep Net-works from Decentralized Data . In Proceedings of the AISTATS. 1273--1282 . Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas. 2017. Communication-Efficient Learning of Deep Net-works from Decentralized Data. In Proceedings of the AISTATS. 1273--1282."},{"key":"e_1_3_2_2_30_1","volume-title":"Proceedings of S&P. 19--38","author":"Mohassel P.","unstructured":"P. Mohassel and Y. Zhang . 2017. SecureML: A System for Scalable Privacy-Preserving Machine Learning . In Proceedings of S&P. 19--38 . P. Mohassel and Y. Zhang. 2017. SecureML: A System for Scalable Privacy-Preserving Machine Learning. In Proceedings of S&P. 19--38."},{"key":"e_1_3_2_2_31_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2483592"},{"key":"e_1_3_2_2_32_1","doi-asserted-by":"publisher","DOI":"10.5555\/3104482.3104584"},{"key":"e_1_3_2_2_33_1","doi-asserted-by":"publisher","DOI":"10.5555\/1756123.1756146"},{"key":"e_1_3_2_2_34_1","volume-title":"Proceedings of ICLR.","author":"Papernot Nicolas","year":"2017","unstructured":"Nicolas Papernot , Mart\u00edn Abadi , \u00dalfar Erlingsson , Ian J. Goodfellow , and Kunal Talwar . 2017 . Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data . In Proceedings of ICLR. Nicolas Papernot, Mart\u00edn Abadi, \u00dalfar Erlingsson, Ian J. Goodfellow, and Kunal Talwar. 2017. Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data. In Proceedings of ICLR."},{"key":"e_1_3_2_2_35_1","volume-title":"REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs. In KDD. 2175--2184.","author":"Pei Shichao","year":"2020","unstructured":"Shichao Pei , Lu Yu , Guoxian Yu , and Xiangliang Zhang . 2020 . REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs. In KDD. 2175--2184. Shichao Pei, Lu Yu, Guoxian Yu, and Xiangliang Zhang. 2020. REA: Robust Cross-lingual Entity Alignment Between Knowledge Graphs. In KDD. 2175--2184."},{"key":"e_1_3_2_2_36_1","doi-asserted-by":"publisher","DOI":"10.1145\/96602.96604"},{"key":"e_1_3_2_2_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242667"},{"key":"e_1_3_2_2_38_1","volume-title":"Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197","author":"Sun Zhiqing","year":"2019","unstructured":"Zhiqing Sun , Zhi-Hong Deng , Jian-Yun Nie , and Jian Tang . 2019 . Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019). Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. 2019. Rotate: Knowledge graph embedding by relational rotation in complex space. arXiv preprint arXiv:1902.10197 (2019)."},{"key":"e_1_3_2_2_39_1","doi-asserted-by":"publisher","DOI":"10.5555\/3304222.3304381"},{"key":"e_1_3_2_2_40_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i01.5354"},{"key":"e_1_3_2_2_41_1","doi-asserted-by":"crossref","unstructured":"Bayu Distiawan Trisedya Jianzhong Qi and Rui Zhang. 2019. Entity Alignment between Knowledge Graphs Using Attribute Embeddings. In AAAI. 297--304.  Bayu Distiawan Trisedya Jianzhong Qi and Rui Zhang. 2019. Entity Alignment between Knowledge Graphs Using Attribute Embeddings. In AAAI. 297--304.","DOI":"10.1609\/aaai.v33i01.3301297"},{"key":"e_1_3_2_2_42_1","doi-asserted-by":"publisher","DOI":"10.5555\/3045390.3045609"},{"key":"e_1_3_2_2_43_1","doi-asserted-by":"publisher","DOI":"10.1145\/2629489"},{"key":"e_1_3_2_2_44_1","doi-asserted-by":"publisher","DOI":"10.1109\/TKDE.2017.2754499"},{"key":"e_1_3_2_2_45_1","doi-asserted-by":"publisher","DOI":"10.5555\/2893873.2894046"},{"key":"e_1_3_2_2_46_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367722.3367779"},{"key":"e_1_3_2_2_47_1","doi-asserted-by":"publisher","DOI":"10.5555\/3060621.3060804"},{"key":"e_1_3_2_2_48_1","first-page":"911","article-title":"Verifynet: Secure and verifiable federated learning","volume":"15","author":"Xu Guowen","year":"2019","unstructured":"Guowen Xu , Hongwei Li , Sen Liu , Kan Yang , and Xiaodong Lin . 2019 . Verifynet: Secure and verifiable federated learning . IEEE TIFS 15 (2019), 911 -- 926 . Guowen Xu, Hongwei Li, Sen Liu, Kan Yang, and Xiaodong Lin. 2019. Verifynet: Secure and verifiable federated learning. IEEE TIFS 15 (2019), 911--926.","journal-title":"IEEE TIFS"},{"key":"e_1_3_2_2_49_1","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v34i05.6476"},{"key":"e_1_3_2_2_50_1","volume-title":"COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. In AAAI. 3025--3032.","author":"Yang Kai","year":"2020","unstructured":"Kai Yang , Shaoqin Liu , Junfeng Zhao , Yasha Wang , and Bing Xie . 2020 . COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. In AAAI. 3025--3032. Kai Yang, Shaoqin Liu, Junfeng Zhao, Yasha Wang, and Bing Xie. 2020. COTSAE: CO-Training of Structure and Attribute Embeddings for Entity Alignment. In AAAI. 3025--3032."},{"key":"e_1_3_2_2_51_1","doi-asserted-by":"publisher","DOI":"10.1145\/3298981"},{"key":"e_1_3_2_2_52_1","volume-title":"KGSynNet: A Novel Entity Synonyms Discov-ery Framework with Knowledge Graph","author":"Yang Yiying","unstructured":"Yiying Yang , Xi Yin , Haiqin Yang , Xingjian Fei , Hao Peng , Kaijie Zhou , Kunfeng Lai , and Jianping Shen . 2021. KGSynNet: A Novel Entity Synonyms Discov-ery Framework with Knowledge Graph . In Proceedings of DASFAA. Springer International Publishing , Cham , 174--190. Yiying Yang, Xi Yin, Haiqin Yang, Xingjian Fei, Hao Peng, Kaijie Zhou, Kunfeng Lai, and Jianping Shen. 2021. KGSynNet: A Novel Entity Synonyms Discov-ery Framework with Knowledge Graph. In Proceedings of DASFAA. Springer International Publishing, Cham, 174--190."},{"key":"e_1_3_2_2_53_1","volume-title":"Proceedings of ICLR.","author":"Yoon Jinsung","unstructured":"Jinsung Yoon , James Jordon , and Mihaela van der Schaar. 2019. PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees . In Proceedings of ICLR. Jinsung Yoon, James Jordon, and Mihaela van der Schaar. 2019. PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees. In Proceedings of ICLR."},{"key":"e_1_3_2_2_54_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367722.3367800"},{"key":"e_1_3_2_2_55_1","doi-asserted-by":"publisher","DOI":"10.5555\/3171837.3171881"},{"key":"e_1_3_2_2_56_1","doi-asserted-by":"publisher","DOI":"10.5555\/3367243.3367308"}],"event":{"name":"CIKM '21: The 30th ACM International Conference on Information and Knowledge Management","location":"Virtual Event Queensland Australia","acronym":"CIKM '21","sponsor":["SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the 30th ACM International Conference on Information & Knowledge Management"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482252","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3459637.3482252","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:12Z","timestamp":1750191132000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3459637.3482252"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,10,26]]},"references-count":56,"alternative-id":["10.1145\/3459637.3482252","10.1145\/3459637"],"URL":"https:\/\/doi.org\/10.1145\/3459637.3482252","relation":{},"subject":[],"published":{"date-parts":[[2021,10,26]]},"assertion":[{"value":"2021-10-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}