{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T08:20:28Z","timestamp":1742977228239,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466632"},{"type":"electronic","value":"9783031466649"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023]]},"DOI":"10.1007\/978-3-031-46664-9_3","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"32-45","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Duet Representation Learning with\u00a0Entity Multi-attribute Information in\u00a0Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Yuanbo","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanbo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongjian","family":"Yang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hangtong","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Yue","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"3_CR1","doi-asserted-by":"crossref","unstructured":"Bollacker, K.D., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of ICMD, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"3_CR2","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Proceedings of NIPS, pp. 2787\u20132795 (2013)"},{"key":"3_CR3","doi-asserted-by":"crossref","unstructured":"Che, F., Zhang, D., Tao, J., Niu, M., Zhao, B.: Parame: regarding neural network parameters as relation embeddings for knowledge graph completion. In: Proceedings of AAAI, pp. 2774\u20132781 (2020)","DOI":"10.1609\/aaai.v34i03.5665"},{"key":"3_CR4","doi-asserted-by":"crossref","unstructured":"Chen, S., Liu, X., Gao, J., Jiao, J., Zhang, R., Ji, Y.: Hitter: Hierarchical transformers for knowledge graph embeddings. In: Proceedings of EMNLP, pp. 10395\u201310407 (2021)","DOI":"10.18653\/v1\/2021.emnlp-main.812"},{"key":"3_CR5","unstructured":"Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs, pp. 4289\u20134300 (2018)"},{"key":"3_CR6","unstructured":"Lin, Y., Liu, Z., Sun, M.: Knowledge representation learning with entities, attributes and relations. In: Proceedings of IJCAI, pp. 2866\u20132872 (2016)"},{"key":"3_CR7","doi-asserted-by":"crossref","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of AAAI, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"3_CR8","unstructured":"Mahdisoltani, F., Biega, J., Suchanek, F.M.: YAGO3: a knowledge base from multilingual wikipedias. In: Proceedings of IDSR (2015)"},{"key":"3_CR9","unstructured":"Nguyen, D.Q., Vu, T., Nguyen, T.D., Phung, D.: Quatre: relation-aware quaternions for knowledge graph embeddings. CoRR abs\/ arXiv: 2009.12517 (2020)"},{"key":"3_CR10","doi-asserted-by":"crossref","unstructured":"Shah, H., Villmow, J., Ulges, A., Schwanecke, U., Shafait, F.: An open-world extension to knowledge graph completion models. In: Proceedings of the AAAI, pp. 3044\u20133051 (2019)","DOI":"10.1609\/aaai.v33i01.33013044"},{"key":"3_CR11","doi-asserted-by":"crossref","unstructured":"Shen, Y., Li, Z., Wang, X., Li, J., Zhang, X.: Datatype-aware knowledge graph representation learning in hyperbolic space. In: Proceedings of CIKM, pp. 1630\u20131639 (2021)","DOI":"10.1145\/3459637.3482421"},{"key":"3_CR12","unstructured":"Shi, B., Weninger, T.: Open-world knowledge graph completion. CoRR abs\/ arXiv: 1711.03438 (2017)"},{"key":"3_CR13","doi-asserted-by":"crossref","unstructured":"Sun, Z., Hu, W., Zhang, Q., Qu, Y.: Bootstrapping entity alignment with knowledge graph embedding. In: Proceedings of IJCAI, pp. 4396\u20134402 (2018)","DOI":"10.24963\/ijcai.2018\/611"},{"key":"3_CR14","unstructured":"Sun, Z., Deng, Z., Nie, J., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: Proceedings of ICLR (2019)"},{"key":"3_CR15","doi-asserted-by":"crossref","unstructured":"Trisedya, B.D., Qi, J., Zhang, R.: Entity alignment between knowledge graphs using attribute embeddings. In: Proceedings of AAAI, pp. 297\u2013304 (2019)","DOI":"10.1609\/aaai.v33i01.3301297"},{"key":"3_CR16","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of ICML, pp. 2071\u20132080 (2016)"},{"key":"3_CR17","doi-asserted-by":"publisher","unstructured":"Wang, S., et al.: Knowledge graph representation via hierarchical hyperbolic neural graph embedding. In: 2021 IEEE International Conference on Big Data (Big Data), Orlando, FL, USA, 15\u201318 December 2021, pp. 540\u2013549. IEEE (2021). https:\/\/doi.org\/10.1109\/BigData52589.2021.9671651","DOI":"10.1109\/BigData52589.2021.9671651"},{"key":"3_CR18","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of AAAI, pp. 1112\u20131119 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"3_CR19","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Z., Jia, J., Luan, H., Sun, M.: Representation learning of knowledge graphs with entity descriptions. In: Proceedings of AAAI, pp. 2659\u20132665 (2016)","DOI":"10.1609\/aaai.v30i1.10329"},{"key":"3_CR20","doi-asserted-by":"crossref","unstructured":"Xie, R., Liu, Z., Luan, H., Sun, M.: Image-embodied knowledge representation learning. In: Proceedings of IJCAI, pp. 3140\u20133146 (2017)","DOI":"10.24963\/ijcai.2017\/438"},{"key":"3_CR21","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: Bengio, Y., LeCun, Y. (eds.) In Proceedings of ICLR (2015)"},{"key":"3_CR22","doi-asserted-by":"crossref","unstructured":"Yang, K., Liu, S., Zhao, J., Wang, Y., Xie, B.: COTSAE: co-training of structure and attribute embeddings for entity alignment. In: Proceedings of AAAI, pp. 3025\u20133032 (2020)","DOI":"10.1609\/aaai.v34i03.5696"},{"key":"3_CR23","doi-asserted-by":"crossref","unstructured":"Yin, J., Jiang, X., Lu, Z., Shang, L., Li, H., Li, X.: Neural generative question answering. In: Proceedings of IJCAI, pp. 2972\u20132978 (2016)","DOI":"10.18653\/v1\/W16-0106"},{"key":"3_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, F., Wang, X., Li, Z., Li, J.: Transrhs: A representation learning method for knowledge graphs with relation hierarchical structure. In: Proceedings of IJCAI, pp. 2987\u20132993 (2020)","DOI":"10.24963\/ijcai.2020\/413"}],"container-title":["Lecture Notes in Computer Science","Advanced Data Mining and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46664-9_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:09:37Z","timestamp":1699103377000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46664-9_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466632","9783031466649"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46664-9_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"5 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ADMA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Advanced Data Mining and Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenyang","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 August 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"adma2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/adma2023.uqcloud.net\/","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":"Yes. Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"503","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":"216","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":"43% - 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":"2.97","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":"3.77","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}