{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,15]],"date-time":"2025-11-15T10:33:25Z","timestamp":1763202805871,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031354144"},{"type":"electronic","value":"9783031354151"}],"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\/978-3-031-35415-1_17","type":"book-chapter","created":{"date-parts":[[2023,9,27]],"date-time":"2023-09-27T06:03:15Z","timestamp":1695794595000},"page":"238-250","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Zero-Shot Entity Typing in\u00a0Knowledge Graphs"],"prefix":"10.1007","author":[{"given":"Shengye","family":"Zhou","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1367-7139","authenticated-orcid":false,"given":"Zhe","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0542-3761","authenticated-orcid":false,"given":"Kewen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0081-1703","authenticated-orcid":false,"given":"Zhiqiang","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,9,28]]},"reference":[{"key":"17_CR1","doi-asserted-by":"crossref","unstructured":"Bollacker, K., Evans, C., Paritosh, P., Sturge, T., Taylor, J.: Freebase: a collaboratively created graph database for structuring human knowledge. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"key":"17_CR2","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26 (2013)"},{"key":"17_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1007\/978-3-030-88361-4_9","volume-title":"The Semantic Web \u2013 ISWC 2021","author":"Z Chen","year":"2021","unstructured":"Chen, Z., Chen, J., Geng, Y., Pan, J.Z., Yuan, Z., Chen, H.: Zero-shot visual question answering using knowledge graph. In: Hotho, A., et al. (eds.) ISWC 2021. LNCS, vol. 12922, pp. 146\u2013162. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88361-4_9"},{"key":"17_CR4","doi-asserted-by":"crossref","unstructured":"Elsahar, H., Gravier, C., Laforest, F.: Zero-shot question generation from knowledge graphs for unseen predicates and entity types. arXiv preprint arXiv:1802.06842 (2018)","DOI":"10.18653\/v1\/N18-1020"},{"key":"17_CR5","doi-asserted-by":"publisher","first-page":"97","DOI":"10.1016\/j.patrec.2022.03.024","volume":"157","author":"X Ge","year":"2022","unstructured":"Ge, X., Wang, Y.C., Wang, B., Kuo, C.J.: Core: a knowledge graph entity type prediction method via complex space regression and embedding. Pattern Recogn. Lett. 157, 97\u2013103 (2022)","journal-title":"Pattern Recogn. Lett."},{"key":"17_CR6","doi-asserted-by":"crossref","unstructured":"Geng, Y., et al.: OntoZSL: ontology-enhanced zero-shot learning. In: Proceedings of the Web Conference 2021, pp. 3325\u20133336 (2021)","DOI":"10.1145\/3442381.3450042"},{"key":"17_CR7","unstructured":"Geng, Y., Chen, J., Chen, Z., Pan, J.Z., Yuan, Z., Chen, H.: K-ZSL: resources for knowledge-driven zero-shot learning. arXiv preprint arXiv:2106.15047 (2021)"},{"issue":"4","key":"17_CR8","doi-asserted-by":"publisher","first-page":"617","DOI":"10.3233\/SW-200404","volume":"12","author":"GA Gesese","year":"2021","unstructured":"Gesese, G.A., Biswas, R., Alam, M., Sack, H.: A survey on knowledge graph embeddings with literals: Which model links better literal-ly? Semantic Web 12(4), 617\u2013647 (2021)","journal-title":"Semantic Web"},{"key":"17_CR9","doi-asserted-by":"crossref","unstructured":"Hao, J., Chen, M., Yu, W., Sun, Y., Wang, W.: Universal representation learning of knowledge bases by jointly embedding instances and ontological concepts. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1709\u20131719 (2019)","DOI":"10.1145\/3292500.3330838"},{"issue":"2","key":"17_CR10","doi-asserted-by":"publisher","first-page":"167","DOI":"10.3233\/SW-140134","volume":"6","author":"J Lehmann","year":"2015","unstructured":"Lehmann, J., et al.: DBpedia-a large-scale, multilingual knowledge base extracted from wikipedia. Semantic web 6(2), 167\u2013195 (2015)","journal-title":"Semantic web"},{"key":"17_CR11","doi-asserted-by":"crossref","unstructured":"Nickel, M., Rosasco, L., Poggio, T.: Holographic embeddings of knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30 (2016)","DOI":"10.1609\/aaai.v30i1.10314"},{"key":"17_CR12","doi-asserted-by":"crossref","unstructured":"Pan, W., Wei, W., Mao, X.L.: Context-aware entity typing in knowledge graphs. arXiv preprint arXiv:2109.07990 (2021)","DOI":"10.18653\/v1\/2021.findings-emnlp.193"},{"key":"17_CR13","doi-asserted-by":"crossref","unstructured":"Pennington, J., Socher, R., Manning, C.D.: Glove: Global vectors for word representation. In: Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 1532\u20131543 (2014)","DOI":"10.3115\/v1\/D14-1162"},{"key":"17_CR14","doi-asserted-by":"crossref","unstructured":"Qin, P., Wang, X., Chen, W., Zhang, C., Xu, W., Wang, W.Y.: Generative adversarial zero-shot relational learning for knowledge graphs. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 8673\u20138680 (2020)","DOI":"10.1609\/aaai.v34i05.6392"},{"key":"17_CR15","doi-asserted-by":"crossref","unstructured":"Suchanek, F.M., Kasneci, G., Weikum, G.: YAGO: a core of semantic knowledge. In: Proceedings of the 16th International Conference on World Wide Web, pp. 697\u2013706 (2007)","DOI":"10.1145\/1242572.1242667"},{"key":"17_CR16","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)"},{"key":"17_CR17","unstructured":"Zeng, D., Liu, K., Lai, S., Zhou, G., Zhao, J.: Relation classification via convolutional deep neural network. In: Proceedings of COLING 2014, the 25th International Conference on Computational Linguistics: Technical Papers, pp. 2335\u20132344 (2014)"},{"key":"17_CR18","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhang, A., Xie, R., Liu, K., Wang, X.: Connecting embeddings for knowledge graph entity typing. arXiv preprint arXiv:2007.10873 (2020)","DOI":"10.18653\/v1\/2020.acl-main.572"},{"key":"17_CR19","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Zhou, H., Zhang, A., Xie, R., Li, Q., Zhuang, F.: Connecting embeddings based on multiplex relational graph attention networks for knowledge graph entity typing. IEEE Trans. Knowl. Data Eng. 35, 4608\u20134620 (2022)","DOI":"10.1109\/TKDE.2022.3142056"},{"key":"17_CR20","doi-asserted-by":"publisher","unstructured":"Zhu, G., Zhang, Z., Su, S.: Few-shot knowledge graph entity typing. In: Gama, J., Li, T., Yu, Y., Chen, E., Zheng, Y., Teng, F. (eds.) Advances in Knowledge Discovery and Data Mining. PAKDD 2022. LNCS, vol. 13280. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-05933-9_26","DOI":"10.1007\/978-3-031-05933-9_26"},{"key":"17_CR21","doi-asserted-by":"crossref","unstructured":"Zhuo, J., Zhu, Q., Yue, Y., Zhao, Y., Han, W.: A neighborhood-attention fine-grained entity typing for knowledge graph completion. In: Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining, pp. 1525\u20131533 (2022)","DOI":"10.1145\/3488560.3498395"}],"container-title":["Lecture Notes in Computer Science","Database Systems for Advanced Applications. DASFAA 2023 International Workshops"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-35415-1_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,26]],"date-time":"2023-10-26T13:05:09Z","timestamp":1698325509000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-35415-1_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031354144","9783031354151"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-35415-1_17","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":"28 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DASFAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Database Systems for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Tianjin","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":"17 April 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 April 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dasfaa2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.tjudb.cn\/dasfaa2023\/","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":"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":"652","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":"125","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":"66","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":"19% - 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","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":"7.3","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":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}