{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T10:11:10Z","timestamp":1743070270239,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783319936970"},{"type":"electronic","value":"9783319936987"}],"license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"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":[[2018]]},"DOI":"10.1007\/978-3-319-93698-7_48","type":"book-chapter","created":{"date-parts":[[2018,6,11]],"date-time":"2018-06-11T19:30:25Z","timestamp":1528745425000},"page":"624-638","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning Knowledge Graph Embeddings via Generalized Hyperplanes"],"prefix":"10.1007","author":[{"given":"Qiannan","family":"Zhu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiaofei","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"JianLong","family":"Tan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ping","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Guo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2018,6,12]]},"reference":[{"key":"48_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: ACM SIGMOD International Conference on Management of Data, SIGMOD 2008, Vancouver, BC, Canada, June, pp. 1247\u20131250 (2008)","DOI":"10.1145\/1376616.1376746"},{"issue":"2","key":"48_CR2","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1007\/s10994-013-5363-6","volume":"94","author":"Antoine Bordes","year":"2013","unstructured":"Bordes, A., Weston, X.G.J., Bengio, Y.: A semantic matching energy function for learning with multi-relational data. CoRR abs\/1301.3485 (2013)","journal-title":"Machine Learning"},{"key":"48_CR3","unstructured":"Bordes, A., Glorot, X., Weston, J.: Joint learning of words and meaning representations for open-text semantic parsing. In: International Conference on Artificial Intelligence and Statistics (2012)"},{"key":"48_CR4","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held, Lake Tahoe, Nevada, United States, 5\u20138 December 2013, pp. 2787\u20132795 (2013)"},{"key":"48_CR5","doi-asserted-by":"crossref","unstructured":"Bordes, A., Weston, J., Collobert, R., Bengio, Y.: Learning structured embeddings of knowledge bases. In: AAAI Conference on Artificial Intelligence, AAAI 2011, San Francisco, California, USA, August 2011","DOI":"10.1609\/aaai.v25i1.7917"},{"key":"48_CR6","series-title":"Lecture Notes in Computer Science (Lecture Notes in Artificial Intelligence)","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1007\/978-3-662-44848-9_11","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"A Bordes","year":"2014","unstructured":"Bordes, A., Weston, J., Usunier, N.: Open question answering with weakly supervised embedding models. In: Calders, T., Esposito, F., H\u00fcllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8724, pp. 165\u2013180. Springer, Heidelberg (2014). https:\/\/doi.org\/10.1007\/978-3-662-44848-9_11"},{"key":"48_CR7","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1007\/978-3-7908-2604-3_16","volume-title":"Proceedings of COMPSTAT 2010","author":"L Bottou","year":"2010","unstructured":"Bottou, L.: Large-scale machine learning with stochastic gradient descent. In: Lechevallier, Y., Saporta, G. (eds.) Proceedings of COMPSTAT 2010, pp. 177\u2013186. Springer, Heidelberg (2010). https:\/\/doi.org\/10.1007\/978-3-7908-2604-3_16"},{"key":"48_CR8","unstructured":"Hoffmann, R., Zhang, C., Ling, X., Zettlemoyer, L., Weld, D.S.: Knowledge-based weak supervision for information extraction of overlapping relations. In: Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 541\u2013550 (2011)"},{"key":"48_CR9","unstructured":"Jenatton, R., Roux, N.L., Bordes, A., Obozinski, G.: A latent factor model for highly multi-relational data. In: International Conference on Neural Information Processing Systems, pp. 3167\u20133175 (2012)"},{"key":"48_CR10","doi-asserted-by":"crossref","unstructured":"Ji, G., He, S., Xu, L., Liu, K., Zhao, J.: Knowledge graph embedding via dynamic mapping matrix. In: Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing, ACL 2015, Beijing, China, 26\u201331 July 2015, vol. 1: Long Papers, pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"48_CR11","unstructured":"Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, USA, 25\u201330 January 2015, pp. 2181\u20132187 (2015)"},{"key":"48_CR12","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space (2013)"},{"key":"48_CR13","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 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held, Lake Tahoe, Nevada, United States, 5\u20138 December 2013, pp. 3111\u20133119 (2013)"},{"issue":"11","key":"48_CR14","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1145\/219717.219748","volume":"38","author":"GA Miller","year":"1995","unstructured":"Miller, G.A.: WordNet: a lexical database for English. Commun. ACM 38(11), 39\u201341 (1995)","journal-title":"Commun. ACM"},{"key":"48_CR15","unstructured":"Nickel, M., Tresp, V., Kriegel, H.: A three-way model for collective learning on multi-relational data. In: Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, 28 June\u20132 July 2011, pp. 809\u2013816 (2011)"},{"key":"48_CR16","unstructured":"Socher, R., Chen, D., Manning, C.D., Ng, A.Y.: Reasoning with neural tensor networks for knowledge base completion. In: Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a Meeting Held, Lake Tahoe, Nevada, United States, 5\u20138 December 2013, pp. 926\u2013934 (2013)"},{"key":"48_CR17","unstructured":"Sutskever, I., Salakhutdinov, R., Tenenbaum, J.B.: Modelling relational data using Bayesian clustered tensor factorization. In: Advances in Neural Information Processing Systems 22: Conference on Neural Information Processing Systems 2009. Proceedings of A Meeting Held, Vancouver, British Columbia, Canada, 7\u201310 December 2009, pp. 1821\u20131828 (2009)"},{"key":"48_CR18","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, Qu\u00e9bec City, Qu\u00e9bec, Canada, 27\u201331 July 2014, pp. 1112\u20131119 (2014)"},{"key":"48_CR19","doi-asserted-by":"crossref","unstructured":"Zhou, X., Zhu, Q., Liu, P., Guo, L.: Learning knowledge embeddings by combining limit-based scoring loss. In: CIKM 2017, pp. 1009\u20131018 (2017)","DOI":"10.1145\/3132847.3132939"}],"container-title":["Lecture Notes in Computer Science","Computational Science \u2013 ICCS 2018"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-319-93698-7_48","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,3]],"date-time":"2023-09-03T04:02:10Z","timestamp":1693713730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-319-93698-7_48"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018]]},"ISBN":["9783319936970","9783319936987"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-319-93698-7_48","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2018]]},"assertion":[{"value":"12 June 2018","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICCS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Computational Science","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Wuxi","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":"2018","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11 June 2018","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 June 2018","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iccs-computsci2018","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.iccs-meeting.org\/iccs2018\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"406","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":"148","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":"60","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":"36% - 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":"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)"}},{"value":"Acceptance rate in the Main Track is 28%. Acceptance rate in the workshops is 43%. A high acceptance rate in the workshops is explained by the nature of these thematic sessions, where many experts in a particular field are personally invited by workshop organisers to participate in their sessions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}