{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T19:12:15Z","timestamp":1743016335213,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031466731"},{"type":"electronic","value":"9783031466748"}],"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\/978-3-031-46674-8_19","type":"book-chapter","created":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:02:29Z","timestamp":1699102949000},"page":"268-283","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning Knowledge Representation with\u00a0Entity Concept Information"],"prefix":"10.1007","author":[{"given":"Yuanbo","family":"Xu","sequence":"first","affiliation":[]},{"given":"Lin","family":"Yue","sequence":"additional","affiliation":[]},{"given":"Hangtong","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Yongjian","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,5]]},"reference":[{"key":"19_CR1","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":"19_CR2","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":"19_CR3","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":"19_CR4","doi-asserted-by":"crossref","unstructured":"Ding, J., Ma, S., Jia, W., Guo, M.: Jointly modeling structural and textual representation for knowledge graph completion in zero-shot scenario. In: Proceedings of Big Data, pp. 369\u2013384 (2018)","DOI":"10.1007\/978-3-319-96890-2_31"},{"key":"19_CR5","unstructured":"Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Proceedings of NIPS, pp. 4289\u20134300 (2018)"},{"key":"19_CR6","doi-asserted-by":"crossref","unstructured":"Kim, Y.: Convolutional neural networks for sentence classification. In: Proceedings of the EMNLP, pp. 1746\u20131751 (2014)","DOI":"10.3115\/v1\/D14-1181"},{"key":"19_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 Conference on Artificial Intelligence, pp. 2181\u20132187 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"19_CR8","unstructured":"Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. In: Proceedings of ICLR (2013)"},{"key":"19_CR9","unstructured":"Nguyen, D.Q., Vu, T., Nguyen, T.D., Phung, D.: QuatRE: relation-aware quaternions for knowledge graph embeddings. CoRR abs\/2009.12517 (2020)"},{"key":"19_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":"19_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":"19_CR12","unstructured":"Shi, B., Weninger, T.: Open-world knowledge graph completion. CoRR abs\/1711.03438 (2017)"},{"key":"19_CR13","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":"19_CR14","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":"19_CR15","doi-asserted-by":"crossref","unstructured":"Wang, B., Shen, T., Long, G., Zhou, T., Wang, Y., Chang, Y.: Structure-augmented text representation learning for efficient knowledge graph completion. In: WWW 2021: The Web Conference 2021, Virtual Event\/Ljubljana, Slovenia, 19\u201323 April 2021, pp. 1737\u20131748. ACM\/IW3C2 (2021)","DOI":"10.1145\/3442381.3450043"},{"key":"19_CR16","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":"19_CR17","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":"19_CR18","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":"19_CR19","unstructured":"Yao, L., Mao, C., Luo, Y.: KG-BERT: BERT for knowledge graph completion. CoRR abs\/1909.03193 (2019)"},{"key":"19_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"263","DOI":"10.1007\/978-3-319-09912-5_22","volume-title":"Active Media Technology","author":"Y Zeng","year":"2014","unstructured":"Zeng, Y., Zhang, T., Hao, H.: Active recommendation of tourist attractions based on visitors interests and semantic relatedness. In: \u015al\u0229zak, D., Schaefer, G., Vuong, S.T., Kim, Y.-S. (eds.) AMT 2014. LNCS, vol. 8610, pp. 263\u2013273. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-09912-5_22"},{"key":"19_CR21","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"},{"key":"19_CR22","doi-asserted-by":"crossref","unstructured":"Zhang, N., et al.: AliCG: fine-grained and evolvable conceptual graph construction for semantic search at Alibaba (2021)","DOI":"10.1145\/3447548.3467057"}],"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\/978-3-031-46674-8_19","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,4]],"date-time":"2023-11-04T13:17:41Z","timestamp":1699103861000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46674-8_19"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031466731","9783031466748"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46674-8_19","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)"}}]}}