{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,9,13]],"date-time":"2025-09-13T16:22:08Z","timestamp":1757780528628,"version":"3.40.3"},"publisher-location":"Cham","reference-count":24,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031442155"},{"type":"electronic","value":"9783031442162"}],"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\/978-3-031-44216-2_35","type":"book-chapter","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:02:58Z","timestamp":1695279778000},"page":"423-434","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Heterogeneous Graph Neural Network Knowledge Graph Completion Model Based on\u00a0Improved Attention Mechanism"],"prefix":"10.1007","author":[{"given":"Junkang","family":"Shi","sequence":"first","affiliation":[]},{"given":"Ming","family":"Li","sequence":"additional","affiliation":[]},{"given":"Jing","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,9,22]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Bala\u017eevi\u0107, I., Allen, C., Hospedales, T.M.: Tucker: tensor factorization for knowledge graph completion. arXiv preprint arXiv:1901.09590 (2019)","DOI":"10.18653\/v1\/D19-1522"},{"key":"35_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, vol. 26 (2013)"},{"key":"35_CR3","unstructured":"Burstein, J., Doran, C., Solorio, T.: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, vol. 1 (Long and Short Papers) (2019)"},{"key":"35_CR4","doi-asserted-by":"crossref","unstructured":"\u00c7avu\u015fo\u011flu, I., Pielka, M., Sifa, R.: Adapting established text representations for predicting review sentiment in Turkish. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 755\u2013756. IEEE (2020)","DOI":"10.1109\/DSAA49011.2020.00100"},{"key":"35_CR5","doi-asserted-by":"crossref","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32 (2018)","DOI":"10.1609\/aaai.v32i1.11573"},{"key":"35_CR6","unstructured":"Guo, L., Sun, Z., Hu, W.: Learning to exploit long-term relational dependencies in knowledge graphs. In: International Conference on Machine Learning, pp. 2505\u20132514. PMLR (2019)"},{"key":"35_CR7","unstructured":"Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. Advances in Neural Information Processing Systems, vol. 31 (2018)"},{"key":"35_CR8","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 the AAAI Conference on Artificial Intelligence, vol. 29 (2015)","DOI":"10.1609\/aaai.v29i1.9491"},{"key":"35_CR9","unstructured":"Liu, H., Wu, Y., Yang, Y.: Analogical inference for multi-relational embeddings. In: International Conference on Machine Learning, pp. 2168\u20132178. PMLR (2017)"},{"key":"35_CR10","doi-asserted-by":"publisher","first-page":"20840","DOI":"10.1109\/ACCESS.2021.3055529","volume":"9","author":"X Liu","year":"2021","unstructured":"Liu, X., Tan, H., Chen, Q., Lin, G.: RAGAT: relation aware graph attention network for knowledge graph completion. IEEE Access 9, 20840\u201320849 (2021)","journal-title":"IEEE Access"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Nguyen, D.Q., Nguyen, T.D., Nguyen, D.Q., Phung, D.: A novel embedding model for knowledge base completion based on convolutional neural network. arXiv preprint arXiv:1712.02121 (2017)","DOI":"10.18653\/v1\/N18-2053"},{"key":"35_CR12","unstructured":"Nickel, M., Tresp, V., Kriegel, H.P., et al.: A three-way model for collective learning on multi-relational data. In: ICML, vol. 11, pp. 3104482\u20133104584 (2011)"},{"issue":"2","key":"35_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3424672","volume":"15","author":"A Rossi","year":"2021","unstructured":"Rossi, A., Barbosa, D., Firmani, D., Matinata, A., Merialdo, P.: Knowledge graph embedding for link prediction: a comparative analysis. ACM Trans. Knowl. Discov. Data (TKDD) 15(2), 1\u201349 (2021)","journal-title":"ACM Trans. Knowl. Discov. Data (TKDD)"},{"key":"35_CR14","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"593","DOI":"10.1007\/978-3-319-93417-4_38","volume-title":"The Semantic Web","author":"M Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., van\u00a0den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) ESWC 2018. LNCS, vol. 10843, pp. 593\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Shang, C., Tang, Y., Huang, J., Bi, J., He, X., Zhou, B.: End-to-end structure-aware convolutional networks for knowledge base completion. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3060\u20133067 (2019)","DOI":"10.1609\/aaai.v33i01.33013060"},{"key":"35_CR16","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071\u20132080. PMLR (2016)"},{"key":"35_CR17","doi-asserted-by":"crossref","unstructured":"Vashishth, S., Sanyal, S., Nitin, V., Agrawal, N., Talukdar, P.: InteractE: improving convolution-based knowledge graph embeddings by increasing feature interactions. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3009\u20133016 (2020)","DOI":"10.1609\/aaai.v34i03.5694"},{"key":"35_CR18","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks. arXiv preprint arXiv:1710.10903 (2017)"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Wang, X., et al.: Heterogeneous graph attention network. In: The World Wide Web Conference, pp. 2022\u20132032 (2019)","DOI":"10.1145\/3308558.3313562"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Wang, Y., Jin, H., et al.: A new concept of knowledge based question answering (KBQA) system for multi-hop reasoning. In: Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pp. 4007\u20134017 (2022)","DOI":"10.18653\/v1\/2022.naacl-main.294"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhang, J., Feng, J., Chen, Z.: Knowledge graph embedding by translating on hyperplanes. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 28 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Wei, Y., Chaudhary, V.: The directionality function defect of performance evaluation method in regression neural network for stock price prediction. In: 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA), pp. 769\u2013770. IEEE (2020)","DOI":"10.1109\/DSAA49011.2020.00108"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Zhang, W., Paudel, B., Zhang, W., Bernstein, A., Chen, H.: Interaction embeddings for prediction and explanation in knowledge graphs. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 96\u2013104 (2019)","DOI":"10.1145\/3289600.3291014"},{"key":"35_CR24","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Cai, J., Zhang, Y., Wang, J.: Learning hierarchy-aware knowledge graph embeddings for link prediction. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 3065\u20133072 (2020)","DOI":"10.1609\/aaai.v34i03.5701"}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-44216-2_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T07:08:21Z","timestamp":1695280101000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-44216-2_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031442155","9783031442162"],"references-count":24,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-44216-2_35","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":"22 September 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Heraklion","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Greece","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":"26 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"32","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2023\/","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":"easyacademia.org","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"947","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":"426","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":"22","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":"45% - 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":"2.4","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":"4","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)"}},{"value":"type of other papers accepted  : 9 Abstract","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)"}}]}}