{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T07:17:14Z","timestamp":1761808634628,"version":"3.40.3"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031472398"},{"type":"electronic","value":"9783031472404"}],"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\/978-3-031-47240-4_13","type":"book-chapter","created":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:02:40Z","timestamp":1698825760000},"page":"234-252","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Entity-Relation Distribution-Aware Negative Sampling for\u00a0Knowledge Graph Embedding"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3541-9031","authenticated-orcid":false,"given":"Naimeng","family":"Yao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7895-9551","authenticated-orcid":false,"given":"Qing","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8917-2196","authenticated-orcid":false,"given":"Yi","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9215-4979","authenticated-orcid":false,"given":"Weihua","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1214-6317","authenticated-orcid":false,"given":"Quan","family":"Bai","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,10,27]]},"reference":[{"key":"13_CR1","doi-asserted-by":"crossref","unstructured":"Ahrabian, K., Feizi, A., Salehi, Y., Hamilton, W.L., Bose, A.J.: Structure aware negative sampling in knowledge graphs. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). pp. 6093\u20136101 (2020)","DOI":"10.18653\/v1\/2020.emnlp-main.492"},{"key":"13_CR2","unstructured":"Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. Advances in neural information processing systems 26 (2013)"},{"key":"13_CR3","doi-asserted-by":"crossref","unstructured":"Cai, L., Wang, W.Y.: Kbgan: Adversarial learning for knowledge graph embeddings. CoRR (2017)","DOI":"10.18653\/v1\/N18-1133"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhou, Y., Wu, D., Zhang, W., Zhou, Y., Li, B., Wang, W.: Imagine by reasoning: A reasoning-based implicit semantic data augmentation for long-tailed classification. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 356\u2013364 (2022)","DOI":"10.1609\/aaai.v36i1.19912"},{"key":"13_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"},{"issue":"7","key":"13_CR6","doi-asserted-by":"publisher","first-page":"1895","DOI":"10.1162\/089976698300017197","volume":"10","author":"TG Dietterich","year":"1998","unstructured":"Dietterich, T.G.: Approximate statistical tests for comparing supervised classification learning algorithms. Neural computation 10(7), 1895\u20131923 (1998)","journal-title":"Neural computation"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Guo, G., Ouyang, S., Yuan, F., Wang, X.: Approximating word ranking and negative sampling for word embedding. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. p. 4092\u20134098 (2018)","DOI":"10.24963\/ijcai.2018\/569"},{"key":"13_CR8","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 (volume 1: Long papers). pp. 687\u2013696 (2015)","DOI":"10.3115\/v1\/P15-1067"},{"key":"13_CR9","doi-asserted-by":"crossref","unstructured":"Kamigaito, H., Hayashi, K.: Unified interpretation of softmax cross-entropy and negative sampling: With case study for knowledge graph embedding. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 5517\u20135531 (2021)","DOI":"10.18653\/v1\/2021.acl-long.429"},{"key":"13_CR10","unstructured":"Kamigaito, H., Hayashi, K.: Comprehensive analysis of negative sampling in knowledge graph representation learning. In: International Conference on Machine Learning. pp. 10661\u201310675. PMLR (2022)"},{"key":"13_CR11","unstructured":"Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. Advances in neural information processing systems 31 (2018)"},{"key":"13_CR12","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. Technical report (2014)"},{"key":"13_CR13","unstructured":"Levy, O., Goldberg, Y.: Neural word embedding as implicit matrix factorization. Advances in neural information processing systems 27 (2014)"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Li, Z., Ji, J., Fu, Z., Ge, Y., Xu, S., Chen, C., Zhang, Y.: Efficient non-sampling knowledge graph embedding. In: Proceedings of the Web Conference 2021. pp. 1727\u20131736 (2021)","DOI":"10.1145\/3442381.3449859"},{"key":"13_CR15","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, January 25\u201330, 2015, Austin, Texas, USA. pp. 2181\u20132187 (2015)"},{"key":"13_CR16","unstructured":"Mahdisoltani, F., Biega, J., Suchanek, F.: Yago3: A knowledge base from multilingual wikipedias. In: 7th biennial conference on innovative data systems research. CIDR Conference (2014)"},{"key":"13_CR17","unstructured":"Mikolov, T., Sutskever, I., Chen, K., Corrado, G., Dean, J.: Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems 26 (2013)"},{"key":"13_CR18","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. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers). pp. 327\u2013333 (2018)","DOI":"10.18653\/v1\/N18-2053"},{"key":"13_CR19","doi-asserted-by":"crossref","unstructured":"Schlichtkrull, M., Kipf, T.N., Bloem, P., Berg, R.v.d., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: European semantic web conference. pp. 593\u2013607 (2018)","DOI":"10.1007\/978-3-319-93417-4_38"},{"key":"13_CR20","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":"13_CR21","unstructured":"Sun, Z., Deng, Z.H., Nie, J.Y., Tang, J.: Rotate: Knowledge graph embedding by relational rotation in complex space. In: International Conference on Learning Representations (2019)"},{"key":"13_CR22","doi-asserted-by":"crossref","unstructured":"Toutanova, K., Chen, D.: Observed versus latent features for knowledge base and text inference. In: Proceedings of the 3rd workshop on continuous vector space models and their compositionality. pp. 57\u201366 (2015)","DOI":"10.18653\/v1\/W15-4007"},{"key":"13_CR23","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":"13_CR24","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"},{"issue":"3","key":"13_CR25","doi-asserted-by":"publisher","first-page":"485","DOI":"10.3390\/sym13030485","volume":"13","author":"M Wang","year":"2021","unstructured":"Wang, M., Qiu, L., Wang, X.: A survey on knowledge graph embeddings for link prediction. Symmetry 13(3), 485 (2021)","journal-title":"Symmetry"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Wang, P., Li, S., et al.: Incorporating gan for negative sampling in knowledge representation learning. CoRR (2018)","DOI":"10.1609\/aaai.v32i1.11536"},{"issue":"12","key":"13_CR27","doi-asserted-by":"publisher","first-page":"2724","DOI":"10.1109\/TKDE.2017.2754499","volume":"29","author":"Q Wang","year":"2017","unstructured":"Wang, Q., Mao, Z., Wang, B., Guo, L.: Knowledge graph embedding: A survey of approaches and applications. IEEE Transactions on Knowledge and Data Engineering 29(12), 2724\u20132743 (2017)","journal-title":"IEEE Transactions on Knowledge and Data Engineering"},{"key":"13_CR28","doi-asserted-by":"crossref","unstructured":"Wang, Y., Ruffinelli, D., Gemulla, R., Broscheit, S., Meilicke, C.: On evaluating embedding models for knowledge base completion. Proceedings of the 4th Workshop on Representation Learning for NLP (RepL4NLP-2019) (2019)","DOI":"10.18653\/v1\/W19-4313"},{"key":"13_CR29","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":"13_CR30","unstructured":"Yang, B., Yih, W., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. In: 3rd International Conference on Learning Representations (ICLR) (2015)"},{"key":"13_CR31","doi-asserted-by":"crossref","unstructured":"Yang, Z., Ding, M., Zhou, C., Yang, H., Zhou, J., Tang, J.: Understanding negative sampling in graph representation learning. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining. pp. 1666\u20131676 (2020)","DOI":"10.1145\/3394486.3403218"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Zhang, N., Deng, S., Sun, Z., Wang, G., Chen, X., Zhang, W., Chen, H.: Long-tail relation extraction via knowledge graph embeddings and graph convolution networks. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 3016\u20133025 (2019)","DOI":"10.18653\/v1\/N19-1306"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Zhang, W., Paudel, B., Wang, L., Chen, J., Zhu, H., Zhang, W., Bernstein, A., Chen, H.: Iteratively learning embeddings and rules for knowledge graph reasoning. In: The World Wide Web Conference. pp. 2366\u20132377 (2019)","DOI":"10.1145\/3308558.3313612"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yao, Q., Shao, Y., Chen, L.: Nscaching: simple and efficient negative sampling for knowledge graph embedding. In: 2019 IEEE 35th International Conference on Data Engineering (ICDE). pp. 614\u2013625 (2019)","DOI":"10.1109\/ICDE.2019.00061"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web \u2013 ISWC 2023"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-47240-4_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,1]],"date-time":"2023-11-01T08:05:52Z","timestamp":1698825952000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-47240-4_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031472398","9783031472404"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-47240-4_13","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":"27 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ISWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Athens","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":"6 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"semweb2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iswc2023.semanticweb.org\/","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":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"248","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":"58","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":"23% - 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":"1","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)"}}]}}