{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T12:43:31Z","timestamp":1770295411136,"version":"3.49.0"},"publisher-location":"Cham","reference-count":34,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783031606250","type":"print"},{"value":"9783031606267","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-3-031-60626-7_2","type":"book-chapter","created":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T23:02:38Z","timestamp":1716073358000},"page":"22-40","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Treat Different Negatives Differently: Enriching Loss Functions with\u00a0Domain and\u00a0Range Constraints for\u00a0Link Prediction"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4682-422X","authenticated-orcid":false,"given":"Nicolas","family":"Hubert","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2017-8426","authenticated-orcid":false,"given":"Pierre","family":"Monnin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9876-6906","authenticated-orcid":false,"given":"Armelle","family":"Brun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4244-684X","authenticated-orcid":false,"given":"Davy","family":"Monticolo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,5,19]]},"reference":[{"issue":"12","key":"2_CR1","doi-asserted-by":"publisher","first-page":"8825","DOI":"10.1109\/TPAMI.2021.3124805","volume":"44","author":"M Ali","year":"2022","unstructured":"Ali, M., et al.: Bringing light into the dark: a large-scale evaluation of knowledge graph embedding models under a unified framework. IEEE Trans. Pattern Anal. Mach. Intell. 44(12), 8825\u20138845 (2022). https:\/\/doi.org\/10.1109\/TPAMI.2021.3124805","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"2_CR2","doi-asserted-by":"publisher","unstructured":"Balazevic, I., Allen, C., Hospedales, T.M.: TuckER: tensor factorization for knowledge graph completion. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, Hong Kong, China, November 3-7, 2019, pp. 5184\u20135193. Association for Computational Linguistics (2019). https:\/\/doi.org\/10.18653\/v1\/D19-1522","DOI":"10.18653\/v1\/D19-1522"},{"key":"2_CR3","unstructured":"Bordes, A., Usunier, N., Garc\u00eda-Dur\u00e1n, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Conference on Neural Information Processing Systems (NeurIPS), pp. 2787\u20132795 (2013)"},{"key":"2_CR4","doi-asserted-by":"publisher","unstructured":"Cai, L., Wang, W.Y.: KBGAN: adversarial learning for knowledge graph embeddings. In: Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, New Orleans, Louisiana, USA, June 1-6, 2018, Volume 1 (Long Papers), pp. 1470\u20131480. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/n18-1133","DOI":"10.18653\/v1\/n18-1133"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Cao, Z., Xu, Q., Yang, Z., Huang, Q.: ER: equivariance regularizer for knowledge graph completion. In: Thirty-Sixth AAAI Conference on Artificial Intelligence, AAAI 2022, Thirty-Fourth Conference on Innovative Applications of Artificial Intelligence, IAAI 2022, The Twelveth Symposium on Educational Advances in Artificial Intelligence, EAAI 2022 Virtual Event, February 22 - March 1, 2022, pp. 5512\u20135520. AAAI Press (2022)","DOI":"10.1609\/aaai.v36i5.20490"},{"key":"2_CR6","doi-asserted-by":"crossref","unstructured":"Cui, Z., Kapanipathi, P., Talamadupula, K., Gao, T., Ji, Q.: Type-augmented relation prediction in knowledge graphs. In: Thirty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2021, Thirty-Third Conference on Innovative Applications of Artificial Intelligence, IAAI 2021, The Eleventh Symposium on Educational Advances in Artificial Intelligence, EAAI 2021, Virtual Event, February 2-9, 2021, pp. 7151\u20137159. AAAI Press (2021)","DOI":"10.1609\/aaai.v35i8.16879"},{"key":"2_CR7","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/978-3-030-77385-4_26","volume-title":"The Semantic Web","author":"C d\u2019Amato","year":"2021","unstructured":"d\u2019Amato, C., Quatraro, N.F., Fanizzi, N.: Injecting background knowledge into embedding models for predictive tasks on knowledge graphs. In: Verborgh, R., et al. (eds.) The Semantic Web. Lecture Notes in Computer Science(), vol. 12731, pp. 441\u2013457. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-77385-4_26"},{"key":"2_CR8","unstructured":"Dettmers, T., Minervini, P., Stenetorp, P., Riedel, S.: Convolutional 2D knowledge graph embeddings. In: Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence, (AAAI-18), the 30th innovative Applications of Artificial Intelligence (IAAI-18), and the 8th AAAI Symposium on Educational Advances in Artificial Intelligence (EAAI-18), New Orleans, Louisiana, USA, February 2-7, 2018, pp. 1811\u20131818. AAAI Press (2018)"},{"key":"2_CR9","doi-asserted-by":"publisher","unstructured":"Ding, B., Wang, Q., Wang, B., Guo, L.: Improving knowledge graph embedding using simple constraints. In: Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Melbourne, Australia, July 15-20, 2018, Volume 1: Long Papers, pp. 110\u2013121. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/P18-1011","DOI":"10.18653\/v1\/P18-1011"},{"key":"2_CR10","doi-asserted-by":"publisher","unstructured":"Guo, S., Wang, Q., Wang, B., Wang, L., Guo, L.: Semantically smooth knowledge graph embedding. 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, July 26-31, 2015, Beijing, China, Volume 1: Long Papers, pp. 84\u201394. The Association for Computer Linguistics (2015). https:\/\/doi.org\/10.3115\/v1\/p15-1009","DOI":"10.3115\/v1\/p15-1009"},{"key":"2_CR11","unstructured":"Hubert, N., Monnin, P., Brun, A., Monticolo, D.: Knowledge graph embeddings for link prediction: beware of semantics! In: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG 2022) Co-Located with the 21th International Semantic Web Conference (ISWC 2022). Virtual Conference, online (2022)"},{"key":"2_CR12","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1007\/978-3-031-17105-5_5","volume-title":"Knowledge Engineering and Knowledge Management","author":"N Hubert","year":"2022","unstructured":"Hubert, N., Monnin, P., Brun, A., Monticolo, D.: New strategies for learning knowledge graph embeddings: the recommendation case. In: Corcho, O., Hollink, L., Kutz, O., Troquard, N., Ekaputra, F.J. (eds.) Knowledge Engineering and Knowledge Management. Lecture Notes in Computer Science(), vol. 13514, pp. 66\u201380. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-031-17105-5_5"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Hubert, N., Monnin, P., Brun, A., Monticolo, D.: Sem@$$k$$: is my knowledge graph embedding model semantic-aware? (2023)","DOI":"10.3233\/SW-233508"},{"key":"2_CR14","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1007\/978-3-030-88361-4_24","volume-title":"The Semantic Web - ISWC 2021","author":"N Jain","year":"2021","unstructured":"Jain, N., Tran, T., Gad-Elrab, M.H., Stepanova, D.: Improving knowledge graph embeddings with ontological reasoning. In: Hotho, A., et al. (eds.) The Semantic Web - ISWC 2021. Lecture Notes in Computer Science(), vol. 12922, pp. 410\u2013426. Springer, Cham (2021). https:\/\/doi.org\/10.1007\/978-3-030-88361-4_24"},{"issue":"2","key":"2_CR15","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1109\/TNNLS.2021.3070843","volume":"33","author":"S Ji","year":"2022","unstructured":"Ji, S., Pan, S., Cambria, E., Marttinen, P., Yu, P.S.: A survey on knowledge graphs: representation, acquisition, and applications. IEEE Trans. Neural Networks Learn. Syst. 33(2), 494\u2013514 (2022). https:\/\/doi.org\/10.1109\/TNNLS.2021.3070843","journal-title":"IEEE Trans. Neural Networks Learn. Syst."},{"key":"2_CR16","unstructured":"Kazemi, S.M., Poole, D.: Simple embedding for link prediction in knowledge graphs. In: Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, NeurIPS 2018, December 3-8, 2018, Montr\u00e9al, Canada, pp. 4289\u20134300 (2018)"},{"key":"2_CR17","unstructured":"Kotnis, B., Nastase, V.: Analysis of the impact of negative sampling on link prediction in knowledge graphs. arXiv preprint: arXiv:1708.06816 (2017)"},{"key":"2_CR18","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"640","DOI":"10.1007\/978-3-319-25007-6_37","volume-title":"The Semantic Web - ISWC 2015","author":"D Krompa\u00df","year":"2015","unstructured":"Krompa\u00df, D., Baier, S., Tresp, V.: Type-constrained representation learning in knowledge graphs. In: Arenas, M., et al. (eds.) The Semantic Web - ISWC 2015. Lecture Notes in Computer Science(), vol. 9366, pp. 640\u2013655. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-25007-6_37"},{"key":"2_CR19","doi-asserted-by":"publisher","unstructured":"Lv, X., Hou, L., Li, J., Liu, Z.: Differentiating concepts and instances for knowledge graph embedding. In: Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, October 31 - November 4, 2018, pp. 1971\u20131979. Association for Computational Linguistics (2018). https:\/\/doi.org\/10.18653\/v1\/d18-1222","DOI":"10.18653\/v1\/d18-1222"},{"key":"2_CR20","series-title":"Lecture Notes in Computer Science()","doi-asserted-by":"publisher","first-page":"668","DOI":"10.1007\/978-3-319-71249-9_40","volume-title":"Machine Learning and Knowledge Discovery in Databases","author":"P Minervini","year":"2017","unstructured":"Minervini, P., Costabello, L., Mu\u00f1oz, E., Nov\u00e1cek, V., Vandenbussche, P.: Regularizing knowledge graph embeddings via equivalence and inversion axioms. In: Ceci, M., Hollmen, J., Todorovski, L., Vens, C., Dzeroski, S. (eds.) Machine Learning and Knowledge Discovery in Databases. Lecture Notes in Computer Science(), vol. 10534, pp. 668\u2013683. Springer, Cham (2017). https:\/\/doi.org\/10.1007\/978-3-319-71249-9_40"},{"key":"2_CR21","doi-asserted-by":"publisher","unstructured":"Mohamed, S.K., Mu\u00f1oz, E., Novacek, V.: On training knowledge graph embedding models. Information 12(4) (2021). https:\/\/doi.org\/10.3390\/info12040147","DOI":"10.3390\/info12040147"},{"key":"2_CR22","unstructured":"Mohamed, S.K., Nov\u00e1cek, V., Vandenbussche, P., Mu\u00f1oz, E.: Loss functions in knowledge graph embedding models. In: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG2019) Co-located with the 16th Extended Semantic Web Conference 2019 (ESWC 2019), Portoroz, Slovenia, June 2, 2019. CEUR Workshop Proceedings, vol.\u00a02377, pp. 1\u201310. CEUR-WS.org (2019)"},{"key":"2_CR23","doi-asserted-by":"publisher","unstructured":"Niu, G., Li, B., Zhang, Y., Pu, S., Li, J.: AutoETER: automated entity type representation with relation-aware attention for knowledge graph embedding. In: Findings of the Association for Computational Linguistics: EMNLP 2020, Online Event, 16-20 November 2020. Findings of ACL, vol. EMNLP 2020, pp. 1172\u20131181. Association for Computational Linguistics (2020). https:\/\/doi.org\/10.18653\/v1\/2020.findings-emnlp.105","DOI":"10.18653\/v1\/2020.findings-emnlp.105"},{"issue":"2","key":"2_CR24","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. Discovery Data 15(2), 1\u201349 (2021)","journal-title":"ACM Trans. Knowl. Discovery Data"},{"key":"2_CR25","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":"MS Schlichtkrull","year":"2018","unstructured":"Schlichtkrull, M.S., Kipf, T.N., Bloem, P., van den Berg, R., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks. In: Gangemi, A., et al. (eds.) The Semantic Web. Lecture Notes in Computer Science(), vol. 10843, pp. 593\u2013607. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-319-93417-4_38"},{"key":"2_CR26","unstructured":"Sun, Z., Deng, Z., Nie, J., Tang, J.: Rotate: knowledge graph embedding by relational rotation in complex space. In: 7th International Conference on Learning Representations, ICLR (2019)"},{"key":"2_CR27","unstructured":"Trouillon, T., Welbl, J., Riedel, S., Gaussier, \u00c9., Bouchard, G.: Complex embeddings for simple link prediction. In: Proceedings of the 33rd International Conference on Machine Learning, ICML, vol.\u00a048, pp. 2071\u20132080 (2016)"},{"issue":"12","key":"2_CR28","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.3390\/electronics10121407","volume":"10","author":"P Wang","year":"2021","unstructured":"Wang, P., Zhou, J., Liu, Y., Zhou, X.: TransET: knowledge graph embedding with entity types. Electronics 10(12), 1407 (2021)","journal-title":"Electronics"},{"issue":"12","key":"2_CR29","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 Trans. Knowl. Data Eng. 29(12), 2724\u20132743 (2017)","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"2_CR30","doi-asserted-by":"crossref","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, pp. 1112\u20131119 (2014)","DOI":"10.1609\/aaai.v28i1.8870"},{"key":"2_CR31","unstructured":"Weyns, M., Bonte, P., Steenwinckel, B., Turck, F.D., Ongenae, F.: Conditional constraints for knowledge graph embeddings. In: Proceedings of the Workshop on Deep Learning for Knowledge Graphs (DL4KG@ISWC), vol.\u00a02635 (2020)"},{"key":"2_CR32","unstructured":"Xie, R., Liu, Z., Sun, M.: Representation learning of knowledge graphs with hierarchical types. In: Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence, IJCAI 2016, New York, NY, USA, 9-15 July 2016, pp. 2965\u20132971. IJCAI\/AAAI Press (2016)"},{"key":"2_CR33","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":"2_CR34","doi-asserted-by":"publisher","unstructured":"Zhang, Y., Yao, Q., Shao, Y., Chen, L.: NSCaching: simple and efficient negative sampling for knowledge graph embedding. In: 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019, pp. 614\u2013625. IEEE (2019). https:\/\/doi.org\/10.1109\/ICDE.2019.00061","DOI":"10.1109\/ICDE.2019.00061"}],"container-title":["Lecture Notes in Computer Science","The Semantic Web"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-60626-7_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,18]],"date-time":"2024-05-18T23:03:27Z","timestamp":1716073407000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-60626-7_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031606250","9783031606267"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-60626-7_2","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"19 May 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ESWC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Semantic Web Conference","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hersonissos","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":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 May 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 May 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"21","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"esws2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/2024.eswc-conferences.org\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}