{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T23:08:51Z","timestamp":1778368131005,"version":"3.51.4"},"publisher-location":"Cham","reference-count":38,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032191014","type":"print"},{"value":"9783032191021","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"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":[[2026]]},"DOI":"10.1007\/978-3-032-19102-1_38","type":"book-chapter","created":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:13:21Z","timestamp":1778364801000},"page":"623-638","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Task-Agnostic Contrastive Pretraining for\u00a0Relational Deep Learning"],"prefix":"10.1007","author":[{"given":"Jakub","family":"Pele\u0161ka","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gustav","family":"\u0160\u00edr","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2026,5,10]]},"reference":[{"key":"38_CR1","unstructured":"Bahri, D., Jiang, H., Tay, Y., Metzler, D.: Scarf: self-supervised contrastive learning using random feature corruption. In: The Tenth International Conference on Learning Representations, ICLR 2022 (2022)"},{"issue":"4","key":"38_CR2","doi-asserted-by":"publisher","first-page":"526","DOI":"10.1162\/neco.1991.3.4.526","volume":"3","author":"P Baldi","year":"1991","unstructured":"Baldi, P., Pineda, F.: Contrastive learning and neural oscillations. Neural Comput. 3(4), 526\u2013545 (1991)","journal-title":"Neural Comput."},{"key":"38_CR3","unstructured":"Brody, S., Alon, U., Yahav, E.: How attentive are graph attention networks? In: International Conference on Learning Representations (2022)"},{"key":"38_CR4","doi-asserted-by":"crossref","unstructured":"Chamberlin, D.D., Boyce, R.F.: SEQUEL: a structured English query language. In: Proceedings of the 1974 ACM SIGFIDET (now SIGMOD) Workshop on Data Description, Access and Control, SIGFIDET 1974, pp. 249\u2013264. Association for Computing Machinery, New York, NY, USA (1974)","DOI":"10.1145\/800296.811515"},{"key":"38_CR5","unstructured":"Chen, K.Y., Chiang, P.H., Chou, H.R., Chen, T.W., Chang, T.H.: Trompt: towards a better deep neural network for tabular data (2023)"},{"key":"38_CR6","unstructured":"Chen, T., Kanatsoulis, C., Leskovec, J.: RelGNN: composite message passing for relational deep learning (2025). arXiv:2502.06784 [cs]"},{"issue":"6","key":"38_CR7","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1145\/362384.362685","volume":"13","author":"EF Codd","year":"1970","unstructured":"Codd, E.F.: A relational model of data for large shared data banks. Commun. ACM 13(6), 377\u2013387 (1970)","journal-title":"Commun. ACM"},{"key":"38_CR8","unstructured":"Codd, E.F.: The Relational Model for Database Management: Version 2. Addison-Wesley Longman Publishing Co., Inc. (1990)"},{"key":"38_CR9","doi-asserted-by":"crossref","unstructured":"Cropper, A., Duman\u010di\u0107, S., Muggleton, S.H.: Turning 30: new ideas in inductive logic programming. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 4833\u20134839 (2020)","DOI":"10.24963\/ijcai.2020\/673"},{"key":"38_CR10","unstructured":"Cvitkovic, M.: Supervised learning on relational databases with graph neural networks (2020)"},{"key":"38_CR11","unstructured":"Fey, M., et al.: Position: relational deep learning - graph representation learning on relational databases. In: Forty-first International Conference on Machine Learning (2024)"},{"key":"38_CR12","unstructured":"Gallier, J.H.: Logic for Computer Science: Foundations of Automatic Theorem Proving. Courier Dover Publications (2015)"},{"key":"38_CR13","unstructured":"Gorishniy, Y., Rubachev, I., Khrulkov, V., Babenko, A.: Revisiting deep learning models for tabular data. In: Proceedings of the 35th International Conference on Neural Information Processing Systems, NIPS 2021, pp. 18932\u201318943. Curran Associates Inc., Red Hook, NY, USA (2021)"},{"key":"38_CR14","unstructured":"Gutmann, M., Hyv\u00e4rinen, A.: Noise-contrastive estimation: a new estimation principle for unnormalized statistical models. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 297\u2013304. JMLR Workshop and Conference Proceedings (2010)"},{"key":"38_CR15","unstructured":"Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs. In: Advances in Neural Information Processing Systems, pp. 1024\u20131034 (2017)"},{"key":"38_CR16","doi-asserted-by":"crossref","unstructured":"Hamilton, W.L.: Graph Representation Learning. Morgan & Claypool Publishers (2020)","DOI":"10.1007\/978-3-031-01588-5"},{"key":"38_CR17","unstructured":"Hassani, K., Khasahmadi, A.H.: Contrastive multi-view representation learning on graphs. In: Proceedings of the 37th International Conference on Machine Learning, ICML 2020, vol.\u00a0119, pp. 4116\u20134126. JMLR.org (2020)"},{"key":"38_CR18","unstructured":"Hu, W., et al.: Strategies for pre-training graph neural networks. In: International Conference on Learning Representations (2020). https:\/\/openreview.net\/forum?id=HJlWWJSFDH"},{"key":"38_CR19","unstructured":"Hu, X., Tang, W., Hsieh, C.K., Shi, S.: Tabtransformer: tabular data modeling using contextual embeddings. arXiv preprint arXiv:2012.06678 (2020)"},{"key":"38_CR20","doi-asserted-by":"crossref","unstructured":"Hu, Z., Dong, Y., Wang, K., Sun, Y.: Heterogeneous graph transformer (2020)","DOI":"10.1145\/3366423.3380027"},{"key":"38_CR21","doi-asserted-by":"crossref","unstructured":"Jiang, X., Jia, T., Fang, Y., Shi, C., Lin, Z., Wang, H.: Pre-training on large-scale heterogeneous graph. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, KDD 2021, pp. 756\u2013766. Association for Computing Machinery, New York, NY, USA (2021)","DOI":"10.1145\/3447548.3467396"},{"key":"38_CR22","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: Bengio, Y., LeCun, Y. (eds.) 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, 7\u20139 May 2015, Conference Track Proceedings (2015)"},{"key":"38_CR23","doi-asserted-by":"crossref","unstructured":"Krogel, M.A., Rawles, S., \u017delezn\u00fd, F., Flach, P.A., Lavra\u010d, N., Wrobel, S.: Comparative Evaluation of Approaches to Propositionalization. Springer, Cham (2003)","DOI":"10.1007\/978-3-540-39917-9_14"},{"issue":"8","key":"38_CR24","doi-asserted-by":"publisher","first-page":"11157","DOI":"10.1109\/TNNLS.2023.3248871","volume":"35","author":"H Li","year":"2024","unstructured":"Li, H., Cao, J., Zhu, J., Luo, Q., He, S., Wang, X.: Augmentation-free graph contrastive learning of invariant-discriminative representations. IEEE Trans. Neural Netw. Learn. Syst. 35(8), 11157\u201311167 (2024)","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"38_CR25","doi-asserted-by":"publisher","first-page":"339","DOI":"10.1007\/978-3-642-14799-9_29","volume-title":"Electronic Government","author":"F Maali","year":"2010","unstructured":"Maali, F., Cyganiak, R., Peristeras, V.: Enabling interoperability of government data catalogues. In: Wimmer, M.A., Chappelet, J.L., Janssen, M., Scholl, H.J. (eds.) Electronic Government, pp. 339\u2013350. Springer, Berlin, Heidelberg (2010)"},{"key":"38_CR26","unstructured":"Motl, J., Schulte, O.: The CTU Prague relational learning repository. arXiv preprint arXiv:1511.03086 (2015)"},{"key":"38_CR27","doi-asserted-by":"crossref","unstructured":"Pele\u0161ka, J., \u0160\u00edr, G.: Redelex: a framework for relational deep learning exploration. In: Machine Learning and Knowledge Discovery in Databases, pp. 438\u2013456. Springer, Cham (2025)","DOI":"10.1007\/978-3-032-05981-9_26"},{"key":"38_CR28","doi-asserted-by":"publisher","unstructured":"Pele\u0161ka, J., \u0160\u00edr, G.: Tabular transformers meet relational databases. ACM Trans. Intell. Syst. Technol. 16(5) (2025). https:\/\/doi.org\/10.1145\/3749991","DOI":"10.1145\/3749991"},{"key":"38_CR29","unstructured":"Robinson, J., et al.: Relbench: a benchmark for deep learning on relational databases. In: The Thirty-Eight Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2024)"},{"key":"38_CR30","unstructured":"Rossi, E., Chamberlain, B., Frasca, F., Eynard, D., Monti, F., Bronstein, M.: Temporal graph networks for deep learning on dynamic graphs. arXiv preprint arXiv:2006.10637 (2020)"},{"key":"38_CR31","doi-asserted-by":"crossref","unstructured":"\u0160\u00edr, G.: Deep Learning with Relational Logic Representations. Czech Technical University (2021)","DOI":"10.3233\/FAIA357"},{"key":"38_CR32","unstructured":"Veli\u010dkovi\u0107, P., Cucurull, G., Casanova, A., Romero, A., Li\u00f2, P., Bengio, Y.: Graph attention networks. In: International Conference on Learning Representations (2018)"},{"key":"38_CR33","first-page":"2902","volume":"35","author":"Z Wang","year":"2022","unstructured":"Wang, Z., Sun, J.: TransTab: learning transferable tabular transformers across tables. Adv. Neural. Inf. Process. Syst. 35, 2902\u20132915 (2022)","journal-title":"Adv. Neural. Inf. Process. Syst."},{"key":"38_CR34","doi-asserted-by":"crossref","unstructured":"White, J.: PubMed 2.0. Med. Reference Serv. Q. 39(4), 382\u2013387 (2020)","DOI":"10.1080\/02763869.2020.1826228"},{"key":"38_CR35","doi-asserted-by":"crossref","unstructured":"Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., Philip, S.Y.: A comprehensive survey on graph neural networks. IEEE Trans. Neural Netw. Learn. Syst. (2020)","DOI":"10.1109\/TNNLS.2020.2978386"},{"key":"38_CR36","unstructured":"Zahradn\u00edk, L., Neumann, J., \u0160\u00edr, G.: A deep learning blueprint for relational databases. In: NeurIPS 2023 Second Table Representation Learning Workshop (2023)"},{"key":"38_CR37","unstructured":"Zhang, H., Gan, Q., Wipf, D., Zhang, W.: GFS: graph-based feature synthesis for prediction over relational databases. arXiv preprint arXiv:2312.02037 (2023)"},{"key":"38_CR38","unstructured":"Zhu, B., Shi, X., Erickson, N., Li, M., Karypis, G., Shoaran, M.: XTab: cross-table pretraining for tabular transformers. In: Proceedings of the 40th International Conference on Machine Learning, pp. 43181\u201343204. PMLR (2023). iSSN 2640-3498"}],"container-title":["Communications in Computer and Information Science","Machine Learning and Principles and Practice of Knowledge Discovery in Databases"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-19102-1_38","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,9]],"date-time":"2026-05-09T22:13:25Z","timestamp":1778364805000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-19102-1_38"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026]]},"ISBN":["9783032191014","9783032191021"],"references-count":38,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-19102-1_38","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"value":"1865-0929","type":"print"},{"value":"1865-0937","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026]]},"assertion":[{"value":"10 May 2026","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECML PKDD","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Porto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Portugal","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 September 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ecml2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ecmlpkdd.org\/2025\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}