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Structure and Learning (Dagstuhl Seminar 21362) Dong, Tiansi Rettinger, Achim Tang, Jie Tversky, Barbara van Harmelen, Frank Knowledge graph Machine learning Neural-symbol unification This report documents the program and the outcomes of Dagstuhl Seminar 21362 "Structure and Learning", held from September 5 to 10, 2021. Structure and learning are among the most prominent topics in Artificial Intelligence (AI) today. Integrating symbolic and numeric inference was set as one of the next open AI problems at the Townhall meeting "A 20 Year Roadmap for AI" at AAAI 2019. In this Dagstuhl seminar, we discussed related problems from an interdiscplinary perspective, in particular, Cognitive Science, Cognitive Psychology, Physics, Computational Humor, Linguistic, Machine Learning, and AI. This report overviews presentations and working groups during the seminar, and lists two open problems. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Tiansi Dong and Achim Rettinger and Jie Tang and Barbara Tversky and Frank van Harmelen 2022 Is Part Of Dagstuhl Reports, Volume 11, Issue 8 (2022) Article Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/DagRep.11.8.11 urn:nbn:de:0030-drops-157670 https://drops.dagstuhl.de/entities/document/10.4230/DagRep.11.8.11 eng https://creativecommons.org/licenses/by/4.0/legalcode