{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,1,16]],"date-time":"2025-01-16T05:24:24Z","timestamp":1737005064799,"version":"3.33.0"},"reference-count":0,"publisher":"Cambridge University Press (CUP)","issue":"4","license":[{"start":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T00:00:00Z","timestamp":1736899200000},"content-version":"unspecified","delay-in-days":198,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["cambridge.org"],"crossmark-restriction":true},"short-container-title":["Theory and Practice of Logic Programming"],"published-print":{"date-parts":[[2024,7]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Many inductive logic programming (ILP) methods are incapable of learning programs from probabilistic background knowledge, for example, coming from sensory data or neural networks with probabilities. We propose Propper, which handles flawed and probabilistic background knowledge by extending ILP with a combination of neurosymbolic inference, a continuous criterion for hypothesis selection (binary cross-entropy) and a relaxation of the hypothesis constrainer (NoisyCombo). For relational patterns in noisy images, Propper can learn programs from as few as 8 examples. It outperforms binary ILP and statistical models such as a graph neural network.<\/jats:p>","DOI":"10.1017\/s1471068424000371","type":"journal-article","created":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T09:52:44Z","timestamp":1736934764000},"page":"628-643","update-policy":"https:\/\/doi.org\/10.1017\/policypage","source":"Crossref","is-referenced-by-count":0,"title":["Towards Probabilistic Inductive Logic Programming with Neurosymbolic Inference and Relaxation"],"prefix":"10.1017","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1301-3073","authenticated-orcid":false,"given":"FIEKE","family":"HILLERSTR\u00d6M","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6265-7276","authenticated-orcid":false,"given":"GERTJAN","family":"BURGHOUTS","sequence":"additional","affiliation":[]}],"member":"56","published-online":{"date-parts":[[2025,1,15]]},"container-title":["Theory and Practice of Logic Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.cambridge.org\/core\/services\/aop-cambridge-core\/content\/view\/S1471068424000371","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,15]],"date-time":"2025-01-15T09:52:44Z","timestamp":1736934764000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.cambridge.org\/core\/product\/identifier\/S1471068424000371\/type\/journal_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7]]},"references-count":0,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2024,7]]}},"alternative-id":["S1471068424000371"],"URL":"https:\/\/doi.org\/10.1017\/s1471068424000371","relation":{},"ISSN":["1471-0684","1475-3081"],"issn-type":[{"value":"1471-0684","type":"print"},{"value":"1475-3081","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,7]]},"assertion":[{"value":"\u00a9 The Author(s), 2025. Published by Cambridge University Press","name":"copyright","label":"Copyright","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http:\/\/creativecommons.org\/licenses\/by\/4.0\/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.","name":"license","label":"License","group":{"name":"copyright_and_licensing","label":"Copyright and Licensing"}},{"value":"This content has been made available to all.","name":"free","label":"Free to read"}]}}