{"type":"ScholarlyArticle","title":"ROCES: Robust Class Expression Synthesis in Description Logics via Iterative Sampling","authors":[{"name":"N'Dah Jean Kouagou","slug":"n-dah-jean-kouagou","given":"N'Dah Jean","family":"Kouagou","affiliation":"Data Science Research Group , Paderborn University , Germany"},{"name":"Stefan Heindorf","slug":"stefan-heindorf","given":"Stefan","family":"Heindorf","affiliation":"Data Science Research Group , Paderborn University , Germany"},{"name":"Caglar Demir","slug":"caglar-demir","given":"Caglar","family":"Demir","affiliation":"Data Science Research Group , Paderborn University , Germany"},{"name":"Axel-Cyrille Ngonga Ngomo","slug":"axel-cyrille-ngonga-ngomo","given":"Axel-Cyrille","family":"Ngonga Ngomo","affiliation":"Data Science Research Group , Paderborn University , Germany"}],"abstract":"We consider the problem of class expression learning using cardinality-minimal sets of examples. Recent class expression learning approaches employ deep neural networks and have demonstrated tremendous performance improvements in execution time and quality of the computed solutions. However, they lack generalization capabilities when it comes to the number of examples used in a learning problem, i.e., they often perform poorly on unseen learning problems where only a few examples are given. In this work, we propose a generalization of the classical class expression learning problem to address the limitations above. In short, our generalized learning problem (GLP) forces learning systems to solve the classical class expression learning problem using the smallest possible subsets of examples, thereby improving the learning systems' ability to solve unseen learning problems with arbitrary numbers of examples. Moreover, we develop ROCES, a learning algorithm for synthesis-based approaches to solve GLP. Experimental results suggest that post training, ROCES outperforms existing synthesis-based approaches on out-of-distribution learning problems while remaining highly competitive overall.","doi":"10.24963/ijcai.2024/479","url":"https://ijcai.org/proceedings/2024/479","pdf":"https://ijcai.org/proceedings/2024/0479.pdf","markdown":"https://ijcai.org/proceedings/2024/479.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/479","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":479,"pages":{"first":4335,"last":4343},"keywords":["Knowledge Representation and Reasoning: KRR: Learning and reasoning","Knowledge Representation and Reasoning: KRR: Description logics and ontologies","Machine Learning: ML: Explainable/Interpretable machine learning","Machine Learning: ML: Neuro-symbolic methods"],"videos":[],"references":[{"position":1,"raw":"[Agrawal et al., 1993] Rakesh Agrawal, Tomasz Imielinski, and Arun N. 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