{"type":"ScholarlyArticle","title":"Enabling Abductive Learning to Exploit Knowledge Graph","authors":[{"name":"Yu-Xuan Huang","slug":"yu-xuan-huang","given":"Yu-Xuan","family":"Huang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Zequn Sun","slug":"zequn-sun","given":"Zequn","family":"Sun","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Guangyao Li","slug":"guangyao-li","given":"Guangyao","family":"Li","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Xiaobin Tian","slug":"xiaobin-tian","given":"Xiaobin","family":"Tian","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Wang-Zhou Dai","slug":"wang-zhou-dai","given":"Wang-Zhou","family":"Dai","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Wei Hu","slug":"wei-hu","given":"Wei","family":"Hu","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Yuan Jiang","slug":"yuan-jiang","given":"Yuan","family":"Jiang","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"},{"name":"Zhi-Hua Zhou","slug":"zhi-hua-zhou","given":"Zhi-Hua","family":"Zhou","affiliation":"National Key Laboratory for Novel Software Technology , Nanjing University , Nanjing , China"}],"abstract":"Most systems integrating data-driven machine learning with knowledge-driven reasoning usually rely on a specifically designed knowledge base to enable efficient symbolic inference. However, it could be cumbersome for the nonexpert end-users to prepare such a knowledge base in real tasks. Recent years have witnessed the success of large-scale knowledge graphs, which could be ideal domain knowledge resources for real-world machine learning tasks. However, these large-scale knowledge graphs usually contain much information that is irrelevant to a specific learning task. Moreover, they often contain a certain degree of noise. Existing methods can hardly make use of them because the large-scale probabilistic logical inference is usually intractable. To address these problems, we present ABductive Learning with Knowledge Graph (ABL-KG) that can automatically mine logic rules from knowledge graphs during learning, using a knowledge forgetting mechanism for filtering out irrelevant information. Meanwhile, these rules can form a logic program that enables efficient joint optimization of the machine learning model and logic inference within the Abductive Learning (ABL) framework. Experiments on four different tasks show that ABL-KG can automatically extract useful rules from large-scale and noisy knowledge graphs, and significantly improve the performance of machine learning with only a handful of labeled data.","doi":"10.24963/ijcai.2023/427","url":"https://ijcai.org/proceedings/2023/427","pdf":"https://ijcai.org/proceedings/2023/0427.pdf","markdown":"https://ijcai.org/proceedings/2023/427.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/427","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Machine Learning","track":"Main Track","number":427,"pages":{"first":3839,"last":3847},"keywords":["Machine Learning: ML: Knowledge-aided learning","Machine Learning: ML: Weakly supervised learning","Knowledge Representation and Reasoning: KRR: Diagnosis and abductive reasoning"],"videos":[],"references":[{"position":1,"raw":"[Bengio, 2017] Yoshua Bengio. 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