{"type":"ScholarlyArticle","title":"Scalable Rule Learning via Learning Representation","authors":[{"name":"Pouya Ghiasnezhad Omran","slug":"pouya-ghiasnezhad-omran","given":"Pouya Ghiasnezhad","family":"Omran","affiliation":"Griffith University"},{"name":"Kewen Wang","slug":"kewen-wang","given":"Kewen","family":"Wang","affiliation":"Griffith University"},{"name":"Zhe Wang","slug":"zhe-wang","given":"Zhe","family":"Wang","affiliation":"Griffith University"}],"abstract":"We study the problem of learning first-order rules from large Knowledge Graphs (KGs). With recent advancement in information extraction, vast data repositories in the KG format have been obtained such as Freebase and YAGO. However, traditional techniques for rule learning are not scalable for KGs. This paper presents a new approach RLvLR to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. Experimental results show that our system outperforms some state-of-the-art systems. Specifically, for massive KGs with hundreds of predicates and over 10M facts, RLvLR is much faster and can learn much more quality rules than major systems for rule learning in KGs such as AMIE+. We also used the RLvLR-mined rules in an inference module to carry out the link prediction task. In this task, RLvLR outperformed Neural LP, a state-of-the-art link prediction system, in both runtime and accuracy.","doi":"10.24963/ijcai.2018/297","url":"https://ijcai.org/proceedings/2018/297","pdf":"https://ijcai.org/proceedings/2018/0297.pdf","markdown":"https://ijcai.org/proceedings/2018/297.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/297","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":297,"pages":{"first":2149,"last":2155},"keywords":["Machine Learning: Relational Learning","Machine Learning: Knowledge-based Learning"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Ko- bilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. 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