{"type":"ScholarlyArticle","title":"Completeness-aware Rule Learning from Knowledge Graphs","authors":[{"name":"Thomas Pellissier Tanon","slug":"thomas-pellissier-tanon","given":"Thomas Pellissier","family":"Tanon","affiliation":"Max Planck Institute of Informatics; Télécom ParisTech {tpelliss , dstepano ,"},{"name":"Daria Stepanova","slug":"daria-stepanova","given":"Daria","family":"Stepanova","affiliation":"Max Planck Institute of Informatics"},{"name":"Simon Razniewski","slug":"simon-razniewski","given":"Simon","family":"Razniewski","affiliation":"Max Planck Institute of Informatics"},{"name":"Paramita Mirza","slug":"paramita-mirza","given":"Paramita","family":"Mirza","affiliation":"Max Planck Institute of Informatics"},{"name":"Gerhard Weikum","slug":"gerhard-weikum","given":"Gerhard","family":"Weikum","affiliation":"Max Planck Institute of Informatics"}],"abstract":"Knowledge graphs (KGs) are huge collections of primarily encyclopedic facts that are widely used in entity recognition, structured search, question answering, and similar. Rule mining is commonly applied to discover patterns in KGs. However, unlike in traditional association rule mining, KGs provide a setting with a high degree of incompleteness, which may result in the wrong estimation of the quality of mined rules, leading to erroneous beliefs such as all artists have won an award. In this paper we propose to use (in-)completeness meta-information to better assess the quality of rules learned from incomplete KGs. We introduce completeness-aware scoring functions for relational association rules. Experimental evaluation both on real and synthetic datasets shows that the proposed rule ranking approaches have remarkably higher accuracy than the state-of-the-art methods in uncovering missing facts.","doi":"10.24963/ijcai.2018/749","url":"https://ijcai.org/proceedings/2018/749","pdf":"https://ijcai.org/proceedings/2018/0749.pdf","markdown":"https://ijcai.org/proceedings/2018/749.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/749","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":"Sister Conferences Best Papers","track":"Best Sister Conferences","number":749,"pages":{"first":5339,"last":5343},"keywords":["Uncertainty in AI: Relational Inference","Multidisciplinary Topics and Applications: Intelligent Database Systems","Machine Learning: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, and Zachary G. 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