{"type":"ScholarlyArticle","title":"Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)","authors":[{"name":"Mehdi Ali","slug":"mehdi-ali","given":"Mehdi","family":"Ali","affiliation":"Smart Data Analytics Group , University of Bonn , Germany; Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) , Sankt Augustin and Dresden , Germany"},{"name":"Max Berrendorf","slug":"max-berrendorf","given":"Max","family":"Berrendorf","affiliation":"Ludwig-Maximilians-Universität München , Munich , Germany"},{"name":"Mikhail Galkin","slug":"mikhail-galkin","given":"Mikhail","family":"Galkin","affiliation":"Mila , McGill University"},{"name":"Veronika Thost","slug":"veronika-thost","given":"Veronika","family":"Thost","affiliation":"IBM Research , MIT-IBM Watson AI Lab"},{"name":"Tengfei Ma","slug":"tengfei-ma","given":"Tengfei","family":"Ma","affiliation":"IBM Research , MIT-IBM Watson AI Lab"},{"name":"Volker Tresp","slug":"volker-tresp","given":"Volker","family":"Tresp","affiliation":"Ludwig-Maximilians-Universität München , Munich , Germany; Siemens AG , Munich , Germany"},{"name":"Jens Lehmann","slug":"jens-lehmann","given":"Jens","family":"Lehmann","affiliation":"Smart Data Analytics Group , University of Bonn , Germany; Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) , Sankt Augustin and Dresden , Germany"}],"abstract":"For many years, link prediction on knowledge. graphs has been a purely transductive task, not allowing for reasoning on unseen entities. \r\nRecently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities.\r\nStill, all these approaches only consider triple-based KGs, whereas their richer counterparts, hyper-relational KGs (e.g., Wikidata), have not yet been properly studied.\r\nIn this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks. \r\nOur experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines. \r\nOur code is available at https://github.com/mali-git/hyper_relational_ilp.","doi":"10.24963/ijcai.2022/731","url":"https://ijcai.org/proceedings/2022/731","pdf":"https://ijcai.org/proceedings/2022/0731.pdf","markdown":"https://ijcai.org/proceedings/2022/731.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/731","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Sister Conferences Best Papers","track":"Sister Conferences Best Papers","number":731,"pages":{"first":5259,"last":5263},"keywords":["Artificial Intelligence: General"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38985411","length_sec":699,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38987268","length_sec":703,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Ali et al., 2021a] Mehdi Ali, Max Berrendorf, Mikhail Galkin, Veronika Thost, Tengfei Ma, Volker Tresp, and Jens Lehmann. 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