{"type":"ScholarlyArticle","title":"Knowledge Hypergraphs: Prediction Beyond Binary Relations","authors":[{"name":"Bahare Fatemi","slug":"bahare-fatemi","given":"Bahare","family":"Fatemi","affiliation":"University of British Columbia; Element AI"},{"name":"Perouz Taslakian","slug":"perouz-taslakian","given":"Perouz","family":"Taslakian","affiliation":"Element AI"},{"name":"David Vazquez","slug":"david-vazquez","given":"David","family":"Vazquez","affiliation":"Element AI"},{"name":"David Poole","slug":"david-poole","given":"David","family":"Poole","affiliation":"University of British Columbia"}],"abstract":"Knowledge graphs store facts using relations between two entities. In this work, we address the question of link prediction in knowledge hypergraphs where relations are defined on any number of entities. While techniques exist (such as reification) that convert non-binary relations into binary ones, we show that current embedding-based methods for knowledge graph completion do not work well out of the box for knowledge graphs obtained through these techniques. To overcome this, we introduce HSimplE and HypE, two embedding-based methods that work directly with knowledge hypergraphs. In both models, the prediction is a function of the relation embedding, the entity embeddings and their corresponding positions in the relation. We also develop public datasets, benchmarks and baselines for hypergraph prediction and show experimentally that the proposed models are more effective than the baselines.","doi":"10.24963/ijcai.2020/303","url":"https://ijcai.org/proceedings/2020/303","pdf":"https://ijcai.org/proceedings/2020/0303.pdf","markdown":"https://ijcai.org/proceedings/2020/303.md","bibtex":"https://ijcai.org/proceedings/2020/bibtex/303","volume":{"slug":"2020","title":"Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2020","url":"https://ijcai.org/proceedings/2020/","year":2020,"location":"Yokohama","dates":"January 2021 (postponed from 11-17 July 2020)","editors":"Christian Bessiere","isbn":"978-0-9992411-6-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2020"},"section":"Machine Learning","track":"Main track","number":303,"pages":{"first":2191,"last":2197},"keywords":["Machine Learning: Relational Learning","Uncertainty in AI: Statistical Relational AI"],"videos":[{"label":"Video","url":"https://videos.ijcai20.org/24544/file_24544.m3u8","length_sec":267,"provider":"hls"}],"references":[{"position":1,"raw":"[Bollacker et al., 2008] Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 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