{"type":"ScholarlyArticle","title":"Learning Embeddings from Knowledge Graphs With Numeric Edge Attributes","authors":[{"name":"Sumit Pai","slug":"sumit-pai","given":"Sumit","family":"Pai","affiliation":""},{"name":"Luca Costabello","slug":"luca-costabello","given":"Luca","family":"Costabello","affiliation":""}],"abstract":"Numeric values associated to edges of a knowledge graph have been used to represent uncertainty, edge importance, and even out-of-band knowledge in a growing number of scenarios, ranging from genetic data to social networks. Nevertheless, traditional knowledge graph embedding models are not designed to capture such information, to the detriment of predictive power. \r\nWe propose a novel method that injects numeric edge attributes into the scoring layer of a traditional knowledge graph embedding architecture. Experiments with publicly available numeric-enriched knowledge graphs show that our method outperforms traditional numeric-unaware baselines as well as the recent UKGE model.","doi":"10.24963/ijcai.2021/395","url":"https://ijcai.org/proceedings/2021/395","pdf":"https://ijcai.org/proceedings/2021/0395.pdf","markdown":"https://ijcai.org/proceedings/2021/395.md","bibtex":"https://ijcai.org/proceedings/2021/bibtex/395","volume":{"slug":"2021","title":"Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2021","url":"https://ijcai.org/proceedings/2021/","year":2021,"location":"Montreal","dates":"19-27 August 2021","editors":"Prof. Zhi-Hua Zhou","isbn":"978-0-9992411-9-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2021"},"section":"Machine Learning","track":"Main Track","number":395,"pages":{"first":2869,"last":2875},"keywords":["Machine Learning: Relational Learning","Natural Language Processing: Embeddings"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}