{"type":"ScholarlyArticle","title":"From One Point to a Manifold: Knowledge Graph Embedding for Precise Link Prediction","authors":[{"name":"Han Xiao","slug":"han-xiao","given":"Han","family":"Xiao","affiliation":""},{"name":"Minlie Huang","slug":"minlie-huang","given":"Minlie","family":"Huang","affiliation":""},{"name":"Xiaoyan Zhu","slug":"xiaoyan-zhu","given":"Xiaoyan","family":"Zhu","affiliation":""}],"abstract":"Knowledge graph embedding aims at offering a numerical knowledge representation paradigm by transforming the entities and relations into continuous vector space. However, existing methods could not characterize the knowledge graph in a fine degree to make a precise link prediction. There are two reasons: being an ill-posed algebraic system and adopting an overstrict geometric form. As precise link prediction is critical, we propose a manifold-based embedding principle (ManifoldE) which could be treated as a well-posed algebraic system that expands the position of golden triples from one point in current models to a manifold in ours. Extensive experiments show that the proposed models achieve substantial improvements against the state-of-the-art baselines especially for the precise prediction task, and yet maintain high efficiency.","doi":null,"url":"https://ijcai.org/proceedings/2016/190","pdf":"https://ijcai.org/proceedings/2016/0190.pdf","markdown":"https://ijcai.org/proceedings/2016/190.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/190","volume":{"slug":"2016","title":"Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2016","url":"https://ijcai.org/proceedings/2016/","year":2016,"location":"New York, NY, USA","dates":"9–15 July 2016","editors":"Subbarao Kambhampati","isbn":"978-1-57735-770-4; 978-1-57735-771-1","publisher":"AAAI Press / International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":null,"track":"","number":190,"pages":{"first":1315,"last":1320},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}