{"type":"ScholarlyArticle","title":"Multi-Modal Bayesian Embeddings for Learning Social Knowledge Graphs","authors":[{"name":"Zhilin Yang","slug":"zhilin-yang","given":"Zhilin","family":"Yang","affiliation":""},{"name":"Jie Tang","slug":"jie-tang","given":"Jie","family":"Tang","affiliation":""},{"name":"William W. Cohen","slug":"william-w-cohen","given":"William W.","family":"Cohen","affiliation":""}],"abstract":"We study the extent to which online social networks can be connected to open knowledge bases. The problem is referred to as learning social knowledge graphs.We propose a multi-modal Bayesian embedding model, GenVector, to learn latent topics that generate word and network embeddings.GenVector leverages large-scale unlabeled data with embeddings and represents data of two modalities — i.e., social network users and knowledge concepts — in a shared latent topic space.Experiments on three datasets show that the proposed method clearly outperforms state-of-the-art methods. We then deploy the method on AMiner, a large-scale online academic search system with a network of 38,049,189 researchers with a knowledge base with 35,415,011 concepts. Our method significantly decreases the error rate in an online A/B test with live users.","doi":null,"url":"https://ijcai.org/proceedings/2016/326","pdf":"https://ijcai.org/proceedings/2016/0326.pdf","markdown":"https://ijcai.org/proceedings/2016/326.md","bibtex":"https://ijcai.org/proceedings/2016/bibtex/326","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":326,"pages":{"first":2287,"last":2292},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}