{"type":"ScholarlyArticle","title":"A Structural Representation Learning for Multi-relational Networks","authors":[{"name":"Lin Liu","slug":"lin-liu","given":"Lin","family":"Liu","affiliation":"BJ ER Center of HVLIP&CC , School of Comp. Sci. , Beijing Institute of Technology , Beijing , China"},{"name":"Xin Li","slug":"xin-li","given":"Xin","family":"Li","affiliation":"BJ ER Center of HVLIP&CC , School of Comp. Sci. , Beijing Institute of Technology , Beijing , China"},{"name":"William K. Cheung","slug":"william-k-cheung","given":"William K.","family":"Cheung","affiliation":"Department of Computer Science , Hong Kong Baptist University , Hong Kong , China"},{"name":"Chengcheng Xu","slug":"chengcheng-xu","given":"Chengcheng","family":"Xu","affiliation":"BJ ER Center of HVLIP&CC , School of Comp. Sci. , Beijing Institute of Technology , Beijing , China"}],"abstract":"Most of the existing multi-relational network embedding methods, e.g., TransE, are formulated to preserve pair-wise connectivity structures in the networks. With the observations that significant triangular connectivity structures and parallelogram connectivity structures found in many real multi-relational networks are often ignored and that a hard-constraint commonly adopted by most of the network embedding methods is inaccurate by design, we propose a novel representation learning model for multi-relational networks which can alleviate both fundamental limitations. Scalable learning algorithms are derived using the stochastic gradient descent algorithm and negative sampling. Extensive experiments on real multi-relational network datasets of WordNet and Freebase demonstrate the efficacy of the proposed model when compared with the state-of-the-art embedding methods.","doi":"10.24963/ijcai.2017/565","url":"https://ijcai.org/proceedings/2017/565","pdf":"https://ijcai.org/proceedings/2017/0565.pdf","markdown":"https://ijcai.org/proceedings/2017/565.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/565","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Natural Language Processing","track":"Main track","number":565,"pages":{"first":4047,"last":4053},"keywords":["Natural Language Processing: Information Extraction","Machine Learning: Knowledge-based Learning","Machine Learning: Multi-instance/Multi-label/Multi-view learning","Natural Language Processing: Information Retrieval"],"videos":[],"references":[{"position":1,"raw":"[Ahmed et al., 2013] Amr Ahmed, Nino Shervashidze, Shravan M. 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