{"type":"ScholarlyArticle","title":"TransNet: Translation-Based Network Representation Learning for Social Relation Extraction","authors":[{"name":"Cunchao Tu","slug":"cunchao-tu","given":"Cunchao","family":"Tu","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , China"},{"name":"Zhengyan Zhang","slug":"zhengyan-zhang","given":"Zhengyan","family":"Zhang","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,"},{"name":"Zhiyuan Liu","slug":"zhiyuan-liu","given":"Zhiyuan","family":"Liu","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , China; National Lab for Information Science and Technology , Tsinghua University , China"},{"name":"Maosong Sun","slug":"maosong-sun","given":"Maosong","family":"Sun","affiliation":"Department of Computer Science and Technology , State Key Lab on Intelligent Technology and Systems ,; Jiangsu Collaborative Innovation Center for Language Ability , Jiangsu Normal University , China"}],"abstract":"Conventional network representation learning (NRL) models  learn low-dimensional vertex representations by simply regarding each edge as a binary or continuous value. However, there exists rich semantic information on edges and the interactions between vertices usually preserve distinct meanings, which are largely neglected by most existing NRL models. In this work, we present a novel Translation-based NRL model, TransNet, by regarding the interactions between vertices as a translation operation. Moreover, we formalize the task of Social Relation Extraction (SRE) to evaluate the capability of NRL methods on modeling the relations between vertices. Experimental results on SRE demonstrate that TransNet significantly outperforms other baseline methods by 10% to 20% on hits@1. The source code and datasets can be obtained from https://github.com/thunlp/TransNet.","doi":"10.24963/ijcai.2017/399","url":"https://ijcai.org/proceedings/2017/399","pdf":"https://ijcai.org/proceedings/2017/0399.pdf","markdown":"https://ijcai.org/proceedings/2017/399.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/399","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":"Machine Learning S-Z","track":"Main track","number":399,"pages":{"first":2864,"last":2870},"keywords":["Machine Learning: Data Mining","Machine Learning: Deep Learning","Multidisciplinary Topics and Applications: AI and Social Sciences"],"videos":[],"references":[{"position":1,"raw":"[Auer et al., 2007] Sören Auer, Christian Bizer, Georgi Ko- bilarov, Jens Lehmann, Richard Cyganiak, and Zachary Ives. Dbpedia: A nucleus for a web of open data. In The semantic web, pages 722-735. 2007.","title":"Dbpedia: A nucleus for a web of open data","authors":"Sören Auer, Christian Bizer, Georgi Kobilarov, Jens Lehmann, Richard Cyganiak, Zachary Ives","year":"2007","venue":"The semantic web","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Bollacker et al., 2008] Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. Freebase: a collaboratively created graph database for structuring human knowledge. In Proceedings of SIGMOD, pages 1247-1250, 2008.","title":"Freebase: a collaboratively created graph database for structuring human knowledge","authors":"Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, Jamie Taylor","year":"2008","venue":"Proceedings of SIGMOD","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Bordes et al., 2013] Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi- relational data. In Proceedings of NIPS, pages 2787-2795, 2013.","title":"Translating embeddings for modeling multirelational data","authors":"Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, Oksana Yakhnenko","year":"2013","venue":"Proceedings of NIPS","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Cao et al., 2015] Shaosheng Cao, Wei Lu, and Qiongkai Xu. Grarep: Learning graph representations with global structural information. In Proceedings of CIKM, pages 891-900, 2015.","title":"Grarep: Learning graph representations with global structural information","authors":"Cao","year":"2015","venue":"Proceedings of CIKM","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Grover and Leskovec, 2016] Aditya Grover and Jure Leskovec. Node2vec: Scalable feature learning for networks. In Proceedings of KDD, 2016.","title":"Node2vec: Scalable feature learning for networks","authors":"Leskovec Grover, Aditya Grover, Jure Leskovec","year":"2016","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Hoffmann et al., 2011] Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, and Daniel S Weld. Knowledge-based weak supervision for information ex- traction of overlapping relations. In Proceedings of ACL- HLT, pages 541-550, 2011.","title":"Knowledge-based weak supervision for information extraction of overlapping relations","authors":"Raphael Hoffmann, Congle Zhang, Xiao Ling, Luke Zettlemoyer, Daniel S Weld","year":"2011","venue":"Proceedings of ACL-HLT","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Kingma and Ba, 2015] Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. In Proceed- ings of ICLR, 2015.","title":"Adam: A method for stochastic optimization","authors":"Diederik Kingma, Jimmy Ba","year":"2015","venue":"Proceedings of ICLR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Li et al., 2016] Juzheng Li, Jun Zhu, and Bo Zhang. Dis- criminative deep random walk for network classification. In Proceedings of ACL, 2016.","title":"Discriminative deep random walk for network classification","authors":"Li","year":"2016","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Liben-Nowell and Kleinberg, 2007] David Liben-Nowell and Jon Kleinberg. The link-prediction problem for social networks. JASIST, 58(7):1019-1031, 2007.","title":"The link-prediction problem for social networks","authors":"Liben-Nowell, Kleinberg, David Liben-Nowell, Jon Kleinberg","year":"2007","venue":"JASIST","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Lin et al., 2016] Yankai Lin, Shiqi Shen, Zhiyuan Liu, Huanbo Luan, and Maosong Sun. Neural relation extrac- tion with selective attention over instances. In Proceedings of ACL, volume 1, pages 2124-2133, 2016.","title":"Neural relation extraction with selective attention over instances","authors":"Lin","year":"2016","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Lindamood et al., 2009] Jack Lindamood, Raymond Heatherly, Murat Kantarcioglu, and Bhavani Thuraising- ham. Inferring private information using social network data. In Proceedings of WWW, pages 1145-1146, 2009.","title":"Inferring private information using social network data","authors":"Lindamood","year":"2009","venue":"Proceedings of WWW","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Mikolov et al., 2013] Tomas Mikolov, Kai Chen, Greg Cor- rado, and Jeffrey Dean. Efficient estimation of word repre- sentations in vector space. In Proceedings of ICIR, 2013.","title":"Efficient estimation of word representations in vector space","authors":"Mikolov","year":"2013","venue":"Proceedings of ICIR","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Mintz et al., 2009] Mike Mintz, Steven Bills, Rion Snow, and Dan Jurafsky. Distant supervision for relation ex- traction without labeled data. In Proceedings of IJCNLP, pages 1003-1011, 2009.","title":"Distant supervision for relation extraction without labeled data","authors":"Mike Mintz, Steven Bills, Rion Snow, Dan Jurafsky","year":"2009","venue":"Proceedings of IJCNLP","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Pedregosa et al., 2011] Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al. Scikit-learn: Machine learning in python. JMLR, 12:2825-2830, 2011.","title":"Scikit-learn: Machine learning in python","authors":"Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg","year":"2011","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Perozzi et al., 2014] Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. Deepwalk: Online learning of social repre- sentations. In Proceedings of KDD, pages 701-710, 2014.","title":"Deepwalk: Online learning of social representations","authors":"Bryan Perozzi, Rami Al-Rfou, Steven Skiena","year":"2014","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Riedel et al., 2010] Sebastian Riedel, Limin Yao, and An- drew McCallum. Modeling relations and their mentions without labeled text. In Proceedings of ECML-PKDD, pages 148-163, 2010.","title":"Modeling relations and their mentions without labeled text","authors":"Sebastian Riedel, Limin Yao, Andrew Mccallum","year":"2010","venue":"Proceedings of ECML-PKDD","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shepitsen et al., 2008] Andriy Shepitsen, Jonathan Gem- mell, Bamshad Mobasher, and Robin Burke. Personalized recommendation in social tagging systems using hierarchi- cal clustering. In Proceedings of RecSys, pages 259-266, 2008.","title":"Personalized recommendation in social tagging systems using hierarchical clustering","authors":"Andriy Shepitsen, Jonathan Gemmell, Bamshad Mobasher, Robin Burke","year":"2008","venue":"Proceedings of RecSys","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Srivastava et al., 2014] Nitish Srivastava, Geoffrey E Hin- ton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. Dropout: a simple way to prevent neural networks from overfitting. JMLR, 15(1):1929-1958, 2014.","title":"Dropout: a simple way to prevent neural networks from overfitting","authors":"Nitish Srivastava, Geoffrey E Hinton, Alex Krizhevsky, Ilya Sutskever, Ruslan Salakhutdinov","year":"2014","venue":"JMLR","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Surdeanu et al., 2012] Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, and Christopher D Manning. Multi- instance multi-label learning for relation extraction. In Proceedings of EMNLP, pages 455-465, 2012.","title":"Multiinstance multi-label learning for relation extraction","authors":"Mihai Surdeanu, Julie Tibshirani, Ramesh Nallapati, Christopher D Manning","year":"2012","venue":"Proceedings of EMNLP","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Tang et al., 2008] Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, and Zhong Su. Arnetminer: Extraction and mining of academic social networks. In Proceedings of KDD, pages 990-998, 2008.","title":"Arnetminer: Extraction and mining of academic social networks","authors":"Jie Tang, Jing Zhang, Limin Yao, Juanzi Li, Li Zhang, Zhong Su","year":"2008","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Tang et al., 2015] Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. Line: Large- scale information network embedding. In Proceedings of WWW, pages 1067-1077, 2015.","title":"Line: Largescale information network embedding","authors":"Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, Qiaozhu Mei","year":"2015","venue":"Proceedings of WWW","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Tu et al., 2016a] Cunchao Tu, Hao Wang, Xiangkai Zeng, Zhiyuan Liu, and Maosong Sun. Community-enhanced network representation learning for network analysis. arXiv preprint arXiv:1611.06645, 2016.","title":"Community-enhanced network representation learning for network analysis","authors":"Tu","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Tu et al., 2016b] Cunchao Tu, Weicheng Zhang, Zhiyuan Liu, and Maosong Sun. Max-margin deepwalk: Discrimi- native learning of network representation. In Proceedings of IJCAI, 2016.","title":"Max-margin deepwalk: Discriminative learning of network representation","authors":"Tu","year":"2016","venue":"Proceedings of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2016/547"},{"position":24,"raw":"[Tu et al., 2017] Cunchao Tu, Han Liu, Zhiyuan Liu, and Maosong Sun. Cane: Context-aware network embedding for relation modeling. In Proceedings of ACL, 2017.","title":"Cane: Context-aware network embedding for relation modeling","authors":"Tu","year":"2017","venue":"Proceedings of ACL","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Wang et al., 2016] Daixin Wang, Peng Cui, and Wenwu Zhu. Structural deep network embedding. In Proceedings of KDD, 2016.","title":"Structural deep network embedding","authors":"Wang","year":"2016","venue":"Proceedings of KDD","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wang et al., 2017a] Suhang Wang, Jiliang Tang, Charu Ag- garwal, Yi Chang, and Huan Liu. Signed network embed- ding in social media. In Proceedings of SDM, 2017.","title":"Signed network embedding in social media","authors":"Wang","year":"2017","venue":"Proceedings of SDM","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Wang et al., 2017b] Xiao Wang, Peng Cui, Jing Wang, Jian Pei, Wenwu Zhu, and Shiqiang Yang. Community preserv- ing network embedding. In Proceedings of AAAI, 2017.","title":"Community preserving network embedding","authors":"Wang","year":"2017","venue":"Proceedings of AAAI","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Yang et al., 2015] Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, and Edward Y Chang. Network represen- tation learning with rich text information. In Proceedings of IJCAI, 2015.","title":"Network representation learning with rich text information","authors":"Cheng Yang, Zhiyuan Liu, Deli Zhao, Maosong Sun, Edward Y Chang","year":"2015","venue":"Proceedings of IJCAI","doi":null,"url":null,"internal_url":"/proceedings/2015/299"}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}