{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:03:01Z","timestamp":1791910981552,"version":"4.3.4"},"publisher-location":"New York, NY, USA","reference-count":44,"publisher":"ACM","license":[{"start":{"date-parts":[[2019,1,30]],"date-time":"2019-01-30T00:00:00Z","timestamp":1548806400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"NSFC","award":["61673338"],"award-info":[{"award-number":["61673338"]}]},{"name":"SNF","award":["RiC 01-032014"],"award-info":[{"award-number":["RiC 01-032014"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2019,1,30]]},"DOI":"10.1145\/3289600.3291014","type":"proceedings-article","created":{"date-parts":[[2019,3,11]],"date-time":"2019-03-11T12:33:01Z","timestamp":1552307581000},"page":"96-104","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":148,"title":["Interaction Embeddings for Prediction and Explanation in Knowledge Graphs"],"prefix":"10.1145","author":[{"given":"Wen","family":"Zhang","sequence":"first","affiliation":[{"name":"Zhejiang University, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bibek","family":"Paudel","sequence":"additional","affiliation":[{"name":"University of Z\u00fcrich, Zurich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Alibaba Group, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abraham","family":"Bernstein","sequence":"additional","affiliation":[{"name":"University of Z\u00fcrich, Z\u00fcrich, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huajun","family":"Chen","sequence":"additional","affiliation":[{"name":"Zhejiang University &amp; AZFT Joint Lab of Knowledge Engine, Hangzhou, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2019,1,30]]},"reference":[{"key":"e_1_3_2_1_1_1","doi-asserted-by":"publisher","DOI":"10.1145\/1376616.1376746"},{"key":"e_1_3_2_1_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5363-6"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-013-5363-6"},{"key":"e_1_3_2_1_4_1","volume-title":"Proceedings of NIPS","author":"Bordes Antoine","year":"2013","unstructured":"Antoine Bordes , Nicolas Usunier , Alberto Garc\u00eda-Dur\u00e1n , Jason Weston , and Oksana Yakhnenko . 2013 . Translating Embeddings for Modeling Multi-relational Data . Proceedings of NIPS (2013), 2787--2795. Antoine Bordes, Nicolas Usunier, Alberto Garc\u00eda-Dur\u00e1n, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. Proceedings of NIPS (2013), 2787--2795."},{"key":"e_1_3_2_1_5_1","volume-title":"Proceedings of AAAI","author":"Bordes Antoine","year":"2011","unstructured":"Antoine Bordes , JasonWeston, Ronan Collobert , and Yoshua Bengio . 2011 . Learning Structured Embeddings of Knowledge Bases . Proceedings of AAAI (2011). Antoine Bordes, JasonWeston, Ronan Collobert, and Yoshua Bengio. 2011. Learning Structured Embeddings of Knowledge Bases. Proceedings of AAAI (2011)."},{"key":"e_1_3_2_1_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00778-015-0394-1"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1034"},{"key":"e_1_3_2_1_8_1","volume-title":"Proceedings of AISTATS","author":"Glorot Xavier","year":"2010","unstructured":"Xavier Glorot and Yoshua Bengio . 2010 . Understanding the difficulty of training deep feedforward neural networks . Proceedings of AISTATS (2010), 249--256. Xavier Glorot and Yoshua Bengio. 2010. Understanding the difficulty of training deep feedforward neural networks. Proceedings of AISTATS (2010), 249--256."},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1145\/2806416.2806502"},{"key":"e_1_3_2_1_10_1","volume-title":"Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering. Procedding of ICDE","author":"Heckel Reinhard","year":"2017","unstructured":"Reinhard Heckel , Michail Vlachos , Thomas P. Parnell , and Celestine D\u00fcnner . 2017. Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering. Procedding of ICDE ( 2017 ), 1033--1044. Reinhard Heckel, Michail Vlachos, Thomas P. Parnell, and Celestine D\u00fcnner. 2017. Scalable and Interpretable Product Recommendations via Overlapping Co-Clustering. Procedding of ICDE (2017), 1033--1044."},{"key":"e_1_3_2_1_11_1","doi-asserted-by":"crossref","unstructured":"G. E. Hinton J. L. McClelland and D. E. Rumelhart. 1986. Distributed Representations. Parallel distributed processing: explorations in the microstructure of cognition (1986).   G. E. Hinton J. L. McClelland and D. E. Rumelhart. 1986. Distributed Representations. Parallel distributed processing: explorations in the microstructure of cognition (1986).","DOI":"10.7551\/mitpress\/5237.001.0001"},{"key":"e_1_3_2_1_12_1","volume-title":"Antoine Bordes, and Guillaume Obozinski.","author":"Jenatton Rodolphe","year":"2012","unstructured":"Rodolphe Jenatton , Nicolas Le Roux , Antoine Bordes, and Guillaume Obozinski. 2012 . A latent factor model for highly multi-relational data. Proceddings of NIPS ( 2012), 3176--3184. Rodolphe Jenatton, Nicolas Le Roux, Antoine Bordes, and Guillaume Obozinski. 2012. A latent factor model for highly multi-relational data. Proceddings of NIPS (2012), 3176--3184."},{"key":"e_1_3_2_1_13_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1067"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.5555\/3015812.3015959"},{"key":"e_1_3_2_1_15_1","volume-title":"Kingma and Jimmy Ba","author":"Diederik","year":"2014","unstructured":"Diederik P. Kingma and Jimmy Ba . 2014 . Adam : A Method for Stochastic Optimization. CoRR abs\/1412.6980 (2014). Diederik P. Kingma and Jimmy Ba. 2014. Adam: A Method for Stochastic Optimization. CoRR abs\/1412.6980 (2014)."},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25007-6_37"},{"key":"e_1_3_2_1_17_1","volume-title":"Cohen","author":"Lao Ni","year":"2011","unstructured":"Ni Lao , Tom M. Mitchell , and William W . Cohen . 2011 . Random Walk Inference and Learning in A Large Scale Knowledge Base. In EMNLP. ACL , 529--539. Ni Lao, Tom M. Mitchell, and William W. Cohen. 2011. Random Walk Inference and Learning in A Large Scale Knowledge Base. In EMNLP. ACL, 529--539."},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1082"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.5555\/2886521.2886624"},{"key":"e_1_3_2_1_20_1","volume-title":"Proceedings of the 34th International Conference on Machine Learning, ICML 2017","author":"Liu Hanxiao","year":"2017","unstructured":"Hanxiao Liu , Yuexin Wu , and Yiming Yang . 2017 . Analogical Inference for Multirelational Embeddings . In Proceedings of the 34th International Conference on Machine Learning, ICML 2017 , Sydney, NSW, Australia, 6- -11 August 2017. 2168-- 2178. Hanxiao Liu, Yuexin Wu, and Yiming Yang. 2017. Analogical Inference for Multirelational Embeddings. In Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6--11 August 2017. 2168-- 2178."},{"key":"e_1_3_2_1_21_1","doi-asserted-by":"publisher","DOI":"10.1145\/219717.219748"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.3115\/v1\/P15-1016"},{"key":"e_1_3_2_1_23_1","volume-title":"STransE: a novel embedding model of entities and relationships in knowledge bases","author":"Nguyen Dat Quoc","unstructured":"Dat Quoc Nguyen , Kairit Sirts , Lizhen Qu , and Mark Johnson . 2016. STransE: a novel embedding model of entities and relationships in knowledge bases . In HLT-NAACL. The Association for Computational Linguistics , 460--466. Dat Quoc Nguyen, Kairit Sirts, Lizhen Qu, and Mark Johnson. 2016. STransE: a novel embedding model of entities and relationships in knowledge bases. In HLT-NAACL. The Association for Computational Linguistics, 460--466."},{"key":"e_1_3_2_1_24_1","doi-asserted-by":"publisher","DOI":"10.1109\/JPROC.2015.2483592"},{"key":"e_1_3_2_1_25_1","volume-title":"Holographic Embeddings of Knowledge Graphs. Proceedings of AAAI (2016)","author":"Nickel Maximilian","unstructured":"Maximilian Nickel , Lorenzo Rosasco , and Tomaso A. Poggio . 2016 . Holographic Embeddings of Knowledge Graphs. Proceedings of AAAI (2016) , 1955--1961. Maximilian Nickel, Lorenzo Rosasco, and Tomaso A. Poggio. 2016. Holographic Embeddings of Knowledge Graphs. Proceedings of AAAI (2016), 1955--1961."},{"key":"e_1_3_2_1_26_1","volume-title":"Proceedings of ICML","author":"Nickel Maximilian","year":"2011","unstructured":"Maximilian Nickel , Volker Tresp , and Hans-Peter Kriegel . 2011 . A Three-Way Model for Collective Learning on Multi-Relational Data . Proceedings of ICML (2011), 809--816. Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. Proceedings of ICML (2011), 809--816."},{"key":"e_1_3_2_1_27_1","volume-title":"RDF2Vec: RDF Graph Embeddings for Data Mining. Procedings of ISWC","author":"Ristoski Petar","year":"2016","unstructured":"Petar Ristoski and Heiko Paulheim . 2016. RDF2Vec: RDF Graph Embeddings for Data Mining. Procedings of ISWC ( 2016 ), 498--514. Petar Ristoski and Heiko Paulheim. 2016. RDF2Vec: RDF Graph Embeddings for Data Mining. Procedings of ISWC (2016), 498--514."},{"key":"e_1_3_2_1_28_1","volume-title":"Modeling Relational Data with Graph Convolutional Networks. Proceddings of ESWC","author":"Schlichtkrull Michael","year":"2018","unstructured":"Michael Schlichtkrull , Thomas N. Kipf , Peter Bloem , Rianne van den Berg , Ivan Titov , and Max Welling . 2018. Modeling Relational Data with Graph Convolutional Networks. Proceddings of ESWC ( 2018 ). Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling. 2018. Modeling Relational Data with Graph Convolutional Networks. Proceddings of ESWC (2018)."},{"key":"e_1_3_2_1_29_1","doi-asserted-by":"publisher","DOI":"10.5555\/3298239.3298420"},{"key":"e_1_3_2_1_30_1","volume-title":"Ng","author":"Socher Richard","year":"2013","unstructured":"Richard Socher , Danqi Chen , Christopher D. Manning , and Andrew Y . Ng . 2013 . Reasoning With Neural Tensor Networks for Knowledge Base Completion. Prodeddings of NIPS ( 2013), 926--934. Richard Socher, Danqi Chen, Christopher D. Manning, and Andrew Y. Ng. 2013. Reasoning With Neural Tensor Networks for Knowledge Base Completion. Prodeddings of NIPS (2013), 926--934."},{"key":"e_1_3_2_1_31_1","volume-title":"Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15(Jun)","author":"Srivastava Nitish","year":"2014","unstructured":"Nitish Srivastava , Geoffrey Hinton , Alex Krizhevsky , Ilya Sutskever , and Ruslan Salakhutdinov . 2014 . Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15(Jun) (2014), 1929--1958. Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, Ilya Sutskever, and Ruslan Salakhutdinov. 2014. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal of Machine Learning Research 15(Jun) (2014), 1929--1958."},{"key":"e_1_3_2_1_32_1","doi-asserted-by":"publisher","DOI":"10.1145\/1242572.1242667"},{"key":"e_1_3_2_1_33_1","doi-asserted-by":"publisher","DOI":"10.1145\/1871437.1871445"},{"key":"e_1_3_2_1_34_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/W15-4007"},{"key":"e_1_3_2_1_35_1","volume-title":"Proceedings ICML (2016)","author":"Trouillon Th\u00e9o","year":"2016","unstructured":"Th\u00e9o Trouillon , Johannes Welbl , Sebastian Riedel , \u00c9ric Gaussier , and Guillaume Bouchard . 2016 . Complex Embeddings for Simple Link Prediction . Proceedings ICML (2016) , 2071--2080. Th\u00e9o Trouillon, Johannes Welbl, Sebastian Riedel, \u00c9ric Gaussier, and Guillaume Bouchard. 2016. Complex Embeddings for Simple Link Prediction. Proceedings ICML (2016), 2071--2080."},{"key":"e_1_3_2_1_36_1","volume-title":"Proceedings of IJCAI (2015)","author":"Wang Quan","year":"2015","unstructured":"Quan Wang , Bin Wang , and Li Guo . 2015 . Knowledge Base Completion Using Embeddings and Rules . Proceedings of IJCAI (2015) , 1859--1866. Quan Wang, Bin Wang, and Li Guo. 2015. Knowledge Base Completion Using Embeddings and Rules. Proceedings of IJCAI (2015), 1859--1866."},{"key":"e_1_3_2_1_37_1","doi-asserted-by":"publisher","DOI":"10.1145\/3178876.3186066"},{"key":"e_1_3_2_1_38_1","doi-asserted-by":"publisher","DOI":"10.5555\/2893873.2894046"},{"key":"e_1_3_2_1_39_1","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/P16-1219"},{"key":"e_1_3_2_1_40_1","volume-title":"Proceedings of IJCAI","author":"Xie Ruobing","year":"2016","unstructured":"Ruobing Xie , Zhiyuan Liu , and Maosong Sun . 2016 . Representation Learning of Knowledge Graphs with Hierarchical Types . Proceedings of IJCAI (2016), 2965--2971. Ruobing Xie, Zhiyuan Liu, and Maosong Sun. 2016. Representation Learning of Knowledge Graphs with Hierarchical Types. Proceedings of IJCAI (2016), 2965--2971."},{"key":"e_1_3_2_1_41_1","volume-title":"Proceedings of ICLR","author":"Yang Bishan","year":"2015","unstructured":"Bishan Yang , Wen tau Yih , Xiaodong He , Jianfeng Gao , and Li Deng . 2015 . Embedding Entities and Relations for Learning and Inference in Knowledge Bases . Proceedings of ICLR (2015). Bishan Yang, Wen tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. Proceedings of ICLR (2015)."},{"key":"e_1_3_2_1_42_1","volume-title":"Cohen","author":"Yang Fan","year":"2017","unstructured":"Fan Yang , Zhilin Yang , and William W . Cohen . 2017 . Differentiable Learning of Logical Rules for Knowledge Base Reasoning. In NIPS. 2316--2325. Fan Yang, Zhilin Yang, and William W. Cohen. 2017. Differentiable Learning of Logical Rules for Knowledge Base Reasoning. In NIPS. 2316--2325."},{"key":"e_1_3_2_1_43_1","doi-asserted-by":"publisher","DOI":"10.5555\/3060832.3061037"},{"key":"e_1_3_2_1_44_1","doi-asserted-by":"publisher","DOI":"10.1145\/3041021.3053380"}],"event":{"name":"WSDM '19: The Twelfth ACM International Conference on Web Search and Data Mining","location":"Melbourne VIC Australia","acronym":"WSDM '19","sponsor":["SIGMOD ACM Special Interest Group on Management of Data","SIGWEB ACM Special Interest Group on Hypertext, Hypermedia, and Web","SIGKDD ACM Special Interest Group on Knowledge Discovery in Data","SIGIR ACM Special Interest Group on Information Retrieval"]},"container-title":["Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3289600.3291014","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3289600.3291014","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T01:02:21Z","timestamp":1750208541000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3289600.3291014"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,1,30]]},"references-count":44,"alternative-id":["10.1145\/3289600.3291014","10.1145\/3289600"],"URL":"https:\/\/doi.org\/10.1145\/3289600.3291014","relation":{},"subject":[],"published":{"date-parts":[[2019,1,30]]},"assertion":[{"value":"2019-01-30","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}