Fetching the paper…
Reading the bibliography…
Embedding knowledge graphs (KGs) for multi-hop logical reasoning is a challenging problem due to massive and complicated structures in many KGs.
Foundations of databases , volume 8
Serge Abiteboul, Richard Hull, and Victor Vianu. 1995 · 1995
Earlier work this paper cites.
Approximate nearest neighbors: towards removing the curse of dimensionality
Piotr Indyk and Rajeev Motwani. 1998 · 1998
Earlier work this paper cites.
The unified medical language system (umls): integrating biomedical terminology
Olivier Bodenreider. 2004 · 2004
Earlier work this paper cites.
Spearman correlation coefficients, differences between
Leann Myers and Maria J Sirois. 2004 · 2004
Earlier work this paper cites.
Freebase: a collaboratively created graph database for structuring human knowledge
Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008 · 2008
Earlier work this paper cites.
Pearson correlation coefficient
Jacob Benesty, Jingdong Chen, Yiteng Huang, and Israel Cohen. 2009 · 2009
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling. 2013 · 2013
Earlier work this paper cites.
Wikidata: a free collaborative knowledgebase
Denny Vrandečić and Markus Krötzsch. 2014 · 2014
Earlier work this paper cites.
Traversing knowledge graphs in vector space
K. Guu, J. Miller, and P. Liang. 2015 · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba. 2015 · 2015
Earlier work this paper cites.
An overview of microsoft academic service (mas) and applications
Arnab Sinha, Zhihong Shen, Yang Song, Hao Ma, Darrin Eide, Bo-June Hsu, and Kuansan Wang. 2015 · 2015
Earlier work this paper cites.
Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen. 2015 · 2015
Earlier work this paper cites.
Transg: A generative mixture model for knowledge graph embedding
Han Xiao, Minlie Huang, Yu Hao, and Xiaoyan Zhu. 2015 · 2015
Earlier work this paper cites.
Jointly embedding knowledge graphs and logical rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo. 2016 · 2016
Cited alongside, same era.
Deeppath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017 · 2017
Cited alongside, same era.
Knowledge graph embedding with iterative guidance from soft rules
Shu Guo, Quan Wang, Lihong Wang, Bin Wang, and Li Guo. 2018 · 2018
Cited alongside, same era.
Embedding logical queries on knowledge graphs
Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, and Jure Leskovec. 2018 · 2018
Cited alongside, same era.
Smoothing the geometry of probabilistic box embeddings
Xiang Li, Luke Vilnis, Dongxu Zhang, Michael Boratko, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
Multi-hop knowledge graph reasoning with reward shaping
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2018 · 2018
Query2box: Reasoning over knowledge graphs in vector space using box embeddings
Hongyu Ren, Weihua Hu, and Jure Leskovec. 2020 · 2020
Later among the works it cites.
Beta embeddings for multi-hop logical reasoning in knowledge graphs
Hongyu Ren and Jure Leskovec. 2020 · 2020
Later among the works it cites.
Complex query answering with neural link predictors
Erik Arakelyan, Daniel Daza, Pasquale Minervini, and Michael Cochez. 2021 · 2021
Later among the works it cites.
Capacity and bias of learned geometric embeddings for directed graphs
Michael Boratko, Dongxu Zhang, Nicholas Monath, Luke Vilnis, Kenneth L Clarkson, and Andrew McCallum. 2021 · 2021
Later among the works it cites.
Probabilistic box embeddings for uncertain knowledge graph reasoning
Xuelu Chen, Michael Boratko, Muhao Chen, Shib Sankar Dasgupta, Xiang Lorraine Li, and Andrew McCallum. 2021 · 2021
Later among the works it cites.
Probabilistic entity representation model for reasoning over knowledge graphs
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Probabilistic embedding of knowledge graphs with box lattice measures
Luke Vilnis, Xiang Li, Shikhar Murty, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
Drugbank 5.0: a major update to the drugbank database for 2018
David S Wishart, Yannick D Feunang, An C Guo, Elvis J Lo, Ana Marcu, Jason R Grant, Tanvir Sajed, Daniel Johnson, Carin Li, Zinat Sayeeda, et al. 2018 · 2018
Cited alongside, same era.
Adaptive convolution for multi-relational learning
Xiaotian Jiang, Quan Wang, and Bin Wang. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, et al. 2019 · 2019
Cited alongside, same era.
Improving local identifiability in probabilistic box embeddings
Shib Dasgupta, Michael Boratko, Dongxu Zhang, Luke Vilnis, Xiang Li, and Andrew McCallum. 2020 · 2020
Cited alongside, same era.
Message passing query embedding
Daniel Daza and Michael Cochez. 2020 · 2020
Cited alongside, same era.
Nurendra Choudhary, Nikhil Rao, Sumeet Katariya, Karthik Subbian, and Chandan Reddy. 2021 · 2021
Later among the works it cites.
Answering complex queries in knowledge graphs with bidirectional sequence encoders
Bhushan Kotnis, Carolin Lawrence, and Mathias Niepert. 2021 · 2021
Later among the works it cites.
Modeling label space interactions in multi-label classification using box embeddings
Dhruvesh Patel, Pavitra Dangati, Jay-Yoon Lee, Michael Boratko, and Andrew McCallum. 2021 · 2021
Later among the works it cites.
Smore: Knowledge graph completion and multi-hop reasoning in massive knowledge graphs
Hongyu Ren, Hanjun Dai, Bo Dai, Xinyun Chen, Denny Zhou, Jure Leskovec, and Dale Schuurmans. 2021 · 2021
Later among the works it cites.
Cone: Cone embeddings for multi-hop reasoning over knowledge graphs
Zhanqiu Zhang, Jie Wang, Jiajun Chen, Shuiwang Ji, and Feng Wu. 2021 · 2021
Later among the works it cites.
Query embedding on hyper-relational knowledge graphs
Dimitrios Alivanistos, Max Berrendorf, Michael Cochez, and Mikhail Galkin. 2022 · 2022
Closest in time.
Query2particles: Knowledge graph reasoning with particle embeddings
Jiaxin Bai, Zihao Wang, Hongming Zhang, and Yangqiu Song. 2022 · 2022
Closest in time.