Fetching the paper…
Reading the bibliography…
Knowledge graph (KG) link prediction is a fundamental task in artificial intelligence, with applications in natural language processing, information retrieval, and biomedicine.
Samuel Humeau, Kurt Shuster, Marie-Anne Lachaux, and Jason Weston · 1905
Earlier work this paper cites.
Kg-bert: Bert for knowledge graph completion
Liang Yao, Chengsheng Mao, and Yuan Luo · 1909
Earlier work this paper cites.
Quantile regression
Roger Koenker and Kevin F Hallock · 2001
Earlier work this paper cites.
Rapid object detection using a boosted cascade of simple features
Paul Viola and Michael Jones · 2001
Earlier work this paper cites.
A survey on knowledge graphs: Representation, acquisition and applications
Shaoxiong Ji, Shirui Pan, Erik Cambria, Pekka Marttinen, and Philip S Yu · 2002
Earlier work this paper cites.
Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Ludmila I Kuncheva and Christopher J Whitaker · 2003
Earlier work this paper cites.
A three-way model for collective learning on multi-relational data
Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel · 2011
Earlier work this paper cites.
A cascade ranking model for efficient ranked retrieval
Lidan Wang, Jimmy Lin, and Donald Metzler · 2011
Earlier work this paper cites.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko · 2013
Earlier work this paper cites.
A review of relational machine learning for knowledge graphs
Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich · 2015
Earlier work this paper cites.
Observed versus latent features for knowledge base and text inference
Kristina Toutanova and Danqi Chen · 2015
Earlier work this paper cites.
Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon · 2015
Earlier work this paper cites.
Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng · 2015
Earlier work this paper cites.
Compositional learning of embeddings for relation paths in knowledge base and text
Kristina Toutanova, Victoria Lin, Wen-tau Yih, Hoifung Poon, and Chris Quirk · 2016
Earlier work this paper cites.
Complex embeddings for simple link prediction
Théo Trouillon, Johannes Welbl, Sebastian Riedel, Éric Gaussier, and Guillaume Bouchard · 2016
Earlier work this paper cites.
Representation learning of knowledge graphs with entity descriptions
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun · 2016
Earlier work this paper cites.
A standard database for drug repositioning
Adam S Brown and Chirag J Patel · 2017
Cited alongside, same era.
Efficient cost-aware cascade ranking in multi-stage retrieval
Ruey-Cheng Chen, Luke Gallagher, Roi Blanco, and J Shane Culpepper · 2017
Cited alongside, same era.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Cited alongside, same era.
Knowledge graph embedding: A survey of approaches and applications
Quan Wang, Zhendong Mao, Bin Wang, and Li Guo · 2017
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel · 2018
Cited alongside, same era.
TuckER: Tensor factorization for knowledge graph completion
Ivana Balazevic, Carl Allen, and Timothy Hospedales · 2019
Cited alongside, same era.
You can teach an old dog new tricks! on training knowledge graph embeddings
Daniel Ruffinelli, Samuel Broscheit, and Rainer Gemulla · 2020
Later among the works it cites.
CoDEx: A Comprehensive Knowledge Graph Completion Benchmark
Tara Safavi and Danai Koutra · 2020
Later among the works it cites.
Transformers: State-of-the-art natural language processing
Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Remi Louf, Morgan Funtowicz, Joe Davison, Sam Shleifer, Patrick von Platen, Clara Ma, Yacine Jernite, Julien Plu, Canwen Xu, Teven Le Scao, Sylvain Gugger, Mariama Drame, Quentin Lhoest, and Alexander Rush · 2020
Later among the works it cites.
Approximate nearest neighbor negative contrastive learning for dense text retrieval
Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul N Bennett, Junaid Ahmed, and Arnold Overwijk · 2020
Later among the works it cites.
Inductive entity representations from text via link prediction
Daniel Daza, Michael Cochez, and Paul Groth · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
BERT: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2019
Cited alongside, same era.
Joint optimization of cascade ranking models
Luke Gallagher, Ruey-Cheng Chen, Roi Blanco, and J Shane Culpepper · 2019
Cited alongside, same era.
Multi-stage document ranking with bert
Rodrigo Nogueira, Wei Yang, Kyunghyun Cho, and Jimmy Lin · 2019
Cited alongside, same era.
Sentence-BERT: Sentence embeddings using Siamese BERT-networks
Nils Reimers and Iryna Gurevych · 2019
Cited alongside, same era.
Rotate: Knowledge graph embedding by relational rotation in complex space
Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang · 2019
Cited alongside, same era.
LibKGE - a knowledge graph embedding library for reproducible research
Samuel Broscheit, Daniel Ruffinelli, Adrian Kochsiek, Patrick Betz, and Rainer Gemulla · 2020
Cited alongside, same era.
Domain-specific language model pretraining for biomedical natural language processing
Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon · 2021
Later among the works it cites.
Knowledge base completion meets transfer learning
Vid Kocijan and Thomas Lukasiewicz · 2021
Later among the works it cites.
Pretrained transformers for text ranking: Bert and beyond
Jimmy Lin, Rodrigo Nogueira, and Andrew Yates · 2021
Later among the works it cites.
Sparse, dense, and attentional representations for text retrieval
Yi Luan, Jacob Eisenstein, Kristina Toutanova, and Michael Collins · 2021
Later among the works it cites.
Scientific language models for biomedical knowledge base completion: An empirical study
Rahul Nadkarni, David Wadden, Iz Beltagy, Noah Smith, Hannaneh Hajishirzi, and Tom Hope · 2021
Later among the works it cites.
Structure-augmented text representation learning for efficient knowledge graph completion
Bo Wang, Tao Shen, Guodong Long, Tianyi Zhou, Ying Wang, and Yi Chang · 2021
Later among the works it cites.
Machine knowledge: Creation and curation of comprehensive knowledge bases
Gerhard Weikum, Xin Luna Dong, Simon Razniewski, and Fabian M. Suchanek · 2021
Later among the works it cites.
Neural bellman-ford networks: A general graph neural network framework for link prediction
Zhaocheng Zhu, Zuobai Zhang, Louis-Pascal Xhonneux, and Jian Tang · 2021
Later among the works it cites.
Building a knowledge graph to enable precision medicine
Payal Chandak, Kexin Huang, and Marinka Zitnik · 2022
Closest in time.
An open challenge for inductive link prediction on knowledge graphs
Mikhail Galkin, Max Berrendorf, and Charles Tapley Hoyt · 2022
Closest in time.
Wisdom of committees: An overlooked approach to faster and more accurate models
Xiaofang Wang, Dan Kondratyuk, Eric Christiansen, Kris M Kitani, Yair Alon, and Elad Eban · 2022
Closest in time.