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Multi-hop knowledge graph (KG) reasoning is an effective and explainable method for predicting the target entity via reasoning paths in query answering (QA) task.
Translating embeddings for modeling multi-relational data
Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013 · 2013
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum. 2015 · 2015
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Representing text for joint embedding of text and knowledge bases
Kristina Toutanova, Danqi Chen, Patrick Pantel, Hoifung Poon, Pallavi Choudhury, and Michael Gamon. 2015 · 2015
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Embedding entities and relations for learning and inference in knowledge bases
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015 · 2015
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Representation learning of knowledge graphs with entity descriptions
Ruobing Xie, Zhiyuan Liu, Jia Jia, Huanbo Luan, and Maosong Sun. 2016 · 2016
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine. 2017 · 2017
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End-to-end differentiable proving
Tim Rocktäschel and Sebastian Riedel. 2017 · 2017
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Deeppath: A reinforcement learning method for knowledge graph reasoning
Wenhan Xiong, Thien Hoang, and William Yang Wang. 2017 · 2017
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Differentiable learning of logical rules for knowledge base reasoning
Fan Yang, Zhilin Yang, and William W Cohen. 2017 · 2017
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Variational knowledge graph reasoning
Wenhu Chen, Wenhan Xiong, Xifeng Yan, and William Yang Wang. 2018 · 2018
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Go for a walk and arrive at the answer: Reasoning over paths in knowledge bases using reinforcement learning
Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, and Andrew McCallum. 2018 · 2018
Cited alongside, same era.
Convolutional 2d knowledge graph embeddings
Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. 2018 · 2018
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Meta-learning for low-resource neural machine translation
Multi-hop knowledge graph reasoning with reward shaping
Xi Victoria Lin, Richard Socher, and Caiming Xiong. 2018 · 2018
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Reinforcewalk: Learning to walk in graph with monte carlo tree search
Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, and Jianfeng Gao. 2018 · 2018
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Open-world knowledge graph completion
Baoxu Shi and Tim Weninger. 2018 · 2018
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One-shot relational learning for knowledge graphs
Wenhan Xiong, Mo Yu, Shiyu Chang, Xiaoxiao Guo, and William Yang Wang. 2018 · 2018
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Hotpotqa: A dataset for diverse, explainable multi-hop question answering
Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William Cohen, Ruslan Salakhutdinov, and Christopher D Manning. 2018 · 2018
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Cognitive graph for multi-hop reading comprehension at scale
Ming Ding, Chang Zhou, Qibin Chen, Hongxia Yang, and Jie Tang. 2019 · 2019
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Jiatao Gu, Yong Wang, Yun Chen, Victor OK Li, and Kyunghyun Cho. 2018 · 2018
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Fewrel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018 · 2018
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Dynamically fused graph network for multi-hop reasoning
Yunxuan Xiao, Yanru Qu, Lin Qiu, Hao Zhou, Lei Li, Weinan Zhang, and Yong Yu. 2019 · 2019
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