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https://github.com/MIRALab-USTC/KGRPapers

Must-read papers on Knowledge Graph Reasoning (KGR)
https://github.com/MIRALab-USTC/KGRPapers

knowledge-graph knowledge-graph-reasoning paper-list

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Must-read papers on Knowledge Graph Reasoning (KGR)

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# Must-read papers on Knowledge Graph Reasoning (KGR)



## [Content](#content)



1. Logical Rules



     1.1 Logical Query

     1.2 Rule Mining



	 1.3 Others

	



2. Relational Paths



     2.1 Path Ranking

     2.2 Path Constraints



	 2.3 Sequential Decision

	



## [Logical Rules](#content)



### [Logical Query](#content)

1. **Embedding Logical Queries on Knowledge Graphs**. NIPS 2018.

*Will Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec*.

[[Paper](https://papers.nips.cc/paper/7473-embedding-logical-queries-on-knowledge-graphs)] [[Code](https://github.com/williamleif/graphqembed)]



1. **Query2box: Reasoning over Knowledge Graphs in Vector Space Using Box Embedding**. ICLR 2020.

*Hongyu Ren, Weihua Hu, Jure Leskovec*.

[[Paper](https://openreview.net/pdf?id=BJgr4kSFDS)]



### [Rule Mining](#content)

1. **AMIE: association rule mining under incomplete evidence in ontological knowledge bases**. WWW 2013.

*Luis Galárraga, Christina Teflioudi, Katja Hose, Fabian M. Suchanek*.

[[Paper](http://luisgalarraga.de/docs/amie.pdf)]

1. **Differentiable learning of logical rules for knowledge base reasoning**. NIPS 2017.

*Yang, Fan and Yang, Zhilin and Cohen, William W*. 

[[Paper](https://papers.nips.cc/paper/6826-differentiable-learning-of-logical-rules-for-knowledge-base-reasoning.pdf)][[code](https://github.com/fanyangxyz/Neural-LP)]



1. **DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs**. NeurIPS 2019. 

*Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang*. 

[[Paper](https://papers.nips.cc/paper/9669-drum-end-to-end-differentiable-rule-mining-on-knowledge-graphs.pdf)] [[Code](https://github.com/alisadeghian/DRUM)]



### [Others](#content)

1. **End-to-end differentiable proving**. NIPS 2017. 

*Rocktäschel, Tim and Riedel, Sebastian*. 

[[Paper](https://papers.nips.cc/paper/6969-end-to-end-differentiable-proving.pdf)][[code](https://github.com/uclnlp/ntp)]

1. **Quantum Embedding of Knowledge for Reasoning**. NeurIPS 2019. 

*Dinesh Garg, Shajith Ikbal, Santosh K. Srivastava, Harit Vishwakarma, Hima Karanam, L Venkata Subramaniam*.

[[Paper](http://papers.nips.cc/paper/8797-quantum-embedding-of-knowledge-for-reasoning.pdf)]

[[Code](https://github.com/IBM/e2r)]



1. **Probabilistic logic neural networks for reasoning**. NeurIPS 2019. 

*Meng Qu, Jian Tang*.

[[Paper](https://papers.nips.cc/paper/8987-probabilistic-logic-neural-networks-for-reasoning.pdf)]

[[Code](https://github.com/DeepGraphLearning/pLogicNet)]



## [Relational Paths](#content)



### [Path Ranking](#content)



1. **Random walk inference and learning in a large scale knowledge base**. EMNLP 2011.

*Ni Lao, Tom Mitchell, William W. Cohen*.

[[Paper](https://aclanthology.info/pdf/D/D11/D11-1049.pdf)]



1. **DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning**. EMNLP 2017. 

*Wenhan Xiong, Thien Hoang, William Yang Wang*.

[[Paper](http://www.cs.ucsb.edu/~william/papers/DeepPath.pdf)] [[Code](https://github.com/xwhan/DeepPath)]



### [Path Constraints](#content)

1. **Traversing Knowledge Graphs in Vector Space**. EMNLP 2015.

*Kelvin Guu, John Miller, Percy Liang*.

[[Paper](https://www.aclweb.org/anthology/D15-1038.pdf)]



1. **PTransE: Modeling Relation Paths for Representation Learning of Knowledge Bases**. EMNLP 2015.

*Yankai Lin, Zhiyuan Liu, Huanbo Luan, Maosong Sun, Siwei Rao, Song Liu*.

[[Paper](https://arxiv.org/pdf/1506.00379.pdf)] [[Code](https://github.com/thunlp/KB2E)]



1. **Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs**. ICML 2019.

*Lingbing Guo, Zequn Sun, Wei Hu*.

[[Paper](http://proceedings.mlr.press/v97/guo19c/guo19c.pdf)]



### [Sequential Decision](#content)

1. **Go for a Walk and Arrive at the Answer: Reasoning Over Paths in Knowledge Bases using Reinforcement Learning**. ICLR 2018.

*Rajarshi Das, Shehzaad Dhuliawala, Manzil Zaheer, Luke Vilnis, Ishan Durugkar, Akshay Krishnamurthy, Alex Smola, Andrew McCallum*.

[[Paper](https://arxiv.org/pdf/1711.05851.pdf)]



1. **M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search**. NIPS 2018.

*Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao*.

[[Paper](https://papers.nips.cc/paper/7912-m-walk-learning-to-walk-over-graphs-using-monte-carlo-tree-search.pdf)]



1. **Multi-Hop Knowledge Graph Reasoning with Reward Shaping**. EMNLP 2018.

*Xi Victoria Lin, Richard Socher, Caiming Xiong*.

[[Paper](https://www.aclweb.org/anthology/D18-1362/)]

1. **Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning**. ICLR 2020.

*Xiaoran Xu, Wei Feng, Yunsheng Jiang, Xiaohui Xie, Zhiqing Sun, Zhi-Hong Deng*.

[[Paper](https://openreview.net/forum?id=rkeuAhVKvB)]