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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)
- Host: GitHub
- URL: https://github.com/MIRALab-USTC/KGRPapers
- Owner: MIRALab-USTC
- Created: 2020-03-02T09:21:02.000Z (almost 5 years ago)
- Default Branch: master
- Last Pushed: 2020-03-16T15:07:07.000Z (almost 5 years ago)
- Last Synced: 2024-08-03T23:17:08.039Z (5 months ago)
- Topics: knowledge-graph, knowledge-graph-reasoning, paper-list
- Homepage:
- Size: 18.6 KB
- Stars: 21
- Watchers: 5
- Forks: 3
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-kg-qaie - KGRPapers
README
# Must-read papers on Knowledge Graph Reasoning (KGR)
## [Content](#content)
1.1 Logical Query
1.2 Rule Mining
2.1 Path Ranking
2.2 Path Constraints
## [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)]