LIANGKE23 / Knowledge_Graph_Reasoning_Papers

Must-read papers on knowledge graph reasoning

Knowledge Graph Reasoning Papers

Mainly Contributed and Maintained by Xin Lv and Jiaxin Shi.

Thanks for all great contributors on GitHub!

Contents

• 1. Survey Papers
• 2. Multi-hop Reasoning
  • 2.1 Entity Prediction
  • 2.2 Relation Prediction
  • 2.3 Inductive Reasoning
• 3. Reasoning with Logic Rule
  • 3.1 Rule Mining/Learning
  • 3.2 Rule-based Reasoning
  • 3.3 Rule-enhanced Knowledge Graph Embedding
• 4. Query-based Reasoning
  • 4.1 Path-based Query
  • 4.2 Complex Logic Query
  • 4.3 Complex Natural Language Query
• 5. Benchmark and Evaluation
• Acknowledgements

1. Survey Papers

1. A Review: Knowledge Reasoning over Knowledge Graph. Xiaojun Chen, Shengbin Jia, Yang Xiang. Expert Systems with Applications. paper

2. Multi-Hop Reasoning

2.1 Entity Prediction

Predict the missing tail entity and corresponding supporting paths in one triple. (h, r, ?) -> (h, r, t)

1. 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, Andrew McCallum. ICLR 2018. paper code

2. M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search. Yelong Shen, Jianshu Chen, Po-Sen Huang, Yuqing Guo, Jianfeng Gao. NeurIPS 2018. paper code

3. Multi-Hop Knowledge Graph Reasoning with Reward Shaping. Xi Victoria Lin, Richard Socher, Caiming Xiong. EMNLP 2018. paper code

4. Adapting Meta Knowledge Graph Information for Multi-Hop Reasoning over Few-Shot Relations. Xin Lv, Yuxian Gu, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu. EMNLP 2019. paper code

5. DIVINE: A Generative Adversarial Imitation Learning Framework for Knowledge Graph Reasoning. Ruiping Li, Xiang Cheng. EMNLP 2019. paper code

6. Collaborative Policy Learning for Open Knowledge Graph Reasoning. Cong Fu, Tong Chen, Meng Qu, Woojeong Jin, Xiang Ren. EMNLP 2019. paper code

7. Reasoning on Knowledge Graphs with Debate Dynamics. Marcel Hildebrandt, Jorge Andres Quintero Serna, Yunpu Ma, Martin Ringsquandl, Mitchell Joblin, Volker Tresp. AAAI 2020. paper code

8. Reasoning Like Human: Hierarchical Reinforcement Learning for Knowledge Graph Reasoning. Guojia Wan, Shirui Pan, Chen Gong, Chuan Zhou, Gholamreza Haffari. IJCAI 2020. paper

9. Learning Collaborative Agents with Rule Guidance for Knowledge Graph Reasoning. Deren Lei, Gangrong Jiang, Xiaotao Gu, Kexuan Sun, Yuning Mao, Xiang Ren. EMNLP 2020. paper code

10. Dynamic Anticipation and Completion for Multi-Hop Reasoning over Sparse Knowledge Graph. Xin Lv, Xu Han, Lei Hou, Juanzi Li, Zhiyuan Liu, Wei Zhang, Yichi Zhang, Hao Kong, Suhui Wu. EMNLP 2020. paper code

11. GaussianPath:A Bayesian Multi-Hop Reasoning Framework for Knowledge Graph Reasoning. Guojia Wan, Bo Du. AAAI 2021. paper code

2.2 Relation Prediction

Given head and tail entity and paths between them, predict the missing relation. (h, ?, t) -> (h, r, t)

1. Random walk inference and learning in a large scale knowledge base. Ni Lao, Tom Mitchell, William W. Cohen. EMNLP 2011. paper

2. Compositional vector space models for knowledge base inference. Arvind Neelakantan, Benjamin Roth, Andrew McCallum. ACL 2015. paper

3. Chains of Reasoning over Entities, Relations, and Text using Recurrent Neural Networks. Rajarshi Das, Arvind Neelakantan, David Belanger, Andrew McCallum. EACL 2017. paper

4. DeepPath: A Reinforcement Learning Method for Knowledge Graph Reasoning. Wenhan Xiong, Thien Hoang, William Yang Wang. EMNLP 2017. paper code

5. Variational Knowledge Graph Reasoning. Wenhu Chen, Wenhan Xiong, Xifeng Yan, William Yang Wang. NAACL 2018. paper

6. Incorporating Graph Attention Mechanism into Knowledge Graph Reasoning Based on Deep Reinforcement Learning. Heng Wang, Shuangyin Li, Rong Pan, Mingzhi Mao. EMNLP 2019. paper code

2.3 Inductive Reasoning

1. Inductive Relation Prediction by Subgraph Reasoning. Komal K. Teru, Etienne Denis, William L. Hamilton. ICML 2020. paper code

2. Inductive Relation Prediction by BERT. Hanwen Zha, Zhiyu Chen, Xifeng Yan. Arxiv 2021. paper code

3. Reasoning with Logic Rule

3.1 Rule Mining/Learning

1. Fast rule mining in ontological knowledge bases with AMIE+. Luis Galárraga, Christina Teflioudi, Katja Hose, Fabian M. Suchanek. VLDB Journal 2015. paper code

2. Scalable Rule Learning via Learning Representation. Pouya Ghiasnezhad Omran, Kewen Wang, Zhe Wang. IJCAI 2018. paper code

3. Rule Learning from Knowledge Graphs Guided by Embedding Models. V. Thinh Ho, D. Stepanova, M. Gad-Elrab, E. Kharlamov, G. Weikum. ISWC 2018. paper code

4. Anytime Bottom-Up Rule Learning for Knowledge Graph Completion. Christian Meilicke, Melisachew Wudage Chekol, Daniel Ruffinelli, Heiner Stuckenschmidt. IJCAI 2019. paper code

3.2 Rule-based Reasoning

1. Differentiable Learning of Logical Rules for Knowledge Base Reasoning. Fan Yang, Zhilin Yang, William W. Cohen. NeurIPS 2017. paper code

2. End-to-End Differentiable Proving. Tim Rocktäschel, Sebastian Riedel. NeurIPS 2017. paper code

3. DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs. Ali Sadeghian, Mohammadreza Armandpour, Patrick Ding, Daisy Zhe Wang. NeurIPS 2019. paper code

4. RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs. Meng Qu, Junkun Chen, Louis-Pascal Xhonneux, Yoshua Bengio, Jian Tang. Arxiv 2020. paper

5. Learning Reasoning Strategies in End-to-End Differentiable Proving. Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel. ICML 2020. paper code

3.3 Rule-enhanced Knowledge Graph Embedding

1. RUGE: Knowledge Graph Embedding with Iterative Guidance from Soft Rules. Shu Guo, Quan Wang, Lihong Wang, Bin Wang, Li Guo. AAAI 2018. paper code

2. Iteratively Learning Embeddings and Rules for Knowledge Graph Reasoning. Wen Zhang, Bibek Paudel, Liang Wang, Jiaoyan Chen, Hai Zhu, Wei Zhang, Abraham Bernstein, Huajun Chen. WWW 2019. paper

3. Probabilistic Logic Neural Networks for Reasoning. Meng Qu, Jian Tang. NeurIPS 2019. paper code

4. Query-based Reasoning

4.1 Path-based Query

1. Traversing Knowledge Graphs in Vector Space. Kelvin Guu, John Miller, Percy Liang. EMNLP 2015. paper code

2. CoKE: Contextualized Knowledge Graph Embedding. Quan Wang, Pingping Huang, Haifeng Wang, Songtai Dai, Wenbin Jiang, Jing Liu, Yajuan Lyu, Yong Zhu, Hua Wu. Arxiv 2019. paper code

4.2 Complex Logic Query

1. Embedding Logical Queries on Knowledge Graphs. William L. Hamilton, Payal Bajaj, Marinka Zitnik, Dan Jurafsky, Jure Leskovec. NeurIPS 2018. paper code

2. Query2box: Reasoning over Knowledge Graphs in Vector Space using Box Embeddings. Hongyu Ren, Weihua Hu, Jure Leskovec. ICLR 2019. paper code

3. Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs. Hongyu Ren, Jure Leskovec. NeurIPS 2020. paper code

4.3 Complex Natural Language Query

1. (Dataset: WikiTableQuestions) Compositional Semantic Parsing on Semi-Structured Tables. Panupong Pasupat, Percy Liang. ACL 2015. paper code

2. (Dataset: MetaQA) Variational Reasoning for Question Answering with Knowledge Graph. Yuyu Zhang, Hanjun Dai, Zornitsa Kozareva, Alexander J. Smola, Le Song. AAAI 2018. paper data

5. Benchmark and Evaluation

1. Is Multi-Hop Reasoning Really Explainable? Towards Benchmarking Reasoning Interpretability. Xin Lv, Yixin Cao, Lei Hou, Juanzi Li, Zhiyuan Liu, Yichi Zhang, Zelin Dai. Arxiv 2021. paper code

Acknowledgements

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