论文阅读记录

1.subgraph/GNN-based Reasoning

• Yongqi Zhang and Quanming Yao. 2022. Knowledge Graph Reasoning with Relational Digraph. In Proceedings of the ACM Web Conference 2022 (WWW ‘22).
  
  RED-GNNpaper
• Zhu Z, Zhang Z, Xhonneux L P, et al. Neural bellman-ford networks: A general graph neural network framework for link prediction[J]. Advances in Neural Information Processing Systems, 2021, 34: 29476-29490.
  
  NBFNetpaper
• Teru K, Denis E, Hamilton W. Inductive relation prediction by subgraph reasoning[C]//International Conference on Machine Learning. PMLR, 2020: 9448-9457.
  
  GraILpaper
• Vashishth S, Sanyal S, Nitin V, et al. Composition-based multi-relational graph convolutional networks[J]. arXiv preprint arXiv:1911.03082, 2019.
  
  CompGCNpaper

2.(Path)Rule-based Reasoning

• Qu M, Chen J, Xhonneux L P, et al. Rnnlogic: Learning logic rules for reasoning on knowledge graphs[J]. arXiv preprint arXiv:2010.04029, 2020.
  
  RNNLogicpaper

• Sadeghian A, Armandpour M, Ding P, et al. Drum: End-to-end differentiable rule mining on knowledge graphs[J]. Advances in Neural Information Processing Systems, 2019, 32.
  DRUMpaper

• Meilicke C, Fink M, Wang Y, et al. Fine-grained evaluation of rule-and embedding-based systems for knowledge graph completion[C]//International semantic web conference. Springer, Cham, 2018: 3-20.
  ruleNpaper

• Yang F, Yang Z, Cohen W W. Differentiable learning of logical rules for knowledge base reasoning[J]. Advances in neural information processing systems, 2017, 30.
  
  Neural LPpaper

3.(Embeddings)Triple-based Reasoning

• Wang F, Zhang Z, Sun L, et al. DiriE: Knowledge Graph Embedding with Dirichlet Distribution[C]//Proceedings of the ACM Web Conference 2022. 2022: 3082-3091.
  

  狄利克雷分布（该方法在嵌入结点和关系时，如何保证entity和relation的embeddings满足分布？）

  DiriE paper

• Sun Z, Deng Z H, Nie J Y, et al. Rotate: Knowledge graph embedding by relational rotation in complex space[J]. arXiv preprint arXiv:1902.10197, 2019.
  

  复数空间

  RotatEpaper

• Yang B, Yih W, He X, et al. Embedding entities and relations for learning and inference in knowledge bases[J]. arXiv preprint arXiv:1412.6575, 2014.
  DistMultpaper

• Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data[J]. Advances in neural information processing systems, 2013, 26.
  

  欧氏空间

  TransEpaper

4.complex FOL（first-order logic） queries reasoning

• Zhu Z, Galkin M, Zhang Z, et al. Neural-Symbolic Models for Logical Queries on Knowledge Graphs[J]. arXiv preprint arXiv:2205.10128, 2022.
  GNN-QEpaper

• Das R, Godbole A, Naik A, et al. Knowledge Base Question Answering by Case-based Reasoning over Subgraphs[C]//International Conference on Machine Learning. PMLR, 2022: 4777-4793.
  CBR-SUBGpaper

• Das R, Godbole A, Monath N, et al. Probabilistic case-based reasoning for open-world knowledge graph completion[J]. arXiv preprint arXiv:2010.03548, 2020.
  PCBRpaper

• Das R, Godbole A, Dhuliawala S, et al. A simple approach to case-based reasoning in knowledge bases[J]. arXiv preprint arXiv:2006.14198, 2020.
  CBRpaper

• Chen, T., Kornblith, S., Norouzi, M., and Hinton, G. A simple framework for contrastive learning of visual representations. In ICML, 2020
  

  the normalized temperature-scaled cross entropy loss (NT-Xent)paper

• Multi-Similarity Loss with General PairWeighting for Deep Metric Learning
  MS losspaper

5.GNN

• Schlichtkrull M, Kipf T N, Bloem P, et al. Modeling relational data with graph convolutional networks[C]//European semantic web conference. Springer, Cham, 2018: 593-607.
  
  R-GCNpaper
• Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks[J]. arXiv preprint arXiv:1609.02907, 2016.
  
  GCNpaper
• Veličković P, Cucurull G, Casanova A, et al. Graph attention networks[J]. arXiv preprint arXiv:1710.10903, 2017.
  GATpaper
• GraphSAGE

6.NLP,prompt

• Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30.
  

  self-attention transformer paper

• Devlin J, Chang M W, Lee K, et al. Bert: Pre-training of deep bidirectional transformers for language understanding[J]. arXiv preprint arXiv:1810.04805, 2018.
  

  bertpaper

• Liu P, Yuan W, Fu J, et al. Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing[J]. arXiv preprint arXiv:2107.13586, 2021.
  

  Prompt surveypaper

• Wang X, Zhou K, Wen J R, et al. Towards Unified Conversational Recommender Systems via Knowledge-Enhanced Prompt Learning[C]//Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. 2022: 1929-1937.
  

  CRS对话推荐系统（prompt）

  UniCRSpaper

7.NLP + GNN

• Lv X, Lin Y, Cao Y, et al. Do Pre-trained Models Benefit Knowledge Graph Completion? A Reliable Evaluation and a Reasonable Approach[C]//Findings of the Association for Computational Linguistics: ACL 2022. 2022: 3570-3581.
  

  PKGCpaper

• Févry T, Soares L B, FitzGerald N, et al. Entities as experts: Sparse memory access with entity supervision[J]. arXiv preprint arXiv:2004.07202, 2020.
  

  EaEpaper

• Verga P, Sun H, Soares L B, et al. Facts as experts: Adaptable and interpretable neural memory over symbolic knowledge[J]. arXiv preprint arXiv:2007.00849, 2020.
  

  FaEpaper

• Yasunaga M, Ren H, Bosselut A, et al. QA-GNN: Reasoning with language models and knowledge graphs for question answering[J]. arXiv preprint arXiv:2104.06378, 2021.
  

  QA-GNNpaper

附录：AI会议汇总（CCF A类）

• NeurIPS（Neural Information Processing Systems）11，12月

• KDD

• ICDM

• WWW（International World Wide Web Conference） 4月，5月

  交叉，新兴，综合领域的顶级会议

  Core Conference Ranking A*类会议，H5指数80，Impact Score 14.69

• IJCAI（International Joint Conference on Artificial Intelligence）

  人工智能领域中最主要的学术会议之一

  Core Conference Ranking A*类会议，H5指数74，Impact Score 11.38

• ICML（International Conference on Machine Learning）5月

  人工智能，机器学习领域难度最高的国际会议

  Core Conference Ranking A*类会议，H5指数171，Impact Score 17.48

• ICLR（不是CCF A?）

论文缩写

w.r.t. = with respect to

i.e.= id est = 也就是说

sota = state of the art