想定している読者
Knowledge Graph に対して教養レベルの知識を持っている方．
リソース
文献サーベイ
知識推論
- Lin, Xi Victoria, Richard Socher, and Caiming Xiong. Multi-hop knowledge graph reasoning with reward shaping. arXiv preprint arXiv:1808.10568 (2018).
- Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018, April). Variational reasoning for question answering with knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
- Gu, L., Xia, Y., Yuan, X., Wang, C., & Jiao, J. (2018). Research on the model for tobacco disease prevention and control based on case-based reasoning and knowledge graph. Filomat, 32(5).
- Zhang, Y., Dai, H., Kozareva, Z., Smola, A. J., & Song, L. (2018, April).Variational reasoning for question answering with knowledge graph. In Thirty-Second AAAI Conference on Artificial Intelligence.
- Trivedi, R., Dai, H., Wang, Y., & Song, L. (2017, August). Know-evolve: Deep temporal reasoning for dynamic knowledge graphs. In Proceedings of the 34th International Conference on Machine Learning-Volume 70 (pp. 3462-3471). JMLR. org.
- Hamilton, W., Bajaj, P., Zitnik, M., Jurafsky, D., & Leskovec, J. (2018).Embedding logical queries on knowledge graphs. In Advances in Neural Information Processing Systems (pp. 2026-2037).
埋め込みの論文
参照：https://github.com/npubird/KnowledgeGraphCourse/blob/master/README.md
---Review---
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Philip, S. Y. (2021). [A survey on knowledge graphs: Representation, acquisition, and applications. IEEE Transactions on Neural Networks and Learning Systems, 33(2), 494-514.] (https://arxiv.org/pdf/2002.00388.pdf%E2%80%8Barxiv.org)
---Basic Models---
Turian J, Ratinov L, Bengio Y. Word representations: A simple and general method for semi-supervised learning. Proceedings of the 48th annual meeting of the association for computational linguistics. Association for Computational Linguistics, 2010: 384-394. (one-hot)
Bordes A, Glorot X, Weston J, et al. Joint learning of words and meaning representations for open-text semantic parsing. Artificial Intelligence and Statistics. 2012: 127-135. (UM)
Bordes A, Weston J, Collobert R, et al. Learning structured embeddings of knowledge bases. AAAI. 2011. (SE)
Mikolov T, Sutskever I, Chen K, et al. Distributed representations of words and phrases and their compositionality. NIPS2013: 3111-3119.
---Translation-based Models(Basic Models)---
Bordes A, Usunier N, Garcia-Duran A, et al. Translating embeddings for modeling multi-relational data. NIPS2013: 2787-2795.(TransE)
Wang Z, Zhang J, Feng J, et al. Knowledge graph embedding by translating on hyperplanes. AAAI2014.(TransH)
Lin Y, Liu Z, Sun M, et al. Learning entity and relation embeddings for knowledge graph completion. AAAI2015.(TransR/CTransR)
Ji G, He S, Xu L, et al. Knowledge graph embedding via dynamic mapping matrix. ACL2015: 687-696. (TransD)
Ji G, Liu K, He S, et al. Knowledge graph completion with adaptive sparse transfer matrix. AAAI. 2016. (TansSparse)
---Translation-based Models(Translation Requirements Relaxing)---
Fan M, Zhou Q, Chang E, et al. Transition-based knowledge graph embedding with relational mapping properties. Proceedings of the 28th Pacific Asia Conference on Language, Information and Computing. 2014. (TransM)
Xiao H, Huang M, Zhu X. From one point to a manifold: Knowledge graph embedding for precise link prediction. arXiv preprint arXiv:1512.04792, 2015. (ManifoldE)
Feng J, Huang M, Wang M, et al. Knowledge graph embedding by flexible translation. Fifteenth International Conference on the Principles of Knowledge Representation and Reasoning. 2016. (TransF)
Xiao H, Huang M, Hao Y, et al. TransA: An adaptive approach for knowledge graph embedding. arXiv preprint arXiv:1509.05490, 2015. (TransA)
---Translation-based Models(Gaussian Distribution Models)---
He S, Liu K, Ji G, et al. Learning to represent knowledge graphs with gaussian embedding. Proceedings of the 24th ACM International on Conference on Information and Knowledge Management. ACM, 2015: 623-632. (KB2E)
Xiao H, Huang M, Zhu X. TransG: A generative model for knowledge graph embedding. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016, 1: 2316-2325. （TransG）
---Semantic Matching Models(Matrix Factorization Models)---
Jenatton R, Roux N L, Bordes A, et al. A latent factor model for highly multi-relational data. NIPS. 2012: 3167-3175. (LFM)
Nickel M, Tresp V, Kriegel H P. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML. 2011, 11: 809-816. (RESCAL)
Yang B, Yih W, He X, et al. Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575, 2014. (DistMult)
Nickel M, Rosasco L, Poggio T. Holographic embeddings of knowledge graphs. AAAI. 2016. (HolE)
Trouillon T, Welbl J, Riedel S, et al. Complex embeddings for simple link prediction. International Conference on Machine Learning. 2016: 2071-2080. (ComplEx)
Liu H, Wu Y, Yang Y. Analogical inference for multi-relational embeddings. Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017: 2168-2178. (ANALOGY)
---Semantic Matching Models(Neural Network Models)---
Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (SLM)
Bordes A, Glorot X, Weston J, et al. A semantic matching energy function for learning with multi-relational data. Machine Learning, 2014, 94(2): 233-259. (SME)
Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 601-610. (MLP)
Liu Q, Jiang H, Evdokimov A, et al. Probabilistic reasoning via deep learning: Neural association models. arXiv preprint arXiv:1603.07704, 2016. (NAM)
Dettmers T, Minervini P, Stenetorp P, et al. Convolutional 2d knowledge graph embeddings. AAAI. 2018. (ConvE)
---Multi-source Information Fusion Models(Entity Type)---
Guo S, Wang Q, Wang B, et al. Semantically smooth knowledge graph embedding. Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2015, 1: 84-94. (SSE)
Xie R, Liu Z, Sun M. Representation Learning of Knowledge Graphs with Hierarchical Types. IJCAI. 2016: 2965-2971. (TKRL)
---Multi-source Information Fusion Models(Relation Paths)---
Lin Y, Liu Z, Luan H, et al. Modeling relation paths for representation learning of knowledge bases. arXiv preprint arXiv:1506.00379, 2015. (PTransE)
Dong X, Gabrilovich E, Heitz G, et al. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2014: 601-610. (MLP+PRA)
Nickel M, Jiang X, Tresp V. Reducing the rank in relational factorization models by including observable patterns. NIPS. 2014: 1179-1187. (PRA+RESCAL)
---Multi-source Information Fusion Models(Textual Descriptions)---
Socher R, Chen D, Manning C D, et al. Reasoning with neural tensor networks for knowledge base completion. NIPS. 2013: 926-934. (NTN)
Xie R, Liu Z, Jia J, et al. Representation learning of knowledge graphs with entity descriptions. AAAI. 2016. (DKRL)
Xiao H, Huang M, Meng L, et al. SSP: semantic space projection for knowledge graph embedding with text descriptions. AAAI. 2017. (SSP)
Wang Z, Li J Z. Text-Enhanced Representation Learning for Knowledge Graph. IJCAI. 2016: 1293-1299. (TEKE)
Wang Z, Zhang J, Feng J, et al. Knowledge graph and text jointly embedding. EMNLP. 2014: 1591-1601.
---Multi-source Information Fusion Models(Logical Rules)---
Wang Q, Wang B, Guo L. Knowledge base completion using embeddings and rules. IJCAI. 2015.
Guo S, Wang Q, Wang L, et al. Jointly embedding knowledge graphs and logical rules. EMNLP. 2016: 192-202. (KALE)
Guo S, Wang Q, Wang L, et al. Knowledge graph embedding with iterative guidance from soft rules. AAAI. 2018. (RUGE)
Ding B, Wang Q, Wang B, et al. Improving knowledge graph embedding using simple constraints. arXiv preprint arXiv:1805.02408, 2018.
---Multi-source Information Fusion Models(Entity Attributes)---
Nickel M, Tresp V, Kriegel H P. Factorizing yago: scalable machine learning for linked data. Proceedings of the 21st international conference on World Wide Web. ACM, 2012: 271-280.
---Multi-source Information Fusion Models(Temporal Information)---
Jiang T, Liu T, Ge T, et al. Encoding temporal information for time-aware link prediction. Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing. 2016: 2350-2354.
---Multi-source Information Fusion Models(Graph Structure)---
Feng J, Huang M, Yang Y. GAKE: graph aware knowledge embedding. COLING. 2016: 641-651. (GAKE)
Graph アルゴリズムライブラリ
データマネジメントのツール
Knowledge Graphに関するコンペ
- Knowledge Graph推論チャレンジhttps://challenge.knowledge-graph.jp/2022/
Knowledge Graphに関する学会・研究会
- SIGSWO(人工知能学会セマンティックウェブとオントロジー研究会) https://www.sigswo.org/home