Survey on knowledge graph embedding

Papers

  1. Q. Wang, Z. Mao, B. Wang and L. Guo, “Knowledge Graph Embedding: A Survey of Approaches and Applications,” in IEEE Transactions on Knowledge and Data Engineering, vol. 29, no. 12, pp. 2724-2743, 1 Dec. 2017.
  2. Maximilian Nickel, Kevin Murphy, Volker Tresp, Evgeniy Gabrilovich. A Review of Relational Machine Learning for Knowledge Graphs. Proc. IEEE, 2015.
  3. Bordes, A.; Usunier, N.; Garcia-Duran, A.; Weston, J.; and Yakhnenko, O. 2013. Translating embeddings for modeling multi-relational data. In Advances in Neural Information Processing Systems (NIPS).
  4. Liu, H.; Wu, Y.; and Yang, Y. 2017. Analogical inference for multi-relational embeddings. In Proceedings of the 34th International Conference on Machine Learning (ICML).
  5. Nickel, M.; Rosasco, L.; and Poggio, T. 2016. Holographic embeddings of knowledge graphs. In Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, AAAI’16.
  6. Nickel, M.; Tresp, V.; and Kriegel, H.-P. 2011. A threeway model for collective learning on multi-relational data. In International Conference on Machine Learning (ICML-11), ICML ’11,
  7. Trouillon, T.; Welbl, J.; Riedel, S.; Gaussier, E.; and Bouchard, G. 2016. Complex embeddings for simple link prediction. In International Conference on Machine Learning (ICML).
  8. Yang, B.; Yih, W.; He, X.; Gao, J.; and Deng, L. 2015. Embedding entities and relations for learning and inference in knowledge bases. International Conference on Learning Representations 2015.
  9. Armand Joulin, Edouard Grave, Piotr Bojanowski, Maximilian Nickel, Tomas Mikolov. Fast Linear Model for Knowledge Graph Embeddings. AKBC, 2017.
  10. Maximilian Nickel, Xueyan Jiang, Volker Tresp. Reducing the Rank in Relational Factorization Models by Including Observable Patterns. NIPS, 2014.
  11. Denis Krompaß, Maximilian Nickel, Volker Tresp. Querying Factorized Probabilistic Triple Databases. ISWC, 2014.
  12. Maximilian Nickel. Tensor Factorization for Relational Learning. PhD Thesis, 2013.
  13. Maximilian Nickel, Volker Tresp. Logistic Tensor Factorization for Multi-Relational Data. SLG, 2013.
  14. Maximilian Nickel, Volker Tresp. Tensor Factorization for Multi-Relational Learning. ECML/PKDD Nectar Track, 2013.
  15. Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel. Factorizing YAGO: Scalable Machine Learning for Linked Data. WWW, 2012.
  16. Maximilian Nickel, Volker Tresp, Hans-Peter Kriegel. A Three-Way Model for Collective Learning on Multi-Relational Data. ICML, 2011.

Web Documents

  • Knowledge Graph Embeddings by Maximilian Nickel [LINK]

GitHub

Researchers

  • Maximilian Nickel. Facebook AI Research. [LINK]

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