Pykg2vec: a Python library for knowledge graph embedding. (English) Zbl 1541.68328
Summary: Pykg2vec is a Python library for learning the representations of the entities and relations in knowledge graphs. Pykg2vec’s flexible and modular software architecture currently implements 25 state-of-the-art knowledge graph embedding algorithms, and is designed to easily incorporate new algorithms. The goal of pykg2vec is to provide a practical and educational platform to accelerate research in knowledge graph representation learning. Pykg2vec is built on top of PyTorch and Python’s multiprocessing framework and provides modules for batch generation, Bayesian hyperparameter optimization, evaluation of KGE tasks, embedding, and result visualization. Pykg2vec is released under the MIT License and is also available in the Python Package Index (PyPI). The source code of pykg2vec is available at https://github.com/Sujit-O/pykg2vec.
References:
| [1] | Mehdi Ali, Charles Tapley Hoyt, Daniel Domingo-Fernandez, Jens Lehmann, and Hajira Jabeen. Biokeen: A library for learning and evaluating biological knowledge graph embeddings.bioRxiv, page 475202, 2018. |
| [2] | James Bergstra, R´emi Bardenet, Yoshua Bengio, and Bal´azs K´egl. Algorithms for hyperparameter optimization.Advances in neural information processing systems, 24:2546- 2554, 2011. |
| [3] | Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. Translating embeddings for modeling multi-relational data. InNeural Information Processing Systems (NIPS), pages 1-9, 2013. |
| [4] | Antoine Bordes, Xavier Glorot, Jason Weston, and Yoshua Bengio. A semantic matching energy function for learning with multi-relational data.Machine Learning, 94(2):233-259, 2014. · Zbl 1319.68169 |
| [5] | Luca Costabello, Sumit Pai, Chan Le Van, Rory McGrath, and Nicholas McCarthy. Ampligraph: a library for representation learning on knowledge graphs, mar 2019. |
| [6] | Tim Dettmers, Pasquale Minervini, Pontus Stenetorp, and Sebastian Riedel. Convolutional 2d knowledge graph embeddings. InThirty-Second AAAI Conference on Artificial Intelligence, 2018. |
| [7] | Miao Fan, Qiang Zhou, Emily Chang, and Fang Zheng. Transition-based knowledge graph embedding with relational mapping properties. InProceedings of the 28th Pacific Asia conference on language, information and computing, pages 328-337, 2014. |
| [8] | Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. Openke: An open toolkit for knowledge embedding. InProceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 139-144, 2018. |
| [9] | Guoliang Ji, Shizhu He, Liheng Xu, Kang Liu, and Jun Zhao. Knowledge graph embedding via dynamic mapping matrix. 2015. |
| [10] | Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. Learning entity and relation embeddings for knowledge graph completion. 2015. |
| [11] | Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. A three-way model for collective learning on multi-relational data. 2011. |
| [12] | Maximilian Nickel, Lorenzo Rosasco, and Tomaso Poggio.Holographic embeddings of knowledge graphs. 2016. |
| [13] | Baoxu Shi and Tim Weninger. Proje: Embedding projection for knowledge graph completion. InThirty-First AAAI Conference on Artificial Intelligence, 2017. |
| [14] | Daniel Smilkov, Nikhil Thorat, Charles Nicholson, Emily Reif, Fernanda B Vi´egas, and Martin Wattenberg. Embedding projector: Interactive visualization and interpretation of embeddings.arXiv preprint arXiv:1611.05469, 2016. |
| [15] | Richard Socher, Danqi Chen, Christopher D Manning, and Andrew Ng. Reasoning with neural tensor networks for knowledge base completion. 2013. |
| [16] | Zhiqing Sun, Zhi-Hong Deng, Jian-Yun Nie, and Jian Tang. Rotate: Knowledge graph embedding by relational rotation in complex space. 2019. |
| [17] | Th´eo Trouillon, Johannes Welbl, Sebastian Riedel, ´Eric Gaussier, and Guillaume Bouchard. Complex embeddings for simple link prediction. pages 2071-2080, 2016. |
| [18] | Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. Knowledge graph embedding by translating on hyperplanes. 2014. |
| [19] | Han Xiao, Minlie Huang, and Xiaoyan Zhu. Transg: A generative model for knowledge graph embedding. pages 2316-2325, 2016. |
| [20] | Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. Embedding entities and relations for learning and inference in knowledge bases. 2014. |
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