@inproceedings{han-etal-2018-openke,
title = "{O}pen{KE}: An Open Toolkit for Knowledge Embedding",
author = "Han, Xu and
Cao, Shulin and
Lv, Xin and
Lin, Yankai and
Liu, Zhiyuan and
Sun, Maosong and
Li, Juanzi",
editor = "Blanco, Eduardo and
Lu, Wei",
booktitle = "Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2018",
address = "Brussels, Belgium",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/D18-2024",
doi = "10.18653/v1/D18-2024",
pages = "139--144",
abstract = "We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on \url{http://openke.thunlp.org/}.",
}
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<abstract>We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp.org/.</abstract>
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%0 Conference Proceedings
%T OpenKE: An Open Toolkit for Knowledge Embedding
%A Han, Xu
%A Cao, Shulin
%A Lv, Xin
%A Lin, Yankai
%A Liu, Zhiyuan
%A Sun, Maosong
%A Li, Juanzi
%Y Blanco, Eduardo
%Y Lu, Wei
%S Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations
%D 2018
%8 November
%I Association for Computational Linguistics
%C Brussels, Belgium
%F han-etal-2018-openke
%X We release an open toolkit for knowledge embedding (OpenKE), which provides a unified framework and various fundamental models to embed knowledge graphs into a continuous low-dimensional space. OpenKE prioritizes operational efficiency to support quick model validation and large-scale knowledge representation learning. Meanwhile, OpenKE maintains sufficient modularity and extensibility to easily incorporate new models into the framework. Besides the toolkit, the embeddings of some existing large-scale knowledge graphs pre-trained by OpenKE are also available, which can be directly applied for many applications including information retrieval, personalized recommendation and question answering. The toolkit, documentation, and pre-trained embeddings are all released on http://openke.thunlp.org/.
%R 10.18653/v1/D18-2024
%U https://aclanthology.org/D18-2024
%U https://doi.org/10.18653/v1/D18-2024
%P 139-144
Markdown (Informal)
[OpenKE: An Open Toolkit for Knowledge Embedding](https://aclanthology.org/D18-2024) (Han et al., EMNLP 2018)
ACL
- Xu Han, Shulin Cao, Xin Lv, Yankai Lin, Zhiyuan Liu, Maosong Sun, and Juanzi Li. 2018. OpenKE: An Open Toolkit for Knowledge Embedding. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 139–144, Brussels, Belgium. Association for Computational Linguistics.