LibKGE - A knowledge graph embedding library for reproducible research (2020.emnlp-demos)
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| Challenge: | Knowledge graph embedding models are trained to predict false triples and high scores for true triples. |
| Approach: | LibKGE is an open-source PyTorch-based library for training, hyperparameter optimization, and evaluation of knowledge graph embedding models for link prediction. |
| Outcome: | LibKGE provides implementations of common knowledge graph embedding models and training methods, and new ones can be easily added. |
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Zhuoran Jin, Tianyi Men, Hongbang Yuan, Zhitao He, Dianbo Sui, Chenhao Wang, Zhipeng Xue, Yubo Chen, Jun Zhao
| Challenge: | Existing methods focus on entity-centric knowledge, but CogKGE supports heterogeneous knowledge. |
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| Challenge: | Existing knowledge embedding tools are available for embeddable knowledge graphs. |
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| Challenge: | Existing methods to embed learning use a standard Neural Networks (NN) backward mechanism, duplicating its memory consumption. |
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| Challenge: | Knowledge Graph (KG) embedding has emerged as a very active area of research over the last few years, resulting in the development of several embeddable methods. |
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| Challenge: | Existing knowledge graph embedding techniques lack the capability to access similarities between entities and relations. |
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| Challenge: | Existing knowledge graph embedding models suffer from limited knowledge representation due to sparse and noisy dataset annotations. |
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Tejas Chheda, Purujit Goyal, Trang Tran, Dhruvesh Patel, Michael Boratko, Shib Sankar Dasgupta, Andrew McCallum
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| Challenge: | Existing knowledge graph embedding approaches model entities and relations in KGs using real-valued, complex-value, or hypercomplex-value representations. |
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UER: An Open-Source Toolkit for Pre-training Models (D19-3)
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Zhe Zhao, Hui Chen, Jinbin Zhang, Xin Zhao, Tao Liu, Wei Lu, Xi Chen, Haotang Deng, Qi Ju, Xiaoyong Du
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