Generative adversarial networks based on Wasserstein distance for knowledge graph embeddings
Knowledge graph embedding aims to project entities and relations into low-dimensional and continuous semantic feature spaces, which has captured more attention in recent years. Most of the existing models roughly construct negative samples via a uniformly random mode, by which these corrupted sample...
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| Published in | Knowledge-based systems Vol. 190; p. 105165 |
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| Main Authors | , , , , |
| Format | Journal Article |
| Language | English |
| Published |
Amsterdam
Elsevier B.V
29.02.2020
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0950-7051 1872-7409 |
| DOI | 10.1016/j.knosys.2019.105165 |
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| Abstract | Knowledge graph embedding aims to project entities and relations into low-dimensional and continuous semantic feature spaces, which has captured more attention in recent years. Most of the existing models roughly construct negative samples via a uniformly random mode, by which these corrupted samples are practically trivial for training the embedding model. Inspired by generative adversarial networks (GANs), the generator can be employed to sample more plausible negative triplets, that boosts the discriminator to improve its embedding performance further. However, vanishing gradient on discrete data is an inherent problem in traditional GANs. In this paper, we propose a generative adversarial network based knowledge graph representation learning model by introducing the Wasserstein distance to replace traditional divergence for settling this issue. Moreover, the additional weak supervision information is also absorbed to refine the performance of embedding model since these textual information contains detailed semantic description and offers abundant semantic relevance. In the experiments, we evaluate our method on the tasks of link prediction and triplet classification. The experimental results indicate that the Wasserstein distance is capable of solving the problem of vanishing gradient on discrete data and accelerating the convergence, additional weak supervision information also can significantly improve the performance of the model. |
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| AbstractList | Knowledge graph embedding aims to project entities and relations into low-dimensional and continuous semantic feature spaces, which has captured more attention in recent years. Most of the existing models roughly construct negative samples via a uniformly random mode, by which these corrupted samples are practically trivial for training the embedding model. Inspired by generative adversarial networks (GANs), the generator can be employed to sample more plausible negative triplets, that boosts the discriminator to improve its embedding performance further. However, vanishing gradient on discrete data is an inherent problem in traditional GANs. In this paper, we propose a generative adversarial network based knowledge graph representation learning model by introducing the Wasserstein distance to replace traditional divergence for settling this issue. Moreover, the additional weak supervision information is also absorbed to refine the performance of embedding model since these textual information contains detailed semantic description and offers abundant semantic relevance. In the experiments, we evaluate our method on the tasks of link prediction and triplet classification. The experimental results indicate that the Wasserstein distance is capable of solving the problem of vanishing gradient on discrete data and accelerating the convergence, additional weak supervision information also can significantly improve the performance of the model. |
| ArticleNumber | 105165 |
| Author | Xu, Chaoyang Guo, Wenzhong Dai, Yuanfei Wang, Shiping Chen, Xing |
| Author_xml | – sequence: 1 givenname: Yuanfei surname: Dai fullname: Dai, Yuanfei organization: College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350116, China – sequence: 2 givenname: Shiping surname: Wang fullname: Wang, Shiping organization: College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350116, China – sequence: 3 givenname: Xing surname: Chen fullname: Chen, Xing organization: College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350116, China – sequence: 4 givenname: Chaoyang surname: Xu fullname: Xu, Chaoyang organization: School of Information Engineering, Putian University, Putian 351100, China – sequence: 5 givenname: Wenzhong surname: Guo fullname: Guo, Wenzhong email: guowenzhong@fzu.edu.cn organization: College of Mathematics and Computer Sciences, Fuzhou University, Fuzhou 350116, China |
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| Keywords | Knowledge graph embedding Generative adversarial networks Wasserstein distance Weak supervision information |
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