Revisit and Outstrip Entity Alignment: A Perspective of Generative Models
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866911783296434176 |
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| author | Guo, Lingbing Chen, Zhuo Chen, Jiaoyan Fang, Yin Zhang, Wen Chen, Huajun |
| author_facet | Guo, Lingbing Chen, Zhuo Chen, Jiaoyan Fang, Yin Zhang, Wen Chen, Huajun |
| contents | Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study embedding-based entity alignment (EEA) from a perspective of generative models. We show that EEA shares similarities with typical generative models and prove the effectiveness of the recently developed generative adversarial network (GAN)-based EEA methods theoretically. We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i.e., generating new entities). We mitigate this problem by introducing a generative EEA (GEEA) framework with the proposed mutual variational autoencoder (M-VAE) as the generative model. M-VAE enables entity conversion between KGs and generation of new entities from random noise vectors. We demonstrate the power of GEEA with theoretical analysis and empirical experiments on both entity alignment and entity synthesis tasks. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2305_14651 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Revisit and Outstrip Entity Alignment: A Perspective of Generative Models Guo, Lingbing Chen, Zhuo Chen, Jiaoyan Fang, Yin Zhang, Wen Chen, Huajun Computation and Language Information Retrieval Machine Learning Recent embedding-based methods have achieved great successes in exploiting entity alignment from knowledge graph (KG) embeddings of multiple modalities. In this paper, we study embedding-based entity alignment (EEA) from a perspective of generative models. We show that EEA shares similarities with typical generative models and prove the effectiveness of the recently developed generative adversarial network (GAN)-based EEA methods theoretically. We then reveal that their incomplete objective limits the capacity on both entity alignment and entity synthesis (i.e., generating new entities). We mitigate this problem by introducing a generative EEA (GEEA) framework with the proposed mutual variational autoencoder (M-VAE) as the generative model. M-VAE enables entity conversion between KGs and generation of new entities from random noise vectors. We demonstrate the power of GEEA with theoretical analysis and empirical experiments on both entity alignment and entity synthesis tasks. |
| title | Revisit and Outstrip Entity Alignment: A Perspective of Generative Models |
| topic | Computation and Language Information Retrieval Machine Learning |
| url | https://arxiv.org/abs/2305.14651 |