Knowledge Graph Error Detection with Contrastive Confidence Adaption
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2023
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| _version_ | 1866909074734448640 |
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| author | Liu, Xiangyu Liu, Yang Hu, Wei |
| author_facet | Liu, Xiangyu Liu, Yang Hu, Wei |
| contents | Knowledge graphs (KGs) often contain various errors. Previous works on detecting errors in KGs mainly rely on triplet embedding from graph structure. We conduct an empirical study and find that these works struggle to discriminate noise from semantically-similar correct triplets. In this paper, we propose a KG error detection model CCA to integrate both textual and graph structural information from triplet reconstruction for better distinguishing semantics. We design interactive contrastive learning to capture the differences between textual and structural patterns. Furthermore, we construct realistic datasets with semantically-similar noise and adversarial noise. Experimental results demonstrate that CCA outperforms state-of-the-art baselines, especially in detecting semantically-similar noise and adversarial noise. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2312_12108 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Knowledge Graph Error Detection with Contrastive Confidence Adaption Liu, Xiangyu Liu, Yang Hu, Wei Computation and Language Artificial Intelligence Knowledge graphs (KGs) often contain various errors. Previous works on detecting errors in KGs mainly rely on triplet embedding from graph structure. We conduct an empirical study and find that these works struggle to discriminate noise from semantically-similar correct triplets. In this paper, we propose a KG error detection model CCA to integrate both textual and graph structural information from triplet reconstruction for better distinguishing semantics. We design interactive contrastive learning to capture the differences between textual and structural patterns. Furthermore, we construct realistic datasets with semantically-similar noise and adversarial noise. Experimental results demonstrate that CCA outperforms state-of-the-art baselines, especially in detecting semantically-similar noise and adversarial noise. |
| title | Knowledge Graph Error Detection with Contrastive Confidence Adaption |
| topic | Computation and Language Artificial Intelligence |
| url | https://arxiv.org/abs/2312.12108 |