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Caps-OWKG: a capsule network model for open-world knowledge graph
International Journal of Machine Learning and Cybernetics ( IF 5.6 ) Pub Date : 2021-01-04 , DOI: 10.1007/s13042-020-01259-4
Yuhan Wang , Weidong Xiao , Zhen Tan , Xiang Zhao

Knowledge graphs are typical multi-relational structures, which is consisted of many entities and relations. Nonetheless, existing knowledge graphs are still sparse and far from being complete. To refine the knowledge graphs, representation learning is utilized to embed entities and relations into low-dimensional spaces. Many existing knowledge graphs embedding models focus on learning latent features in close-world assumption but omit the changeable of each knowledge graph.In this paper, we propose a knowledge graph representation learning model, called Caps-OWKG, which leverages the capsule network to capture the both known and unknown triplets features in open-world knowledge graph. It combines the descriptive text and knowledge graph to get descriptive embedding and structural embedding, simultaneously. Then, the both above embeddings are used to calculate the probability of triplet authenticity. We verify the performance of Caps-OWKG on link prediction task with two common datasets FB15k-237-OWE and DBPedia50k. The experimental results are better than other baselines, and achieve the state-of-the-art performance.



中文翻译:

Caps-OWKG:用于开放世界知识图的胶囊网络模型

知识图是典型的多关系结构,由许多实体和关系组成。但是,现有的知识图仍然很稀疏,还远远不够完整。为了完善知识图,利用表示学习将实体和关系嵌入到低维空间中。许多现有的知识图嵌入模型都专注于在近距离假设中学习潜在特征,但忽略了每个知识图的可变性。在本文中,我们提出了一种称为Caps-OWKG的知识图表示学习模型,该模型利用胶囊网络来捕获开放世界知识图中的已知和未知三元组特征。它结合了描述性文本和知识图,从而同时获得了描述性嵌入和结构性嵌入。然后,以上两个嵌入都用于计算三重态真实性的概率。我们使用两个常见的数据集FB15k-237-OWE和DBPedia50k验证了Caps-OWKG在链接预测任务上的性能。实验结果优于其他基准,并达到了最新水平。

更新日期:2021-01-05
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