异常实体感知知识图谱补全

Keyi Sun, Shuo Yu, Ciyuan Peng, Xiang Li, Mehdi Naseriparsa, Feng Xia
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引用次数: 0

摘要

在现实场景中,知识图仍然是不完整的,并且包含异常信息,例如冗余、矛盾、不一致、拼写错误和异常值。知识图的这些缺点可能会影响许多应用程序的服务质量。尽管提出了许多方法来完成知识图补全,但它们都无法处理知识图的异常信息。因此,为了解决知识图补全任务中的异常信息问题,我们设计了一种新的知识图补全框架ABET,该框架特别关注异常实体。ABET由两个部分组成:a)异常实体预测和b)知识图谱补全。首先,预测组件对知识图中的异常实体进行自动预测。然后,补全分量根据不同的关系有效地捕获异构结构信息和邻居的高阶特征。实验表明,ABET是一种有效的知识图谱补全框架,与基线相比有了显著的改进。进一步验证了ABET对于具有异常实体的知识图补全任务的鲁棒性。
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Abnormal Entity-Aware Knowledge Graph Completion
In real-world scenarios, knowledge graphs remain incomplete and contain abnormal information, such as redundan-cies, contradictions, inconsistencies, misspellings, and abnormal values. These shortcomings in the knowledge graphs potentially affect service quality in many applications. Although many approaches are proposed to perform knowledge graph completion, they are incapable of handling the abnormal information of knowledge graphs. Therefore, to address the abnormal information issue for the knowledge graph completion task, we design a novel knowledge graph completion framework called ABET, which specially focuses on abnormal entities. ABET consists of two components: a) abnormal entity prediction and b) knowledge graph completion. Firstly, the prediction component automati-cally predicts the abnormal entities in knowledge graphs. Then, the completion component effectively captures the heterogeneous structural information and the high-order features of neighbours based on different relations. Experiments demonstrate that ABET is an effective knowledge graph completion framework, which has made significant improvements over baselines. We further verify that ABET is robust for knowledge graph completion task with abnormal entities.
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