Private and Shared Feature Extractors Based on Hierarchical Neighbor Encoder for Adaptive Few-Shot Knowledge Graph Completion

Canqun Yang, Weiwen Zhang
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Abstract

While Knowledge Graphs (KGs) have been applied in many AI tasks, KGs are known for being incomplete with many missing facts. Previous works rely on a large number of training data for KG completion. However, there are often few entity pairs available for most relations in KGs. In this paper, we propose a Few-shot Knowledge Graph Completion (FKGC) model, named Private and Shared feature extractors based on Hierarchical neighbor encoder for Adaptive few-shot knowledge graph completion (PSHA). In the PSHA model, we first exploit the hierarchical attention mechanism to extract the inherent and valuable hidden information of the neighborhood surrounding the entity. Following that, we adopt a private feature extractor to extract the private features of relation information of the entity pairs, and then a shared feature extractor is used to extract the shared features of the entity pairs of the support set. In addition, an adaptive aggregator aggregates entity pairs of the support set about the query. We conduct experiments on the 2-shot and 5-shot of the NELL-One and CoDEx-S-One dataset. The experimental results show that the PSHA outperforms the existing FKGC models in both scenarios.
基于层次近邻编码器的私有和共享特征提取器的自适应少镜头知识图补全
虽然知识图谱(KGs)已经应用于许多人工智能任务,但众所周知,知识图谱是不完整的,有许多缺失的事实。以前的工作依赖于大量的训练数据来完成KG。本文提出了一种基于层次邻居编码器的自适应少拍知识图补全(PSHA)模型,命名为私有和共享特征提取器。在PSHA模型中,我们首先利用层次关注机制提取实体周围邻域的固有且有价值的隐藏信息。然后,我们采用私有特征提取器提取实体对关系信息的私有特征,然后使用共享特征提取器提取支持集实体对的共享特征。此外,自适应聚合器聚合关于查询的支持集的实体对。我们在NELL-One和CoDEx-S-One数据集的2-shot和5-shot上进行了实验。实验结果表明,PSHA在两种情况下都优于现有的FKGC模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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