复杂网络下基于路径选择的表示学习方法

基于路径和基于知识表示的推理是当前知识图谱领域两大主流推理方法，二者的融合算法可以提高知识推理的准确率，但是依旧存在表示学习的时候效率低下、预测准确率低、模型过拟合等若干问题.本文方法针对这些问题提出了基于路径选择的表示学习方法.对路径特征信息进行进一步的过滤和筛选，保留关键路径，在路径信息和知识表示的结合过程中使用平衡参数对缺失路径信息的三元组进行处理.使用公开数据集对模型进行测试，实验表明模型可以有效提高泛化能力和准确率. 

Representation Learning Based on Path Selection in Complex Networks

Path-based and representation-based reasoning are two major methods on knowledge inference. A combination of both algorithms can improve the accuracy of knowledge reasoning. However, there are still some problems, such as inefficiencies in learning, low prediction accuracy and over-fitting of the model. A representation learning method based on path selection was proposed in this paper to further filter the path feature information, to hold the key paths and to use the balance parameter to process the triples of missing path information. In this paper, a public data set was used to test the model. Experiments show that the model can effectively improve the generalization ability and accuracy.