考虑数据稀疏的知识图谱缺失连接自动补全算法

Automatic completion algorithm for missing links in nowledge graph considering data sparsity

针对数据稀疏的云冈石窟语料库,通过人工对语义规则的认识实现数据稀疏的补全,一定程度上影响了后续工作的准确率,为此,本文提出了考虑数据稀疏的知识图谱缺失连接自动补全算法。通过设置数据的邻域结构,构建了基于数据稀疏的知识图谱嵌入表示模型,用于抽取数据关系,采用长短时记忆网络模型自动补全变长序列中的缺失数据,实现了知识图谱缺失连接的自动补全。最后,将该算法应用于云冈石窟知识图谱的构建,从实验结果可以看出,在同一数据库集上,考虑数据稀疏的知识图谱缺失连接自动补全算法的准确率高达95.4%,远高于其他传统补全算法。

Aiming at the Yungang Grottoes corpus with sparse data,the completion of sparse data is realized through the artificial understanding of semantic rules,which affects the accuracy of the follow-up work to a certain extent.Therefore,an automatic completion algorithm for missing connection of knowledge atlas considering data sparsity is proposed,and some corresponding contents are also proposed in the text.By setting the neighborhood structure of the data,the knowledge graph embedded representation model based on data sparsity is constructed to extract the unknown relationship of the data.Then,the long-short-term memory network model is used to automatically complete the missing data in the variable-length sequence,and the automatic completion of the missing connection of the knowledge graph is realized.Finally,the algorithm is applied to the construction of the knowledge graph of Yungang Grottoes,it can be seen from the experimental results that on the same database set,considering the data sparsity,the accuracy of the algorithm is up to 95.4%,much higher than other traditional algorithms.