基于空间投影和关系路径的地理知识图谱表示学习

摘要

Humanoid intelligence has developed rapidly and it benefits from the complete knowledge graph especially elementary education knowledge graph represented by geography.The traditional knowledge graph is represented by network leading to high computation complexity.This paper puts forward a new algorithm named PTransW(Path-based TransE and Considering Relation Type by Weight).It combines the space projection with the semantic infor-mation of relation path,taking advantage of the semantic information of relation type for further improvement.The experiment results on the FB15K and GEOGRAPHY data sets show that the ability of dealing with complex relation in knowledge graph is improved significantly by PTransW model.%近年来,类人智能技术和相关产品飞速发展,这在很大程度上得益于完备知识图谱的构建,特别是以地理为代表的基础教育知识图谱.传统的知识图谱采用网络知识组织形式进行表示,计算复杂度较高,而且三元组的知识表示形式不能有效地度量和利用实体间语义关联关系.该文构建了基于空间投影和关系路径的知识表示学习算法—PTransW(Path-based TransE and Considering Relation Type by Weight)模型,该模型结合空间投影和关系路径来对翻译模型进行扩展,并加入关系类型的语义信息进行改进.最后,在FB15K数据集和GEOGRAPHY数据集上训练并做链接预测实验.实验结果表明,PT ransW模型对复杂关系的建模能力取得了较大地提升;对于规模较小的数据集,复杂度低的T ransE和T ransR模型将会训练得更充分;但是PT ransE和PT ransW模型由于利用了关系路径和反向关系中的语义信息,在关系预测方面有很大的优势.