TransE和TransH模型空间地址表示学习中的对比研究——以广州市天河区为例

将地理知识融入空间地址，研究空间信息与语义信息融合的知识表示学习方法；将空间地址数据集在TransE模型和TransH模型上进行训练，通过元组分类和向量间距离评估的方法进行对比研究.研究结果表明:(1)在地址实体的表示学习任务中，TransH模型在对复杂关系的建模任务上明显优于TransE模型；(2)在语义知识基础上融入空间关系，能够有效地解决地址实体语义相似而空间距离不相近和空间距离相近而语义不相似的两大问题.语义关系与空间关系的融合，将能够挖掘更多有价值的信息，有利于进一步开展地理知识图谱的补全工作，可为地理知识图谱表示学习提供方法借鉴.

Comparing TransE and TransH Algorithms in Spatial Address Representation Learning: A Case Study of Tianhe District,Guangzhou City

The knowledge representation learning methods of spatial and semantic information fusion are studied by integrating geographic knowledge into spatial address. Spatial address data sets are trained on two classical translation models of TransE and TransH, and a comparative study is conducted through tuple classification and distance evaluation between vectors. The results of the study show that, in the representation learning task of address entities, the TransH algorithm model is significantly better than the TransE algorithm model in modeling complex relationships. The integration of semantic knowledge and spatial relationship can effectively solve the problems of address entities lacking correspondence between semantic similarity and spatial distance similarity. The fusion of the semantic relationship and the spatial relationship will be able to reveal more valuable information, help to complete the geographic knowledge graph and provide reference for the study of the geographic knowledge graph.