面向路网匹配的层次化语义相似性度量模型

Hierarchical Semantic Similarity Metric Model Oriented to Road Network Matching

当前路网主要借助属性表中若干特征项的属性信息对其进行语义相似性评估,很少顾及到路网的分级层次结构、空间拓扑信息以及邻域要素信息,一定程度上影响着语义相似性评估结果的准确性。针对上述问题,本文提出一种“整体(骨架树)→部分(同构子树)→个体(stroke)”的路网层次化语义相似性度量模型,该模型顾及了路网属性特征项、上下级拓扑关联和邻域POI的语义信息,突破了传统模型仅以路网属性特征项的语义信息作为相似性评估指标的局限性。①利用stroke技术表达路网,并对其进行分级;②将路网数据映射到关联骨架树,进而建立考虑其层次性的路网相似性度量模型;③利用层次分析法和熵权法分别确定约束指标权值,并通过加权法计算路网的语义相似度。将该模型应用到路网匹配实验中,并与既有模型进行对比,结果表明利用本文提出的语义相似性度量模型,同时结合同构子树进行道路匹配,不仅可以提高匹配结果的准确性,而且可以提高匹配效率。对于文中案例所选的路网,拓扑关联语义信息对匹配结果的影响较邻域POI语义信息更显著,且与遍历法相比,以同构子树作为参照进行路网匹配,其匹配速率得到明显提升。

The road network data have characteristics such as multiple sources and heterogeneity,which affect the data sharing and integrating to some extent.As a solution to deal with this problem,this study proposes a same-name road matching technology,which mainly depends on road similarity metric and its matching strategy.The semantic similarity is a more effective metric than geometric similarity,therefore it is of great theoretical value and practical significance to conduct road network matching based on semantic similarity metric.At present,the semantic similarity of road networks is mainly evaluated by the attribute information of some feature items in the attribute table,with little concern for the hierarchical structure of road network,spatial topology information,and neighborhood element information,which has limitation in the estimation accuracy of the semantic similarity results.To address the above problems,a hierarchical semantic similarity metric model named"whole(skeleton tree)→part(isomorphic subtree)→individual(stroke)"is proposed in this paper,in which the semantic information of attribute feature items,topological association of upper and lower classes,and POIs in the neighborhood of the road network is taken into account,thereby overcoming the limitation of the traditional model.Firstly,the road network is expressed using stroke technique and ranked hierarchically.Next,the road network data are mapped to the associated skeleton tree according to the hierarchical relationship between various classes of stroke,and then a road network similarity metric model is established considering the hierarchical nature.Finally,the weights of constraint indexs are determined using hierarchical analysis and entropy weighting method respectively,and the semantic similarity of the road network is calculated using the weighting method.The model proposed in this paper is verified in the road network matching experiments and compared with the existing iterative model.The results show that the proposed matching road network using the semantic similarity metric model combined with isomorphic sub-trees can not only improve the accuracy of matching results but also increase the matching efficiency.From the case study of the road network conducted in the paper,the topology association semantic information has a more significant impact on the matching results than that of neighborhood POI semantic information,and the matching efficiency is remarkably improved when using isomorphic subtrees as reference for road network matching compared with the iterative method.