关键词: 语义相似度度量, 地理本体, 反向传播（BP）神经网络, 粒子群算法

Abstract: In view of the existing measurement method in the consideration not comprehensive and the calculation of index weights are determined on the basis of experience, this paper presents the geographic ontology concept semantic similarity measurement model based on PSO-BP—BP neural network optimized by particle swarm optimization. The model uses the properties of ontology, ontology structure and semantic relationship similarity, combines with the comprehensive weighted information calculation concept similarity. At the same time, the particle swarm optimization algorithm is used to optimize the BP neural network to obtain the factor weight, avoiding artificial subjective interference to determine factors weights in the existing methods. Finally, from the basic concepts of geographic information extracted 200 groups of samples, with 190 of group as the training set, the neural network model is trained to obtain the value of weights and the remaining 10 groups as a test set. Comparing the new model with several commonly used algorithms, by analyzing the correlation coefficient between algorithm results and expert judge results of the test set, it shows that the new model can more correctly solve the similarity of the concept of geographic ontology, in line with the characteristics of human cognition, more effective.

Key words: semantic similarity measurement, geographic ontology, Back Propagation（BP） neural network, Particle Swarm Optimization（PSO）