【摘要】：針對(duì)現(xiàn)有度量方法中考慮因素不夠全面和因子權(quán)重計(jì)算依據(jù)經(jīng)驗(yàn)確定的不足,提出粒子群優(yōu)化BP神經(jīng)網(wǎng)絡(luò)(PSO-BP)的地理本體概念語(yǔ)義相似度度量模型。該模型利用本體屬性、本體結(jié)構(gòu)和語(yǔ)義關(guān)系的相似度,結(jié)合權(quán)重信息計(jì)算概念的綜合相似度;同時(shí),利用粒子群算法優(yōu)化的BP神經(jīng)網(wǎng)絡(luò)獲取因子權(quán)重,避免現(xiàn)有方法中因子權(quán)重確定的人為主觀干擾。最后,從基礎(chǔ)地理信息概念中提取出200組樣本,用其中190組作為訓(xùn)練集,對(duì)神經(jīng)網(wǎng)絡(luò)模型進(jìn)行訓(xùn)練,以獲取權(quán)重;剩余10組作為測(cè)試集。將該模型和幾種常用算法進(jìn)行對(duì)比,通過(guò)分析測(cè)試集的各算法求解結(jié)果和專(zhuān)家判定結(jié)果之間的相關(guān)系數(shù),結(jié)果表明該模型計(jì)算地理本體概念的相似度更為準(zhǔn)確,符合人類(lèi)認(rèn)知特性,效果更好。
[Abstract]:In view of the fact that the factors in the existing measurement methods are not comprehensive enough and the calculation of factor weights is determined by experience, a semantic similarity measurement model of geographical ontology concept based on particle swarm optimization BP neural network (PSO-BP) is proposed. The model uses the similarity of ontology attribute, ontology structure and semantic relationship, combined with the weight information to calculate the comprehensive similarity of the concept. At the same time, the BP neural network optimized by particle swarm optimization algorithm is used to obtain the factor weight to avoid the artificial subjective interference determined by the factor weight in the existing methods. Finally, 200 groups of samples are extracted from the concept of basic geographic information, 190 of which are used as training sets to train the neural network model to obtain the weight, and the remaining 10 groups are used as the test set. The model is compared with several common algorithms, and the correlation coefficients between the results of each algorithm and the results of expert decision are analyzed. The results show that the model is more accurate in calculating the similarity of geographical ontology concepts, in line with the cognitive characteristics of human beings, and the effect is better.
【作者單位】： 信息工程大學(xué);海軍出版社;
【分類(lèi)號(hào)】：TP391.1;TP18

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本文編號(hào)：2501514