计算机科学 ›› 2020, Vol. 47 ›› Issue (1): 199-204.doi: 10.11896/jsjkx.181202351
许飞翔1,叶霞1,李琳琳1,曹军博1,王馨2
XU Fei-xiang1,YE Xia1,LI Lin-lin1,CAO Jun-bo1,WANG Xin2
摘要: 不同作战部队在指挥信息系统测试评估中建立的指标存在异构问题,导致在信息交互和测试数据共享上存在较大困难。实现指标本体概念的映射和集成,建立一个统一的全局指标本体树可以有效地解决该问题,其中本体概念相似度计算的准确性至关重要。针对现有本体概念相似度计算模型中存在的精度不高的问题,提出了基于模拟退火改进BP(Back Propagation)神经网络(Simulated Annealing Back Propagation,SA-BP)算法的相似度综合计算模型。首先,对经典的基于语义距离、信息内容和概念属性的相似度计算模型进行改进,同时提出了基于概念子节点重合度的相似度计算模型;然后,采用SA-BP算法进行相似度综合计算,避免现有方法中人为确定权重的主观性和简单线性加权的不准确性问题;最后,从某作战部队不同单位建立的各异的指挥信息系统评估指标的本体概念中提取样本数据,对相似度综合计算模型进行训练测试。实验数据表明,相比于PSO-BP计算模型和主成分分析确定权值的线性加权计算模型,基于SA-BP算法的相似度综合计算模型的计算结果与专家评价结果的Pearson相关系数分别提升了0.0695和0.1351,达到了极强相关的一致性。实验数据充分说明,模拟退火算法改进的BP神经网络在训练后可以较好地收敛,在综合计算本体概念相似度时更加准确,从而有效地解决了本体概念集成的关键问题。
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