摘要
针对空节点的最大近似搜索问题,提出一种基于多层次策略的贪心算法,区别对待相邻的空节点,使用粗糙集定义空节点表示模型;添加不同标识符,构建最大近似搜索模型;结合启发式策略,改进节点数据结构。通过比较版本之间的增量,减少空节点之间的对比计算量。实验表明:本算法能够有效降低空节点迭代次数,提高映射效率。
Abstract:Aiming at the problem of maximum approximate search for blank nodes,a greedy method based on different strategies is proposed to distinguish adjacent nodes from each other.The rough node is used to define the empty node representation model.The maximum approximate search model is constructed by adding different identifiers.The node data structure is improved by heuristic strategy.The amount of comparative computation of blank nodes is reduced by comparing the increments between versions.Experiments show that this algorithm can effectively reduce the number of blank nodes iteration and improve the mapping efficiency.
作者
王凯
杨枢
WANG Kai;YANG Shu;WANG Kai(Department of Health Management,Bengbu Medical College,Bengbu,Anhui 233030,China;School of Information and Computer,Hefei University of Technology,Hefei 233009,China)
出处
《成都师范学院学报》
2018年第3期118-124,共7页
Journal of Chengdu Normal University
基金
安徽省高校自然科学重点研究项目“基于统计模型检验和风险分析的婴儿培养箱安全性评价关键技术研究”(KJ2017A223)
蚌埠医学院科技发展基金项目“基于多态映射的医学领域本体融合理论与方法”(BYKF1717)
关键词
RDF
粗糙集
最大近似
启发式策略
RDF
rough set
maximum approximation
heuristic strategy
