摘要：

大数据背景下大规模本体映射的时间复杂度较高,效率和精度较低。为此,提出一种基于模块化和局部置信度的多策略自适应大规模本体映射算法。对本体内部进行聚类和模块化,基于信息检索策略发现模块间高相似度的相关子本体,计算相关子本体间各映射策略下的局部置信度,在组合映射结果时基于局部置信度对相应策略的权值进行自适应调整。在此基础上,利用启发式贪心策略提取映射结果并基于映射规则矫正结果。实验结果表明,与Falcon、ASMOV方法相比,该算法具有较高的查全率、查准率与F-measure值。

关键词: 大数据, 大规模本体映射, 模块化, 局部置信度, 自适应

Abstract:

Large-scale ontology mapping in the context of large data has high time complexity,low efficiency and accuracy.Therefore,a multi-strategy adaptive large-scale ontology mapping algorithm based on modularity and local confidence is proposed.Clustering and modularizing the inner part of the system,discovering the correlated sub-ontologies with high similarity between modules based on information retrieval strategy,calculating the local confidence under each mapping strategy among the correlated sub-ontologies,and adjusting the weight of the corresponding strategy adaptively based on the local confidence when combining the mapping results.On this basis,heuristic greedy strategy is used to extract mapping results and correct them based on mapping rules.Experimental results show that compared with Falcon and ASMOV methods,the proposed algorithm has higher recall,precision and F-measure value.

Key words: big data, large-scale ontology mapping, modularity, local confidence, self-adaption

中图分类号: 

• TP391