模糊本体合并语义不一致性自动检测和处理  

Automatic Detection and Processing of Semantic Inconsistency for Fuzzy Ontology Merging

作　　者：罗永红[1] 张友生[2] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410114 [2]湖南师范大学物理与信息科学学院,长沙410081

出　　处：《计算机工程》2013年第10期181-186,191,共7页Computer Engineering

基　　金：国家自然科学基金资助项目(51277015);湖南省科技计划基金资助项目(2011GK3114);湖南省教育厅科学研究基金资助项目(13C1003)

摘　　要：构建模糊本体能有效解决领域知识建模的不确定性与模糊性,在分布式环境中,多数据源的语义集成和异构系统的语义互操作可以通过对多个模糊本体进行合并而实现。针对模糊本体合并过程中存在的语义不一致性问题,提出一种语义不一致性映射的自动检测算法。包括语义循环不一致、subclass-of公理冗余不一致、属性不一致和不相交公理冗余不一致的子检测方法,根据不同的不一致映射类型分别采取相应的处理策略,并开发基于自动检测和处理的模糊本体合并系统。实验结果表明,该算法能节省遍历局部本体的时间和成本,模糊本体合并系统的合准率高于传统的GLUE、PROMPT、Chimaera合并系统。Fuzzy ontologies can be built to effectively deal with uncertainty and ambiguity for domain knowledge modeling. Merging multiple fuzzy local ontologies may implement semantic integration of multiple data sources and semantic interoperability between heterogeneous systems in distributed environment. In order to solve the problem of semantic inconsistency mappings for fuzzy ontology merging, this paper presents an automatic detection algorithm of semantic inconsistency mapping which includes sub detection methods of semantic circular inconsistency, subclass-of axiom redundancy inconsistency, attribute inconsistency and disjoint axioms redundancy, and adopts corresponding processing strategy according to different type of inconsistency mapping respectively, and establishes fuzzy ontology merging system based on automatic detection and processing. Experimental results demonstrate that the automatic detecting algorithm saves time and cost of traversing local ontologies, and the accuracy of this system is higher than traditional merging systems, such as GLUE, PROMPT, Chimaera.

关 键 词：模糊本体 本体合并 自动检测 映射 隶属度 语义不一致性 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程]