基于本体的知识融合框架

Ontology-Based Knowledge Fusion Framework

为了提高融合知识的语义规范性和准确性,提出一个包含元知识集构建、知识测度指标确定、知识融合算法设计和融合知识后处理等功能模块的知识融合框架.根据知识元素在领域本体中的关系分析知识融合的模式,提出知识融合过程的元知识集构建方法;运用最大熵模型分析知识元素的语义相关性,并综合知识单元间的关系强度及其权重构建融合知识测度,给出了知识融合算法的规则、退火遗传融合算法的关键操作和基于信息扩散原理的知识评价机制.最后通过实例证明,文中的知识融合框架有利于控制知识融合结果的规模,提高了知识的语义相关性和准确度.

An ontology-based knowledge fusion framework is presented to improve semantic specification and accuracy of fusion-knowledge. The framework includes the construction of meta- knowledge sets, calculation of fusion-knowledge metric, knowledge fusion algorithm, and post- processing for fusion-knowledge. Knowledge fusion pattern is analyzed and the construction method for the meta-knowledge set is presented according to the relationship between knowledge elements in domain ontology. Semantic relativity is analyzed using maximum entropy models. Fusion-knowledge metric with relationship strength and weight of knowledge elements is formulated. Genetic simulated annealing fusion algorithm, fusion rules and evaluation mechanism based on information diffusion theory are developed. Finally, the effectiveness of the knowledge fusion framework is demonstrated by an illustrative example. The results show that it is beneficial to control the scale of new knowledge, and improve semantic relativity and accuracy of fusion-knowledge.