摘要
针对知识融合产生的新知识规模庞大的问题,提出一个基于本体的融合知识测度指标.利用默认关系强度分析知识单元之间融合的紧密程度,根据词汇链的构建规则定义语义相关度,由概念本体树的语义距离计算概念之间的语义相关度,并运用最大熵模型分析融合知识的语义熵.分析知识元素属性值对融合知识的影响,确定其相应效用权重系数;综合上述指标构建对融合算法具有特定趋势指导作用的融合知识测度,并分析该测度指标对称性、确定性、非负性和扩展性等性质.应用实例表明了所提出指标的有效性,并进一步说明了融合知识测度在知识评价体系中的重要作用.
Fusion-knowledge metric based on ontology is presented to control the scale of new knowledge causing by knowledge fusion. The compact degree between knowledge units is analyzed by using tacit relationship strength. Semantic relevancy is formulated based on the construction rule of lexical chains, and calculated with the semantic distance between ontology concepts. The semantic entropy is analyzed by using the maximum entropy model. The utility weight is studied by analyzing the effect of attribute value on fusion-knowledge. On the basis of the above analysis, the fusion-knowledge metric is formulated to guide the design of the knowledge fusion algorithm, and some properties of the fusion-knowledge metric, such as symmetry, determinacy, non-negativity and expansibility, are studied. Finally, the effectiveness of fusion-knowledge metric is demonstrated by an illustrative example, and the important effect of the fusion-knowledge metric on the knowledge evaluation mechanism is discussed.
出处
《控制与决策》
EI
CSCD
北大核心
2014年第9期1649-1654,共6页
Control and Decision
基金
国家863计划项目(2008AA04Z113)
关键词
本体
融合知识测度
关系强度
效用权重
语义熵
ontology
fusion-knowledge metric
relationship strength
utility weight
semantic entropy
