The measurement of concept similarity is defined by semantic similarity and semantic distance.
相似度的度量由语义相似度和语义距离来定义。
The present semantic similarity algorithms are analyzed and a hybrid similarity measure is proposed based on semantic distance and dynamic weight.
分析了现有的概念相似度的算法，提出了基于语义距离和动态权重的混合概念间语义相似度算法。
The traditional methods measure semantic similarity by computing semantic distance between nodes, which are difficult to ensure the accuracy of computation.
传统的语义相似度计算方法大都利用结点间的语义距离来衡量，难以保证计算的准确性。
The results indicated that semantic distance in terms of commonsense relations between words is useful to predict the performance of the free association test.
结果表明，以两词间所保持的常识关系来定义的语义距离，对预测自由联想的作业是有效的。
The paper proposes a model of concept semantic similarity computation based on thesaurus and semantic distance, and describes its computation process in detail.
提出基于叙词表、基于距离的概念语义相似度计算方法，详细叙述其计算流程。
The Web prefetching method based on weighted semantic distance induces the user interests and reflects the concept relations of the terms by weighted semantic distance.
基于带权语义距离的网页预取方法对用户兴趣进行归纳，用带权语义距离反映词与词之间的概念关系。
Semantic Web; Ontology; Adaptive Learning; Learner Model; Knowledge Management; Intelligent Reasoning; Cognitive Ability; Constructivism; Semantic Distance; Semantic Similarity.
语义网；本体；自适应学习；学习者模型；知识管理；智能推理；认知能力；建构主义； 语义距离； 语义相似度。
The relationship between the object and the class is established through the relevance of the attribute to the fuzzy class and the semantic distance between the object and the class.
利用属性与类的相关和对象与类间的语义距离来建立对象与类的关系；利用属性与类的相关和类与子类间的语义距离来建立类与子类的关系。
All kinds of approaches to computing the semantic matching degree on IFS, including those of near compactness, semantic distance, similarity and compound conditions match, are discussed.
讨论了计算直觉模糊集语义匹配度的若干途径，包括贴近度、语义距离、相似度、复合条件的匹配等。
The relationship between the class and the sub-class is established through the relevance of the attribute to the fuzzy Glass and the semantic distance between the class and the sub-class.
利用属性与类的相关和类与子类间的语义距离来建立类与子类的关系。同时，讨论了模糊类层次中多重继承的问题。
Because the influence of semantic asymmetry and semantic density has not been considered in current concept similarity computation based on semantic distance, the computation result is not accurate.
因现有基于语义距离的概念相似度计算方法未考虑语义不对称性和语义密度的影响，导致计算结果不够准确。
The model is based on different semantic relationships, and is estimated according to maximum likelihood estimation. Semantic distance is used to estimate semantic relationships in estimating period.
该模型定义在不同语义关系之上，基于极大似然估计法利用语义距离来对语义关系进行参数估计。
In semantic similarity, we think about the semantic distance and the characteristics of ontology, the concept's amount of information, the depth of concept, the density of concept and symmetry factor;
语义相似度考虑了语义距离和本体库特征，加入概念的信息量、概念的深度、概念的密度和不对称因子的辅助影响；
This paper presented two key problems to shorten "semantic gap" distance between low-level visual features and high-level semantic features.
为了缩短介于低层视觉特征与高层语义特征之间的“语义鸿沟”距离，提出了急需解决的两大关键问题。
The semantic relationships between multi-context key words are taken into account and the fuzzy similarity matrix is derived from non-distance computing in this method.
该方法不仅考虑了多背景关键词之间的语义关系，而且通过非距离计算得到模糊相似矩阵。
In this paper, factors that affect ontological semantic similarity are analyzed and quantified, and a distance-based semantic similarity calculation model for concepts is proposed.
该文分析和量化了影响本体语义相似度的各种因素，并提出了一种基于距离的概念语义相似度计算模型。
Recently, almost all current approaches rely on distance between low-level features for judging semantic similarity, and then understand the content of image.
目前几乎所有的图像分类方法都依赖于用图像底层特征间的距离来度量图像内容的语义相似度，实现对图像内容的理解。
In addition, based on a semantic syntax-directed translation from pure patterns to distorted patterns, a distance measure including both semantics and syntax is proposed.
这里既考虑模式的统计特征，又通过词意、句法指导下的变换来描写畸变模式的结构。
On the other hand, a proper distance metric, which reflects the semantic similarity between images, is obtained by using a distance metric learning algorithm.
另一方面，对线性融合后的特征，利用距离测度学习算法从图像训练集中学到一个距离函数以更恰当的反映图像内容间的相似度。
On the other hand, a proper distance metric, which reflects the semantic similarity between images, is obtained by using a distance metric learning algorithm.
另一方面，对线性融合后的特征，利用距离测度学习算法从图像训练集中学到一个距离函数以更恰当的反映图像内容间的相似度。
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