A novel vector space model for tree based concept similarity measurement

The attribute based vector space model generalizes standard representations of similarity concept in terms of tree architecture. In the model, every concept in the hierarchical tree has its collections of attributes including common and distinctive parts, and the probability of the attributes attached to the concept. A concept is represent as an attribute based vector space, and the similarity is described as feature matching process with cosine similarity measure. The model contains node depth information, node density information of the tree architecture inherent and hidden in it, we show that this measure compares favorably to other measures. This measure is flexible in that it can make comparisons between any two concepts in a hierarchical tree without regard to corpus and dictionary information.