PROJECT TITLE :

Hierarchy-Cutting Model based Association Semantic for Analyzing Domain Topic on the Web - 2017

ABSTRACT:

Association link network (ALN) can organize huge Web info to provide several intelligent services in our big data society. Effective semantic layered technologies not solely can offer theoretical support for information discovery in Web resources, however additionally can improve the looking potency of connected data systems such as Web data system and industrial information system. How to realize the layer division of association semantic by the hierarchy analysis of ALN is an important research topic. To unravel this problem, this paper proposes a hierarchy-cutting model of association semantic. First, experiments of 4 sorts of keywords with totally different linking roles are conducted to discover the possible distribution law. Experimental results show that these keywords with association role reveal previous power-law distribution. Then, primarily based on the discovered power-law distribution, up-cutting and down-cutting points are presented to divide the association semantic into 3 layers. At the same time, theories of the hierarchy-cutting model are presented. Finally, examples of current core topic and permanent topics belonging to a website are given. The experiments show that hierarchy-cutting points have high accuracy. The multilayer theory of association semantic can give a theoretical support for knowledge recommendation with different particle sizes on ALNs.