Building a term suggestion and ranking system based on a probabilistic analysis model and a semantic analysis graph

Term suggestion is a kind of information retrieval technique that attempts to suggest relevant terms to help users formulate more effective queries and reduce unnecessary search steps. In this paper, we apply two semantic analysis methods, the probabilistic analysis model and semantic analysis graph, to design a term suggestion system that can effectively deal with the problems of synonymy and polysemy. The main contributions of this paper are the following. First, we apply two semantic analysis methods to design a high‐performance term suggestion system. Second, we design an intelligent mechanism that can effectively balance cost and performance to minimize the number of iterations required for our system.