To allow deeper semantic understanding of strings, string matching with synonyms has recently received increasing attention. However, all the works focus on string semantics, which requires matching of entire strings, and cannot handle partial matching with substring semantics, resulting in its limited applications. To remedy this issue, we first propose a novel similarity measure between two strings allowing substring matching with synonyms, and develop an efficient algorithm to find the strings that have a substring semantically similar to the query string. Since considering synonyms on substrings enlarges the search space significantly, we devise efficient filtering methods to reduce the number of expensive similarity computations. Experiments with real-life datasets show the efficiency of our algorithm.