In semantic-based query expansion, computing term-concept association is a key step in finding associated concepts to describe the needed query. A method called K2CM (keyword to concept method) is proposed to compute the term-concept association. In K2CM, the attaching relationship among term, document and concept together with term-concept co-occurrence relationship are introduced to compute term-concept association. The attaching relationship derives from the fact that a term is attached to some concepts in annotated corpus, where a term is in some documents and the documents are labeled with some concepts. For term-concept co-occurrence relationship, it is enhanced by the text distance and the distribution feature of term-concept pair in corpus. Experimental results of semantic-based search on three different corpuses show that compared with classical methods, semantic-based query expansion on the basis of K2CM can improve search effectiveness.