In this paper, we present an implementation method of a semantic information retrieval system using specialized and general knowledge and its application for the field of International Relations (IR). To realize our system, we apply the Semantic Associative Search Method to the system. The Semantic Associative Search Method makes it possible to compute semantic relationships between words and documents according to a given context dynamically. The important features of our system are distilled to the three points: 1) a user can obtain and analyze IR-related documents by using general words even if the user does not have special knowledge of IR, 2) a user can analyze both time-varying and source-specific semantics of IR-related documents, 3) a user can acquire IR-related information that maintains relevancies to IR expertise. This new semantic retrieval environment for IR field is realized by creating a semantic vector space where document data with metadata of both technical terms and general words can be mapped, and also by applying a learning system to the IR document database, which can adapt retrieval results to individual context and improve accuracy of the database. To verify the feasibility and the practical effectiveness of our system, we performed qualitative and quantitative experiments with the evaluation by IR experts.