Information science is widely adapted to various fields as biology, sociology, and economics. The basic work required to informatics is computerized information, such as, database and networks. To construct a database, we have to arrange properties of objects, and to build network, we have to find out relations between objects. To discover relation between different objects, we need to prove it one by one empirically. Usually, it is time consuming, besides, the number of works increases exponentially as the number of objects grows. But, if we know some objects are close to another or some objects have similar functions, we can make inference of the relationship between objects by that kind of knowledge. And these inferences can avoid false trial and errors in discovering relations. Ontology is a structured representation of conceptual knowledge. This hierarchical knowledge can be applied at inference of relation between objects. Objects with similar functions share similar ontology terms. Therefore, combining relation network with ontology make possible to reflect that kind of knowledge and we can infer unknown relations from it. In this paper, we have proposed a visualization method in 3D space, to verify specific relation network based on proper ontology structure. For the visualization algorithm, we have modified conventional graph drawing algorithm to map relation network on ontology tree and o gather related ontology terms. We have used protein-protein interaction data for relation network, and Gene Ontology for ontology structure. Proposed method visualizes proteins onto 3D space. We have designed experiments for relationship between Euclidean distance of each protein and existence of interaction. The results support our ideas and gave directions to future works.