Relation network is constructed by discovering relations between objects. Discovering relations is challenging and
usually time consuming job. For example, most relation in protein-protein interaction networks has been discovered one
by one empirically. However, if we know some objects have similar functions, we can make inference of the relationship
between objects. 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 makes it possible to reflect that kind of knowledge and we can infer unknown relations.
In this paper, we propose a visualization method in 3D space, to examine specific relation network based on a proper
ontology structure. To gather related ontology terms, we added a degree of freedom to conventional layered drawing
algorithm so that the position of the term in an ontology tree can move like a mobile. And we combined it with modified
spring embedder model to map relation network onto the ontology tree. We have used protein-protein interaction data
from Ubiquitination Information System for relation network, and Gene Ontology for ontology structure. The proposed
method lays out the protein relation data in 3D space with a meaningful distance measure. Finally, we have designed
experiments to verify the relationship between Euclidean distance of each protein and existence of interaction. The
results support that our method provides a means to discover new relation based on visualization.