A two dimensional graph is a powerful method for representing a set of objects that usually appears in many sources of literature. Numerous efforts have been made to discover image semantics based on contents of literature. However, conventional methods have not been fully able to satisfy users because a wide variety of techniques are being developed, and each is very useful for enhancing system capabilities in their own way. In this paper, we have developed a method to automatically extract relationships from graphs on the basic of their captions and image content, particularly from graph titles. Furthermore, we improved our idea by applying several technologies such as ontology and a dependency parser. The relationships discovered in a graph are presented in the form of a triple (subject, predicate, object). Our objectives are to find implicit and explicit information in the graph and reduce the semantic gap between an image and literature context. Accuracy was manually estimated to identify the most reliable triple. Based on our results, we concluded that the accuracy via our method was acceptable. Therefore, our method is dependable and worthy of future development.