Authors:
Sarunya Kanjanawattana
and
Masaomi Kimura
Affiliation:
Shibaura Institute of Technology, Japan
Keyword(s):
Relationship, OCR, Ontology, Triple, Edit Distance, Dependency Parser.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Data Engineering
;
Enterprise Information Systems
;
Information Systems Analysis and Specification
;
Knowledge Acquisition
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Ontology Engineering
;
Symbolic Systems
Abstract:
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 t
o 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.
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