Abstract—Information retrieval is a baseline of search engine systems. There is a very large amount of data published on the Internet that cannot be manually searched. However, search engine systems should not only present relevant results but also obtain new knowledge from the user's searches. For example, new knowledge in academic research areas may be presented in graph images. In this study, we utilize methods to extract graphical and textual information from graph images and store this new knowledge in an ontology. We propose a search engine system that is applicable to an ontology that contains this extractable information, which is extracted from images with graphs. The developed ontology is useful because users can acquire a considerable amount of knowledge that is discovered from the semantic relations in the ontology. To evaluate the search engine system, ten participants tested the system and responded to their feedback. The results indicate that the proposed system provides accurate and relevant results; moreover, as indicated by the higher F-measure values comparing to an Elasticsearch-based search engine system, the performance of our system is highly acceptable. It clarified that the ontology-based search engine system provides precise and concise information outperforming than the Elasticsearch-based search engine system.
Index Terms—Ontology, search engine system, graph information, semantic relations, information retrieval.
Sarunya Kanjanawattana is with Institute of Engineering, Suranaree University of Technology, Nakhonratchasima, Thailand (e-mail: email@example.com).
Masaomi Kimura is with the Department of Information Science, Shibaura Institute of Technology, Tokyo, Japan (e-mail: firstname.lastname@example.org).
Cite: Sarunya Kanjanawattana and Masaomi Kimura, "Semantic-Based Search Engine System for Graph Images in Academic Literatures by Use of Semantic Relationships," International Journal of Machine Learning and Computing vol. 9, no. 6, pp. 828-839, 2019.Copyright © 2019 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).