Semantic-based search engine system for graph images in academic literature

Abstract

It is well known that information retrieval is an essential aspect of search engine systems because 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 present in images that include graphs. In this study, we utilize methods to extract graphical and textual information from graph images and store this new knowledge in an ontology. We also 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 considerable amount of knowledge that is discovered from the semantic relations in the ontology. To evaluate the search engine system, we conducted four simulations to address four main issues. The results indicate that the proposed system provides accurate and relevant results; moreover, as indicated by the high F-measure values, the performance of the system is highly acceptable. However, we also found some limitations, which will be mitigated in a future study.