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

Sarunya Kanjanawattana, Masaomi Kimura

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publicationEAI International Conference on Technology, Innovation, Entrepreneurship and Education - TIE’2017
EditorsAngelica Reyes-Munoz, Victor Callaghan, David Crawford, Ping Zheng
PublisherSpringer Verlag
Pages121-134
Number of pages14
ISBN (Print)9783030022419
DOIs
Publication statusPublished - 2019 Jan 1
Event1st International Conference on Technology, Innovation, Entrepreneurship and Education, TIE 2017 - Canterbury, United Kingdom
Duration: 2017 Sep 112017 Sep 12

Publication series

NameLecture Notes in Electrical Engineering
Volume532
ISSN (Print)1876-1100
ISSN (Electronic)1876-1119

Conference

Conference1st International Conference on Technology, Innovation, Entrepreneurship and Education, TIE 2017
CountryUnited Kingdom
CityCanterbury
Period17/9/1117/9/12

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ASJC Scopus subject areas

  • Industrial and Manufacturing Engineering

Cite this

Kanjanawattana, S., & Kimura, M. (2019). Semantic-based search engine system for graph images in academic literature. In A. Reyes-Munoz, V. Callaghan, D. Crawford, & P. Zheng (Eds.), EAI International Conference on Technology, Innovation, Entrepreneurship and Education - TIE’2017 (pp. 121-134). (Lecture Notes in Electrical Engineering; Vol. 532). Springer Verlag. https://doi.org/10.1007/978-3-030-02242-6_10