Information Retrieval for Ontology-Based Biological Data
Khin Myo Sett
Dr. Khin Myo Sett, Department of Computer Studies, Mandalay University, Mandalay, Myanmar.
Manuscript received on June 30, 2018. | Revised Manuscript received on July 12, 2018. | Manuscript published on August 15, 2018. | PP: 6-12 | Volume-3 Issue-5, August 2018. | Retrieval Number: E0191073518
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© The Authors. Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: This paper provides a brief overview of the information retrieval process and also describes the main classic information retrieval models such as keyword-based information retrieval and ontology-based information retrieval. A brief overview of image retrieval system is discussed in this chapter. Some categories of image retrieval such as keywords-based image retrieval, content-based image retrieval, and ontology-based image retrieval are surveyed in this chapter. Evaluation methods of ontology-based biological information system are also presented. Graph database and other types of graph database are illustrated and Neo4j graph database in this research area are described. This paper describes the architecture of ontology-based biological information system in detail. Taxonomy and ontology concept are discussed in the ontology construction model. Biology graph database structure is discussed over the state of the art which aims to motivate and introduced the strength of neo4j database internal design. This work discusses the implementation of the proposed system. Java implementation is also provided in this chapter. Evaluation framework is discussed for the ontology based biological information system. Unified Modeling Language (UML) diagrams are described in this chapter. This work describes the query results and discussed the analysis of these experimental results.
Keywords: Keyword-Based Information Retrieval, Ontology-Based Information Retrieval, Biological Information System, Graph Database.