Developing a Taxonomic Information Model for Biological Data using Ontology
Khin Myo Sett
Dr. Khin Myo Sett, Department of Computer Studies, Mandalay University, Mandalay, Myanmar.
Manuscript received on August 04, 2018. | Revised Manuscript received on August 11, 2018. | Manuscript published on August 15, 2018. | PP: 1-7 | Volume-5, Issue-7, August 2018. | Retrieval Number: G1078075718
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Abstract: In real world applications, the amount of available information and data increases exponentially. Since this growth does not entail as just as large quality increase of the available knowledge, methods and tools are needed to filter and process this mass of information. For this purpose, ontologies which represent data and their interrelations in a computer “interpretable” form get more and more established. Because of the implementation of ontology-based biological information system is presented in this thesis. To observe the ontology-based biological information, “A Manual of Practical Zoology Chordates” written by P.S.VERMA is used as the resource available. The first implementation with Neo4j Community Server installation, configuration, and biology based ontology graph nodes are created in the database server. Taxonomic Biological ontology model is conceptually implemented in biological domain and class orientation of superclass and subclasses hierarchy structure is described. Java implementation of Mynode class, query engine class, search and admin search for any simple query and more complex query is also presented. Secondly, in the java implementation, detail search, group search and admin user for database operations are provided. The most commonly used standard format can be obtained by the use of ontology. Evaluation of taxonomic biological information retrieval using Cypher query and conceptual visualization results are presented. In this thesis, it has been evaluated consistency of the query results with the results of set theory, and the precision and the recall.
Keywords: Ontology, Biological Data, Taxonomic Biological Ontology Model.