Ontology driven fish data storage and manipulation / Mohd Najib Mohd Ali

Mohd Najib , Mohd Ali (2017) Ontology driven fish data storage and manipulation / Mohd Najib Mohd Ali. Masters thesis, University of Malaya.

	PDF (The Candidate's Agreement) Restricted to Repository staff only Download (1620Kb)
Preview	PDF (Thesis M.A) Download (6Mb) | Preview

Abstract

Ontology is a vocabulary that defines the concepts and relationships (also referred as “terms”) used to describe and represent an area of concern. It is used for classifying terms of any domain of interest, which in turn characterizes possible relationships, and defines possible constraints related to the terms. Ontology provides meaning to human and computers where each ontology term will have associated metadata allowing it to have annotations, hierarchy, and relationship. Studying the role of ontologies and how to manipulate them is essential to evaluate their contribution in Semantic Web applications such as data integrations and semantic annotations. There are a number of existing fish and fisheries related databases on the internet but there are presently no specific ontology created for the fish domain. Thus there is a need to create the necessary ontology for this domain so that in the future, data for fish and fisheries can be integrated to create a large network of information. This study aims to apply semantic web applications to fish and fisheries data and to show that such data can be properly manipulated using ontology. In this study a Fish Ontology (FO) is created to show how an ontology for fish can be used to gather more information from established ontology domains related to fish, such as genetic makeup, locations, and diseases. The Fish Ontology in this study demonstrates the possibility of using ontology as an automatic fish classification tool. The methods presented in this study enable automated classification of a fish specimen based on its taxon rank, using the FO, showing how data within the ontology can be linked to other data using data manipulation such as data extraction, or deletion. Future studies should include more species in the ontology model, improved annotations, and more revised terms.

Actions (For repository staff only : Login required)