Fish Ontology framework for taxonomy-based fish recognition

Ali, N.M. and Khan, H.A. and Then, A.Y.H. and Chong, V.C. and Gaur, M. and Dhillon, S.K. (2017) Fish Ontology framework for taxonomy-based fish recognition. PeerJ, 5. e3811. ISSN 2167-8359

Full text not available from this repository.
Official URL: http://dx.doi.org/10.7717/peerj.3811

Abstract

Life science ontologies play an important role in Semantic Web. Given the diversity in fish species and the associated wealth of information, it is imperative to develop an ontology capable of linking and integrating this information in an automated fashion. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information on unknown fish based on metadata restrictions. It is designed to support knowledge discovery, provide semantic annotation of fish and fisheries resources, data integration, and information retrieval. Automated classification for unknown specimens is a unique feature that currently does not appear to exist in other known ontologies. Examples of automated classification for major groups of fish are demonstrated, showing the inferred information by introducing several restrictions at the species or specimen level. The current version of FO has 1,830 classes, includes widely used fisheries terminology, and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish species globally, the FO will be an indispensable tool for fish scientists and other interested users.

Item Type: Article
Uncontrolled Keywords: Biodiversity; Bioinformatics; Fish Ontology; Fisheries; Life data technology; Semantic Web; Taxonomy
Subjects: Q Science > Q Science (General)
Q Science > QH Natural history
Divisions: Faculty of Science > Institute of Biological Sciences
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 18 Sep 2018 04:31
Last Modified: 18 Sep 2018 04:31
URI: http://eprints.um.edu.my/id/eprint/19246

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