Fish Ontology framework for taxonomy-based fish recognition

Najib M. Ali; Haris A. Khan; Amy Y-Hui Then; Chong Ving Ching; Sarinder Kaur Dhillon

doi:10.7287/peerj.preprints.3020v1

Fish Ontology framework for taxonomy-based fish recognition

Najib M. Ali, Haris A. Khan, Amy Y-Hui Then, Chong Ving Ching, Sarinder Kaur Dhillon

Institute of Biological Sciences, University of Malaya, Kuala Lumpur, Malaysia

DOI: 10.7287/peerj.preprints.3020v1

Published: 2017-06-13
Accepted: 2017-06-13

Subject Areas: Aquaculture, Fisheries and Fish Science, Bioinformatics, Computational Science
Keywords: Fish Ontology, biodiversity, taxonomy, fisheries, semantic web, bioinformatics, life data technology

Copyright: © 2017 Ali et al.
Licence: This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.

Cite this article: Ali NM, Khan HA, Then AY, Ving Ching C, Dhillon SK. 2017. Fish Ontology framework for taxonomy-based fish recognition. PeerJ Preprints 5:e3020v1 https://doi.org/10.7287/peerj.preprints.3020v1

Abstract

Life science ontologies play an important role in semantic web. In the fish and fisheries research field, it is imperative to have an ontology that can automatically provide information for biological objects annotations and links to relevant data pieces. As such, we introduce the Fish Ontology (FO), an automated classification architecture of existing fish taxa which provides taxonomic information of unknown fish based on metadata restrictions. It is designed to support knowledge discovery, providing semantic annotation of fish and fisheries resources, data integration, and information retrieval. The automated classification for unknown specimen is a feature not existing in other known ontologies covering fish species proﬁling and fisheries data. 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 1830 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.