Fish Ontology framework for taxonomy-based fish recognition
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 (...
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| Format: | Article |
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PeerJ
2017
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| _version_ | 1825721262294433792 |
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| author | Ali, N.M. Khan, H.A. Then, Amy Yee Hui Chong, Ving Ching Gaur, M. Dhillon, Sarinder Kaur |
| author_facet | Ali, N.M. Khan, H.A. Then, Amy Yee Hui Chong, Ving Ching Gaur, M. Dhillon, Sarinder Kaur |
| author_sort | Ali, N.M. |
| collection | UM |
| description | 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. |
| first_indexed | 2024-03-06T05:47:43Z |
| format | Article |
| id | um.eprints-19246 |
| institution | Universiti Malaya |
| last_indexed | 2024-03-06T05:47:43Z |
| publishDate | 2017 |
| publisher | PeerJ |
| record_format | dspace |
| spelling | um.eprints-192462019-10-24T08:03:59Z http://eprints.um.edu.my/19246/ Fish Ontology framework for taxonomy-based fish recognition Ali, N.M. Khan, H.A. Then, Amy Yee Hui Chong, Ving Ching Gaur, M. Dhillon, Sarinder Kaur Q Science (General) QH Natural history 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. PeerJ 2017 Article PeerReviewed Ali, N.M. and Khan, H.A. and Then, Amy Yee Hui and Chong, Ving Ching and Gaur, M. and Dhillon, Sarinder Kaur (2017) Fish Ontology framework for taxonomy-based fish recognition. PeerJ, 5. e3811. ISSN 2167-8359, DOI https://doi.org/10.7717/peerj.3811 <https://doi.org/10.7717/peerj.3811>. http://dx.doi.org/10.7717/peerj.3811 doi:10.7717/peerj.3811 |
| spellingShingle | Q Science (General) QH Natural history Ali, N.M. Khan, H.A. Then, Amy Yee Hui Chong, Ving Ching Gaur, M. Dhillon, Sarinder Kaur Fish Ontology framework for taxonomy-based fish recognition |
| title | Fish Ontology framework for taxonomy-based fish recognition |
| title_full | Fish Ontology framework for taxonomy-based fish recognition |
| title_fullStr | Fish Ontology framework for taxonomy-based fish recognition |
| title_full_unstemmed | Fish Ontology framework for taxonomy-based fish recognition |
| title_short | Fish Ontology framework for taxonomy-based fish recognition |
| title_sort | fish ontology framework for taxonomy based fish recognition |
| topic | Q Science (General) QH Natural history |
| work_keys_str_mv | AT alinm fishontologyframeworkfortaxonomybasedfishrecognition AT khanha fishontologyframeworkfortaxonomybasedfishrecognition AT thenamyyeehui fishontologyframeworkfortaxonomybasedfishrecognition AT chongvingching fishontologyframeworkfortaxonomybasedfishrecognition AT gaurm fishontologyframeworkfortaxonomybasedfishrecognition AT dhillonsarinderkaur fishontologyframeworkfortaxonomybasedfishrecognition |