Fish Ontology framework for taxonomy-based fish recognition

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.

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

“…Harmonizing fish data annotation with an ontology will enable easier data aggregation and analysis. One available ontology, FISHO (https://bioportal.bioontology.org/ontologies/FISHO), 18 focuses on ichthyology, diversity, and adaptation. The Food and Agriculture Organization (FAO) of the United Nations initiated several fisheries ontologies, but the ontologies available remained drafts.…”

Visualizing Scientists’ Cognitive Representation of Materials Data through the Application of Ontology

“…New discoveries of this nature have been previously reported where ontological inferences unveiled a series of new species of fish that otherwise were lost within data. 58 It is natural to assume, therefore, that discovery of materials through this approach can also occur. Ontological inferences can potentially recategorize materials data into new categories that were not traditionally associated with said materials.…”