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.

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.

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. 15 Life science ontologies play an important role in semantic web. In the fish and fisheries research field, it 16 is imperative to have an ontology that can automatically provide information for biological objects 17 annotations and links to relevant data pieces. As such, we introduce the Fish Ontology (FO), an automated 18 classification architecture of existing fish taxa which provides taxonomic information of unknown fish 19 based on metadata restrictions. It is designed to support knowledge discovery, providing semantic 20 annotation of fish and fisheries resources, data integration, and information retrieval. The automated 21 classification for unknown specimen is a feature not existing in other known ontologies covering fish 22 species profiling and fisheries data. Examples of automated classification for major groups of fish are 23 demonstrated, showing the inferred information by introducing several restrictions at the species or 24 specimen level. The current version of FO has 1830 classes, includes widely used fisheries terminology, 25 and models major aspects of fish taxonomy, grouping, and character. With more than 30,000 known fish 26 species globally, the FO will be an indispensable tool for fish scientists and other interested users. 28 INTRODUCTION29 Increasing amount of data produced by a single species has made it harder for fish researchers to manage 30 and provide fish data in a single database. Moreover, the high demand of having related data (metadata) 31 for a single species have encourage researchers to find an alternative for the current database set. Since 32 the arrival of computational automation, it is impractical to generate species data based on human 33 observation (Perez & Benjamins, 1999). The semantic web technology provides a promising platform for 34 biodiversity r...