Fish Ontology framework for taxonomy-based fish recognition

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

doi:10.7717/peerj.3811

Cited by 8 publications

(4 citation statements)

References 45 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

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

Arnaud¹,

Laporte²,

Kim

et al. 2020

Patterns

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Summary Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

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Section: Resultsmentioning

confidence: 99%

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

Arnaud¹,

Laporte²,

Kim

et al. 2020

Patterns

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show abstract

“…The fish ontology architecture (Ref. [57] p. 6), for example, provides links between the property "extinct", the property category "fish status", and the entity "fish".…”

Section: Ontologiesmentioning

confidence: 99%

Information Retrieval and Knowledge Organization: A Perspective from the Philosophy of Science

Hjørland

2021

Information

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Information retrieval (IR) is about making systems for finding documents or information. Knowledge organization (KO) is the field concerned with indexing, classification, and representing documents for IR, browsing, and related processes, whether performed by humans or computers. The field of IR is today dominated by search engines like Google. An important difference between KO and IR as research fields is that KO attempts to reflect knowledge as depicted by contemporary scholarship, in contrast to IR, which is based on, for example, “match” techniques, popularity measures or personalization principles. The classification of documents in KO mostly aims at reflecting the classification of knowledge in the sciences. Books about birds, for example, mostly reflect (or aim at reflecting) how birds are classified in ornithology. KO therefore requires access to the adequate subject knowledge; however, this is often characterized by disagreements. At the deepest layer, such disagreements are based on philosophical issues best characterized as “paradigms”. No IR technology and no system of knowledge organization can ever be neutral in relation to paradigmatic conflicts, and therefore such philosophical problems represent the basis for the study of IR and KO.

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“…They also reported that the returned accuracy by the trained personnel and experts for distinguishing and labelling specimens is expected to be in the range of 64 % to 95 %, which is within the performance range of automated methods. Classification of specimens' images to their corresponding species requires development of models and methods that are able to characterize a species' morphology and apply this knowledge to their recognition (Wong et al, 2016;Ali et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Comparison of Fully Automated and Semi-Automated Methods for Species Identification

Kalafi,

Anuar,

Sakharkar

et al. 2018

Fol. Biol.

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The process of manual species identification is a daunting task, so much so that the number of taxonomists is seen to be declining. In order to assist taxonomists, many methods and algorithms have been proposed to develop semi-automated and fully automated systems for species identification. While semi-automated tools would require manual intervention by a domain expert, fully automated tools are assumed to be not as reliable as manual or semiautomated identification tools. Hence, in this study we investigate the accuracy of fully automated and semi-automated models for species identification. We have built fully automated and semi-automated species classification models using the monogenean species image dataset. With respect to monogeneans’ morphology, they are differentiated based on the morphological characteristics of haptoral bars, anchors, marginal hooks and reproductive organs (male and female copulatory organs). Landmarks (in the semi-automated model) and shape morphometric features (in the fully automated model) were extracted from four monogenean species images, which were then classified using k-nearest neighbour and artificial neural network. In semi-automated models, a classification accuracy of 96.67 % was obtained using the k-nearest neighbour and 97.5 % using the artificial neural network, whereas in fully automated models, a classification accuracy of 90 % was obtained using the k-nearest neighbour and 98.8 % using the artificial neural network. As for the crossvalidation, semi-automated models performed at 91.2 %, whereas fully automated models performed slightly higher at 93.75 %.

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Fish Ontology framework for taxonomy-based fish recognition

Cited by 8 publications

References 45 publications

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

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

Information Retrieval and Knowledge Organization: A Perspective from the Philosophy of Science

Comparison of Fully Automated and Semi-Automated Methods for Species Identification

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