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Semantic Annotation of Objects of Interest in Digitized Herbarium Specimens for Fine-Grained Object Classification
Published in Archana Patel, Narayan C. Debnath, Bharat Bhushan, Semantic Web Technologies, 2023
Zaenal Akbar, Wita Wardani, Taufik Mahendra, Yulia A. Kartika, Ariani Indrawati, Tutie Djarwaningsih, Lindung P. Manik, Aris Yaman
Ontologies play a crucial role in improving data aggregation and integration across the biodiversity domain in this area. They can be used to describe physical samples, sampling processes, and biodiversity observations that involve no physical sampling [24]. It has been predicted that the data will become less centralized, but the need for cross-species queries will become more common [25]. That is why ontologies would help scientists to achieve that. For example, Plant Ontology (PO) has been widely used to describe plant anatomy and morphology, as well as stages of plant development [26]. A simplified version of PO also can be used to drive a question answering dialog between non-expert users and a knowledge-base about Capsicum [27]. Another widely used ontology is the Darwin Core (DC), a standard for sharing data about the occurrence of life on earth and its associations with the environment [28]. It provides terminology for describing multiple types of information from an organism, such as taxonomic, location, and sampling protocol. It can be used to not only record the occurrence of a species at a specific time and location but also to manage alien species [29] and as a hub to connect data across multiple biodiversity information systems [30].
Building an information infrastructure of spectroscopic profiling data for food-drug quality and safety management
Published in Enterprise Information Systems, 2020
The design of the domain ontology uses a multi-layered scheme, i.e. each concept or property in the ontology can be further constrained by other terminologies. For example, test object (E0065.A0004) can be encoded by FOODON (Dooley et al. 2018; Griffiths et al. 2017); Modality (E0065.A0007) can be encoded by the PSI (Proteomics Standards Initiative) MS (Turewicz and Deutsch 2011; Mayer et al. 2013) terminology in case of mass spectrometry devices. In this way, the ontology acts as a backbone that can further reference external terminologies, to achieve extensibility and flexibility. Currently, the following terminology resources have been referenced: FOODON (Dooley et al. 2018; Griffiths et al. 2017) (20,910 items), ENVO (Buttigieg et al. 2016) (Environment Ontology, 562 items), PO (Cooper et al. 2016) (Plant Ontology, 1734 items), CHEBI (55,080 items), KEGG (Kanehisa et al. 2014) MEDICUS DGROUP (2119 terms) & DRUG (2206 terms), and PSI MS (Turewicz and Deutsch 2011; Mayer et al. 2013) (2935 terms). Extension by these terminologies gives a higher semantic granularity for concepts in the domain ontology, which helps to achieves better SDE (Structured Data Entry) and semantic interoperability (e.g. by annotating concepts with unique codes and coding systems) in actual applications.