Abstract
Wildlife and preservation research activities in the tropical forest of Sabah, Malaysia, can generate a wide variety of data. However, each research activity manages its data independently. Since these data are disparate, gaining unified access to them remains a challenge. We propose the Forest Observatory Ontology (FOO) as a basis for integrating different datasets. FOO comprises a novel upper-level ontology that integrates wildlife data generated by sensors. We used existing ontological resources from various domains (i.e., wildlife) to model FOO’s concepts and establish their relationships. FOO was then populated with multiple semantically modelled datasets. FOO structure and utility are subsequently evaluated using specialised software and task-based methods. The evaluation results demonstrate that FOO can be used to answer complex use-case questions promptly and correctly.
Resource type: Ontology and Knowledge Graph.
License: Creative Commons 4.0 International SA (CC BY-SA 4.0).
Ontology’s URL https://naeima.github.io/foo_html/.
Knowledge Graph’s URL https://naeima.github.io/fooKG/.
Main website URL: https://www.ontology.forest-observatory.org.
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Hamed, N., Rana, O., Goossens, B., Orozco-terWengel, P., Perera, C. (2023). FOO: An Upper-Level Ontology for the Forest Observatory. In: Pesquita, C., et al. The Semantic Web: ESWC 2023 Satellite Events. ESWC 2023. Lecture Notes in Computer Science, vol 13998. Springer, Cham. https://doi.org/10.1007/978-3-031-43458-7_29
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DOI: https://doi.org/10.1007/978-3-031-43458-7_29
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