Abstract
Wildlife research activities generate data on ecosystems and species interactions from varied independent projects. Forest Observatories are online platforms that curate, integrate, and analyze wildlife research data for forest monitoring. However, integrating data from disparate sources can be challenging due to data heterogeneity. This study, in collaboration with a research facility in the forest of Sabah, Malaysian Borneo, proposes a novel approach to integrate heterogeneous wildlife data for Forest Observatories. We used the Forest Observatory Ontology (FOO) to standardize wildlife data entities generated by sensors. Four semantically modeled wildlife datasets populated FOO, resulting in an ontology-based knowledge graph named FooDS (Forest Observatory Ontology Data Store). We evaluated FOO and FooDS using specialized open-source ontology scanners, domain experts’ feedback, and applied use cases. This study contributes FooDS, the first ontology-based knowledge graph for Forest Observatories, which provides accurate query responses, reasoning about data, and granular data acquisition from diverse datasets. FOO in turtle format, FOO’s documentation and FooDS in turtle format and their resource website are published at https://w3id.org/def/foo , https://w3id.org/def/fooDocs , https://w3id.org/def/fooDS , and https://ontology.forest-observatory.org .
Published Version
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