FOODS: Ontology-based knowledge graphs for forest observatories

Hamed, Naeima ORCID: https://orcid.org/0000-0002-2998-5056, Rana, Omer ORCID: https://orcid.org/0000-0003-3597-2646, Orozco Ter Wengel, Pablo ORCID: https://orcid.org/0000-0002-7951-4148, Goossens, Benoit ORCID: https://orcid.org/0000-0003-2360-4643 and Perera, Charith ORCID: https://orcid.org/0000-0002-0190-3346 2025. FOODS: Ontology-based knowledge graphs for forest observatories. ACM Journal on Computing and Sustainable Societies 3 (1) , pp. 1-42. 10.1145/3707637

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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 .

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics Biosciences
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords:	Wildlife data, Internet of Things, Ontology, Knowledge Graph
Publisher:	Association for Computing Machinery (ACM)
ISSN:	2834-5533
Date of First Compliant Deposit:	11 November 2024
Date of Acceptance:	12 October 2024
Last Modified:	21 Jan 2025 11:45
URI:	https://orca.cardiff.ac.uk/id/eprint/175336

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