Abstract

Precision beekeeping is an emerging domain that utilizes digital technologies and tools, including sensors and artificial intelligence, to enhance the management and sustainability of apiaries. However, the lack of formalized and structured knowledge representation models hinders data integration, interoperability and decision support in the domain. This work presents a precision beekeeping ontology (PBO) designed to represent concepts related to bees, smart devices, monitoring and observation. The ontology was developed by adopting the seven-step methodology using Protégé. Domain understanding and determining the scope of the ontology were based on domain expert knowledge, literature review, standard vocabularies and upper ontologies (DUL, SSN/SOSA, AGROVOC and GEMET). Classes and class hierarchy, object and data properties were carefully defined to capture the concepts and relationships in the domain. Constraints on properties were specified and 74 instances were created based on a Kaggle beehive metrics dataset. 8 competence questions were predefined to determine the scope of knowledge, and later, 6 other related competence questions were defined and successfully executed based on internal hive temperature and hive weight by running SPARQL queries on the Apache Jena-Fuseki server. Domain expert validation and structural evaluation framework were applied and logical consistency was checked by HermiT reasoner in Protégé, using completeness, relevance, accuracy and clarity as evaluation criteria.