在总结现有的本体理论与本体学习技术的基础上,建立了一套本体学习的自适应机制,通过获取农业数据源变化信息、本体服务的需求,以及对已构建本体的评估,提出基于多Agent的农业领域本体自适应学习理论与模型,实现对农业领域本体学习规则的实时自动调整,提升农业本体构建的动态适应性及优化能力,对大规模构建农业领域本体研究具有一定的实际意义。
An adaptive mechanism of ontology learning was established based on ontology learning and ontology theory. By considering the capture of information on changed agricultural data source, the demand of ontology service and the evaluation of the ontology that has been modeled, the theory and model of ontology adaptive learning was proposed based on multi-agent, to achieve the automatic adjustment of learning rules of agricultural ontology and promotion of dynamic adaptability and optimization of agricultural ontology, which are of practical significance of modeling agricultural domain ontology in large scale.