Complex problems require diverse expertise and multiple techniques to solve. In order to solve such problems, complex multi-agent systems include numbers of heterogeneous agents, which may include both of human experts and autonomous agents, to work together toward some complex problems. Most complex multi-agent systems are working in open domains. Due to heterogeneities and dynamic working environments, expertise and capabilities of agents might not be well estimated and presented in the system. Therefore, how to discover useful knowledge from human and autonomous experts, make more accurate estimation for experts' capabilities and to find out suitable expert(s) to solve incoming problems ("Expert Mining") are important research issues in the area of multi-agent system. In this paper, we introduce an ontology-based approach for knowledge and expert mining in hybrid multi-agent systems. Here, ontologies are hired to describe knowledge of the system. Knowledge and expert mining processes are executed as the system handle incoming problems. In this approach, we try to embed more self-learning and self-adjusting abilities in the system, and make it more suitable for high-ability hetero-generous experts and open environments.