Abstract. Biomedical knowledge bases and ontologies constantly evolve to update the knowledge in the domain of interest. One problem in current change management methodologies is the over-reliance on human factors. Despite the advantages of human intervention in the process of ontology maintenance, including a relative increase of the overall rationality of the system, it does not guarantee reproducible results of a change. To overcome this issue, we propose using intelligent agents to discover and learn patterns for different changes and their consequences. In this paper, we present a novel multi-agent-based approach, to manage the evolving structure of biomedical ontologies. This framework aims to assist and guide ontology engineers through the change management process in general, and aids in tracking and representing the changes, particularly through the use of category theory. It provides an efficient way to automatically capture, validate, and implement a change.

Requirement volatility is an issue in software engineering in general, and in Web-based clinical applications in particular, which often originates from an incomplete knowledge of the domain of interest. With advances in the health science, many features and functionalities need to be added to, or removed from, existing software applications in the biomedical domain. At the same time, the increasing complexity of biomedical systems makes them more difficult to understand, and consequently it is more difficult to define their requirements, which contributes considerably to their volatility. In this paper, we present a novel agent-based approach for analyzing and managing volatile and dynamic requirements in an ontology-driven laboratory information management system (LIMS) designed for Web-based case reporting in medical mycology. The proposed framework is empowered with ontologies and formalized using category theory to provide a deep and common understanding of the functional and nonfunctional requirement hierarchies and their interrelations, and to trace the effects of a change on the conceptual framework.