Incremental biomedical ontology change management through learning agents

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. © 2008 Springer-Verlag Berlin Heidelberg.