Agent and Multi-Agent Systems: Technologies and Applications

Volume 4953 of the series Lecture Notes in Computer Science pp 526-535

Incremental Biomedical Ontology Change Management through Learning Agents

  • Arash Shaban-NejadAffiliated withDepartment of Computer Science and Software Engineering, Concordia University
  • , Volker HaarslevAffiliated withDepartment of Computer Science and Software Engineering, Concordia University

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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.


Bio-Ontologies Multi-Agent Learning Change Management Category Theory