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.

Abstract. With increasing popularity of using ontologies, many industrial and clinical applications have employed ontologies as their conceptual backbone. Ontologies try to capture knowledge from a domain of interest and when the knowledge changes, the definitions will be altered. We study change management in the FungalWeb Ontology, which is the result of integrating numerous biological databases and web accessible textual resources. The fungal taxonomy is currently unstable and evolves over time. This evolution can be seen in both nomenclature and the taxonomic structure. In an experiment we have focused on changes in medical species of fungus which can potentially alter the related disease name and description in an integrated clinical system. In order to address certain aspects of representation of changes in an ontology driven clinical application we propose a methodology based on category theory as a mathematical notation, which is independent of a specific choice of ontology language and any particular implementation.

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.

We propose the use of formal ontological inferencing, rather than cladistics, to reconstruct phylogeny trees and to analyze the evolutionary relationships between species. For this experiment, we focused on the phylogeny of fungi. Lexical chaining technique has been used for incremental population of evolving ontological elements. Also category theory has been employed to provide an underlying formalism for capturing and analyzing the evolutionary behavior of the system.