A good medical ontology is expected to cover its domain completely and correctly. On the other hand, large ontologies are hard to build, hard to understand, and hard to maintain. Thus, adding new concepts (often multi-word concepts) to an existing ontology must be done judiciously. In this research, we propose a two-stage framework for evaluating candidate concepts for ontology expansion. The framework first employs a utility function that evaluates each candidate concept based on Semantic Relevance and Redundancy Avoidance. For candidates that meet a minimum utility threshold, a secondary Goodness Function is applied to evaluate an additional qualitative aspect, namely contextual fit. This framework of combining a utility metric with a goodness metric will be helpful for expert staff working on maintaining and extending ontologies. This systematic approach will enable the incremental expansion of ontologies while maintaining both depth and contextual relevance by integrating only concepts with high “utility.”
Naren Khatwani is a Data Science Ph.D. student at the New Jersey Institute of Technology under the supervision of Dr. James Geller and Dr. Lijing Wang. His research lies in the domain of Biomedical ontologies, with a focus primarily on Incremental Ontology Expansion and Concept Goodness. He is also an adjunct instructor and a teaching assistant for graduate level courses.
Dr. James Geller is Professor and Chair of the Department of Data Science in the Ying Wu College of Computing at NJIT. He received his MS and PhD degrees from the State University of New York at Buffalo in Computer Science, with a focus on AI/Knowledge Representation. Dr. Geller cofounded SABOC (the Structural Analysis of Biomedical Ontologies Center) at the Department of Computer Science at NJIT. He has published over 200 journal papers, conference papers and book chapters in Medical Informatics, Semantic Web Technology, Object-Oriented Database Modeling, Knowledge Representation, and other topics.