About the Book

The book synthesizes research on the analysis of biomedical ontologies using formal concept analysis, including through auditing, curation, and enhancement. As the evolution of biomedical ontologies almost inevitably involves manual work, formal methods are a particularly useful tool for ontological engineering and practice, particularly in uncovering unexpected "bugs" and content materials.

The book first introduces simple but formalized strategies for discovering undesired and incoherent patterns in ontologies before exploring the application of formal concept analysis for semantic completeness. The book then turns to formal concept analysis, a classical approach used in the mathematical treatment of orders and lattices, as an ontological engineering principle, focusing on the structural property of ontologies with respect to its conformation to lattice or not (non-lattice). The book helpfully covers the development of more efficient algorithms for non-lattice detection and extraction required by exhaustive lattice/non-lattice analysis. The book goes on to highlight the power and utility of uncovering non-lattice structure for debugging ontologies and describes methods that leverage the linguistic information in concept names (labels) for ontological analysis. It also addresses visualization and performance evaluation issues before closing with an overview and forward-looking perspectives on the field.

This book is intended for graduate students and researchers interested in biomedical ontologies and their applications. It can be a useful supplement for courses on knowledge representation and engineering and also provide readers with a reference for related scientific publications and literature to assist in identifying potential research topics. All mathematical concepts and notations used in this book can be found in standard discrete mathematics textbooks, and the appendix at the end of the book provides a list of key ontological resources, as well as annotated non-lattice and lattice examples that were discovered using the authors' methods, demonstrating how "bugs are fixed" by converting non-lattices to lattices with minimal edit changes.

About the Author: Guo-Qiang "GQ" Zhang is Professor of Medicine, Biomedical Informatics, and Public Health in the University of Texas Health Science Center at Houston (UTHealth Houston). He serves as Co-director for the Texas Institute for Restorative Neurotechnologies and UTHealth's Vice President and Chief Data Scientist. Before joining UTHealth Houston, he served as the inaugural Director of the Institute for Biomedical Informatics, Chief of the Division of Biomedical Informatics, and Associate Director of the Center for Clinical and Translational Science at the University of Kentucky. He spent prior years as professor in the Case School of Engineering and in the School of Medicine at Case Western Reserve University, where he created the Division of Biomedical Informatics in the School of Medicine. GQ Zhang received his Ph.D. in Computer Science from Cambridge University. His research spans clinical and research informatics, data science, neuroinformatics, and biomedical ontologies. During the last decade, he led a research group that has developed production-strength, informatics tools for data capturing, data management, cohort discovery, and clinical decision support, resulting in over 200 scientific publications and multiple awards across the National Institutes of Health (NIH) institutes and the National Science Foundation (NSF).
Licong Cui received her Ph.D. in Computer Science from Case Western Reserve University. She is an assistant professor in School of Biomedical Informatics at the University of Texas Health Science Center at Houston. Before joining UTHealth, she was an assistant professor in the Department of Computer Science and member of the Institute for Biomedical Informatics at the University of Kentucky. Her research interests include ontologies and terminologies, neuroinformatics, big data analytics, large-scale data integration and management, and information extraction and retrieval. She has been a Principal Investigator of several highly competitive research awards funded by the NIH and the NSF. She is a recipient of the prestigious NSF CAREER Award.
Rashmie Abeysinghe received his B.S. in Computer Science from University of Peradeniya, Peradeniya, Sri Lanka and Ph.D. in Computer Science from University of Kentucky. He completed a Summer Internship at the National Library of Medicine, NIH. After completing his Ph.D. study, he joined the Department of Neurology, McGovern Medical School at the University of Texas Health Science Center at Houston as a Research Scientist. His research interests revolve around biomedical ontologies particularly from a quality assurance perspective, information extraction, and deep learning. His paper won a Distinguished Paper Award at the 2021 American Medical Informatics Association (AMIA) Annual Symposium. His papers were also selected as finalists for both the 2018 and 2019 AMIA Annual Symposium Student Paper Competitions.