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-latticedetection 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.

In-depth coverage of biomedical ontologies Practical, theory-based methodology for ontology quality assurance Rigorous algorithms with visualized real-world examples

Autorentext

Guo-Qiang "GQ" Zhang is distinguished chair in digital innovation, vice president, and chief data scientist at the University of Texas Health Science Center at Houston (UTHealth Houston). He serves as co-director for the Texas Institute for Restorative Neurotechnologies and holds primary appointment as full professor in the Department of Neurology at UTHealth Houston's McGovern Medical School. He has secondary joint appointments in the McWilliams School of Biomedical Informatics and School of Public Health, UTHealth Houston. Before joining UTHealth, he served as the inaugural director for the Institute for Biomedical Informatics, chief of the Division of Biomedical Informatics, and associate director for the Center for Clinical and Translational Science (CTSA) at the University of Kentucky. He spent prior years as a faculty member at the Case School of Engineering and School of Medicine, at Case Western Reserve University, where he created its Division of Biomedical Informatics in the School of Medicine while serving as its CTSA informatics core director. Dr. Zhang received his Ph.D. in Computer Science from Cambridge University. His research work and interests span data ecosystems and big data, biomedical ontology development and quality assurance, clinical and research informatics, and data coordination systems for prospective metadata management in national consortia, such as the latest, on-going BRAIN Initiative Cell Atlas Network (BICAN). He has launched a research program called ``logic-based phenotyping,'' a formalized mathematical approach for temporal phenotype representation and reasoning using an expressive and purpose-fitting framework called "temporal ensemble logic". His work has been funded by research awards from the National Institutes of Health (NIH) and the National Science Foundation (NSF). Rashmie Abeysinghe received his B.S. degree in Computer Science from University of Peradeniya, Peradeniya, Sri Lanka in 2014 and Ph.D. degree in Computer Science from University of Kentucky, Lexington, KY, USA in 2020. He completed a Summer Internship at the National Library of Medicine, NIH in 2019. In 2020, he joined the Department of Neurology, McGovern Medical School at the UTHealth Houston, first as a Research Scientist and then as Assistant Professor. His research interests revolves around biomedical ontologies from a quality assurance perspective, information extraction, and deep learning. His paper won a Distinguished Paper Award at the 2021 AMIA Annual Symposium. His papers were also selected as finalists for both the 2018 and 2019 AMIA Annual Symposium Best Student Paper Competitions. Licong Cui received her Ph.D. in Computer Science from Case Western Reserve University (2014). She is associate professor in McWilliams School of Biomedical Informatics at UTHealth Houston. Before joining UTHealth Houston, she was an assistant professor in the Department of Computer Science and a 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 a number of highly competitive research awards funded by the NIH and the NSF. She is a recipient of the prestigious NSF CAREER Award.

Inhalt
Introduction.- Simple Relational Patterns.- Formal Concept Analysis and Semantic Completeness.- Algorithms for Extracting Non-lattice Substructures.- Non-lattice Substructures in Ontological Analysis.- Lexical Sequences and Patterns.- Visualization and Retrospective Ground-Truthing.- Conclusion.