Zhang, Guo-Qiang.
Formal Methods for the Analysis of Biomedical Ontologies [electronic resource] / by Guo-Qiang Zhang, Rashmie Abeysinghe, Licong Cui. - 1st ed. 2022. - XIV, 245 p. 119 illus., 52 illus. in color. online resource. - Synthesis Lectures on Data, Semantics, and Knowledge, 2691-2031 . - Synthesis Lectures on Data, Semantics, and Knowledge, .
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.
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 keyontological 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.
9783031121319
10.1007/978-3-031-12131-9 doi
Information retrieval.
Computer architecture.
Data structures (Computer science).
Information theory.
Artificial intelligence--Data processing.
Medicine--Research.
Biology--Research.
Ontology.
Data Storage Representation.
Data Structures and Information Theory.
Data Science.
Biomedical Research.
Ontology.
QA76.9.A73
005.72
Formal Methods for the Analysis of Biomedical Ontologies [electronic resource] / by Guo-Qiang Zhang, Rashmie Abeysinghe, Licong Cui. - 1st ed. 2022. - XIV, 245 p. 119 illus., 52 illus. in color. online resource. - Synthesis Lectures on Data, Semantics, and Knowledge, 2691-2031 . - Synthesis Lectures on Data, Semantics, and Knowledge, .
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.
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 keyontological 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.
9783031121319
10.1007/978-3-031-12131-9 doi
Information retrieval.
Computer architecture.
Data structures (Computer science).
Information theory.
Artificial intelligence--Data processing.
Medicine--Research.
Biology--Research.
Ontology.
Data Storage Representation.
Data Structures and Information Theory.
Data Science.
Biomedical Research.
Ontology.
QA76.9.A73
005.72