Ontology as a means for systematic biology
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Biologists use ontologies as a method to organize and publish their acquired knowledge. Computer scientists have shown the value of ontologies as a means for knowledge discovery. This dissertation makes a number of contributions to enable systematic biologists to better leverage their ontologies in their research. Systematic biology, or phylogenetics, is the study of evolution. “Assembling a Tree of Life” (AToL) is an NSF grand challenge to describe all life on Earth and estimate its evolutionary history. AToL projects commonly include a study a taxon (organism) to create an ontology to capture its anatomy. Such anatomy ontologies are manually curated based on the data from morphology-based phylogenetic studies. Annotated digital imagery, morphological characters and phylogenetic (evolutionary) trees are the key components of morphological studies. Given the scale of AToL, building an anatomy ontology for each taxon manually is infeasible. The primary contribution of this dissertation is automatic inference and concomitant formalization required to compute anatomy ontologies. New anatomy ontologies are formed by applying transformations on an existing anatomy ontology for a model organism. The conditions for the transformations are derived from observational data recorded as morphological characters. We automatically created the Cypriniformes Gill and Hyoid Arches Ontology using the morphological character data provided by the Cypriniformes Tree of Life (CTOL) project. The method is based on representing all components of a phylogenetic study as an ontology using a domain meta-model. For this purpose we developed Morphster, a domain-specific knowledge acquisition tool for biologists. Digital images often serve as proxies for natural specimens and are the basis of many observations. A key problem for Morphster is the treatment of images in conjunction with ontologies. We contributed a formal system for integrating images with ontologies where images either capture observations of nature or scientific hypotheses. Our framework for image-ontology integration provides opportunities for building workflows that allow biologists to synthesize and align ontologies. Biologists building ontologies often had to choose between two ontology systems: Open Biomedical Ontologies (OBO) or the Semantic Web. It was critical to bridge the gap between the two systems to leverage biological ontologies for inference. We created a methodology and a lossless round-trip mapping for OBO ontologies to the Semantic Web. Using the Semantic Web as a guide to organize OBO, we developed a mapping system which is now a community standard.