The wide spread use of ontologies in many scientific areas creates a wealth of ontologyannotated data and necessitates the development of ontology-based data mining algorithms. We have developed generalization and mining algorithms for discovering cross-ontology relationships via ontology-based data mining. We present new interestingness measures to evaluate the discovered cross-ontology relationships. The methods presented in this dissertation employ generalization as an ontology traversal technique for the discovery of interesting and informative relationships at multiple levels of abstraction between concepts from different ontologies. The generalization algorithms combine ontological annotations with the structure and semantics of the ontologies themselves to discover interesting crossontology relationships. The first algorithm uses the depth of ontological concepts as a guide for generalization. The ontology annotations are translated to higher levels of abstraction one level at a time accompanied by incremental association rule mining. The second algorithm conducts a generalization of ontology terms to all their ancestors via transitive ontology relations and then mines cross-ontology multi-level association rules from the generalized transactions. Our interestingness measures use implicit knowledge conveyed by the relation semantics of the ontologies to capture the usefulness of cross-ontology relationships. We describe the use of information theoretic metrics to capture the interestingness of cross-ontology relationships and the specificity of ontology terms with respect to an annotation dataset. Our generalization and data mining agorithms are applied to the Gene Ontology and the postnatal Mouse Anatomy Ontology. The results presented in this work demonstrate that our generalization algorithms and interestingness measures discover more interesting and better quality relationships than approaches that do not use generalization. Our algorithms can be used by researchers and ontology developers to discover inter-ontology connections. Additionally, the cross-ontology relationships discovered using our algorithms can be used by researchers to understand different aspects of entities that interest them.

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. / text