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Draft only Using Multiple Reference Ontologies : Managing Composite Annotations

There are a growing number of reference ontologies available across a variety of biomedical domains and current research focuses on their construction, organization and use. An important use case for these ontologies is annotation—where users create metadata that access concepts and terms in reference ontologies. We draw on our experience in physiological modeling to present a compelling use case that demonstrates the potential complexity of such annotations. In the domain of physiological biosimulation, we argue that most annotations require the use of multiple reference ontologies. We suggest that these “composite” annotations should be retained as a repository of knowledge about post-coordination that promotes sharing and interoperation across biosimulation models. Connecting multiple reference ontologies We define a reference ontology as a carefullyconstructed ontology that aims to completely cover a specific realm or domain of knowledge. By definition, such an ontology must be both broad and deep in its domain, and designed for reusability across multiple sorts of users and use cases. In biology, one goal of the OBO resource (http://obofoundry.org/) is to encourage the development of non-overlapping reference ontologies so that users can unambiguously access terms from such ontologies. In biology, an exemplar reference ontology is the Foundational Model of Anatomy (FMA). Ontologies are most effective when they are designed with specific use cases in mind. For many, the motivating use case has been annotation: users need to add unambiguous semantic metadata about their raw data, whether that data is from genomic research, clinical findings, or images. To date, the conception of these annotations has been relatively simple. For example, a gene expression level from some experimental result will be annotated in-line with a Gene Ontology (GO) id, or possibly a direct URI to the relevant GO term. Annotations (even simple ones) provide a compelling justification for ontology development. Annotations allow external users, or even computer systems to explore and automatically align data and results across multiple sources. This use of annotations requires users to carry out two sorts of tasks: (1) annotating source data against ontologies, and (2) searching and integrating data from sources that use those ontologies for annotation. As others have pointed out, these tasks fit well into the intelligent information retrieval capabilities of the semantic web. In this paper, we argue that this relatively simple use of annotation and ontologies can become very complex if annotations include multiple ontologies. Our domain of interest is in biosimulation, where researchers build models for understanding pathology or physiology. We show that when researchers annotate such models, they need to use multiple orthogonal ontologies. We present our preliminary architecture for these composite annotations, and describe prototype tools and ideas for the two user tasks described above: Annotating biosimulation models and then searching and integrating those models. As we show, these annotations provide a solution to one case of the postvs. pre-coordination problem: there are too many properties of too many biological entities to attempt to pre-coordinate all combinations. Instead, via composite annotations, users can postcoordinate concepts as needed, and store those combinations of terms across ontologies that are useful and relevant for their tasks. Without retaining this knowledge, ontology developers and end users are faced with a combinatorial problem—a cross product of terms across many large orthogonal ontologies. The Biophysical Semantics of Biosimulation For several years, our research group has been developing systems and ontologies for use with physiological biosimulation models. Recently, researchers have aimed at building a complete Physiome, a flexible integration of component models into largescale or special-purpose biosimulations for application to clinical and investigatory problems. Toward this goal, a number of libraries of biosimulation models have been made available, notably BioModels (an SBML collection, http://www.ebi.ac.uk/biomodels/), the CellML repository (http://www.cellml.org), and the JSim library (http://physiome.org/jsim/).