Neurobiological Ontology Development: Principles and Methods for Non-ontologists

The book is written as a practical guide for researchers who want to know more about the role ontologies play in today’s neuroscientific findings and who may want to develop ontologies for their specific research domain. It is geared as a reader for the graduate level and provides a guide to the "best practices" in neurobiological ontology development, culled from leading experts in the development and application of ontologies for representation and meta-analysis of neuroscientific data. The book is divided into four sections: Motivation, Theory, Practice, and Application. An appendix reviews current tools and choices for biomedical ontology development, sharing, and dissemination. The first section, "Motivations for Ontologies in Neurobiological Research", is an introduction to and motivation for ontologies for biomedical researchers and neuroscientists. Ontologies are defined, and examples regarding concepts, instances, classes, relationships, and reasoning are drawn from neuroscientific and clinical research wherever possible. The motivation for the application of ontologies to biomedical research is presented, drawing from the successes of the Gene Ontology (GO) and others, with some foreshadowing of the Applications found in Section 4. An overview of coordinated efforts in ontology sharing and re-use is included, so that readers can see what ontologies already exist and will know where to look for areas of ontology development and related tools for ontology-based representation in specific scientific domains. The second section, "Theory: An Ideal Ontology", focuses on the theory and formalisms underlying ontology development and application, presented with a minimum of mathematical symbols. It is expected that the readership is at most modestly familiar with first-order logic but not necessarily with more sophisticated mathematical logics or formalisms. The goal for this section is to introduce the basics of ontology design and logic-based implementation, and to explain how ontology design may affect downstream applications (such as searching and reasoning over data that have been annotated with an ontology). In addition, this section broaches a few of the current issues and controversies in ontology design and implementation that could have a practical impact on choices in building new ontologies and ontology-based applications for science. While an entire book can be written about the choices that go into ontology design, we choose to focus on educating the reader regarding the basic issues, with indications for other sources with more detail.The third section, "Practice: Where Representation Meets Reality", focuses on the general issues that biomedical researchers face when using ontologies to represent their studies and data. The distinction between top-down (knowledge-driven) and bottom-up (data-driven) methods is a key challenge: researchers often come to ontology development with a specific problem they wish to solve or a particular type of data they wish to represent and reason about. Some start by defining the lowest level concepts that are closest to the actual instances of data, and others start at the top, modeling the structure of their research process. Each of these approaches has merit, and each has challenges. Ultimately, both top-down and bottom-up methods may be needed to form ontological bridges between data and the high-level knowledge that is linked to data in a particular domain. Similarly, in the case where several ontologies may already be applicable to different portions of the data or concepts the researcher is attempting to model, the role and advantages of ontology selection or harmonization needs to be understood. The final chapter of this section includes a discussion of where theoretical purity must interact with the complexity of actually linking to the data and sometimes incomplete knowledge, and what the resultant hybrid of pure ontology and real-world data means for ontology applicatio