Ontologies are being used increasingly to promote the reusability of scientific information by allowing heterogeneous data to be integrated under a common, normalized representation. Definitions play a central role in the use of ontologies both by humans and by computers. Textual definitions allow ontologists and data curators to understand the intended meaning of ontology terms and to use these terms in a consistent fashion across contexts. Logical definitions allow machines to check the integrity of ontologies and reason over data annotated (...) with ontology terms to make inferences that promote knowledge discovery. Therefore, it is important not only to include in ontologies multiple types of definitions in both formal and in natural languages, but also to ensure that these definitions meet good quality standards so they are useful. While tools such as Protégé can assist in creating well-formed logical definitions, producing good definitions in a natural language is still to a large extent a matter of human ingenuity supported at best by just a small number of general principles. For lack of more precise guidelines, definition authors are often left to their own personal devices. This paper aims to fill this gap by providing the ontology community with a set of principles and conventions to assist in definition writing, editing, and validation, by drawing on existing definition writing principles and guidelines in lexicography, terminology, and logic. (shrink)

Definitions vary according to context of use and target audience. They must be made relevant for each context to fulfill their cognitive and linguistic goals. This involves adapting their logical structure, type of content, and form to each context of use. We examine from these perspectives the case of definitions in ontologies.

The translational research community, in general, and the Clinical and Translational Science Awards (CTSA) community, in particular, share the vision of repurposing EHRs for research that will improve the quality of clinical practice. Many members of these communities are also aware that electronic health records (EHRs) suffer limitations of data becoming poorly structured, biased, and unusable out of original context. This creates obstacles to the continuity of care, utility, quality improvement, and translational research. Analogous limitations to sharing objective data in (...) other areas of the natural sciences have been successfully overcome by developing and using common ontologies. This White Paper presents the authors’ rationale for the use of ontologies with computable semantics for the improvement of clinical data quality and EHR usability formulated for researchers with a stake in clinical and translational science and who are advocates for the use of information technology in medicine but at the same time are concerned by current major shortfalls. This White Paper outlines pitfalls, opportunities, and solutions and recommends increased investment in research and development of ontologies with computable semantics for a new generation of EHRs. (shrink)

To understand what ontologies do through their definitions, we propose a theoretical explanation of the functions of definitions in ontologies backed by empirical neuropsychological studies. Our goal is to show how these functions should motivate (i) the systematic inclusion of definitions in ontologies and (ii) the adaptation of definition content and form to the specific context of use of ontologies.

Changes in an upper level ontology have obvious conse-quences for the domain ontologies that use it at lower levels. It is therefore crucial to document the changes made between successive versions of ontologies of this kind. We describe and apply a method for tracking, explaining and measuring changes between successive versions of upper level ontologies such as the Basic Formal Ontology (BFO). The proposed change-tracking method extends earlier work on Realism-Based Ontology Versioning (RBOV) and Evolutionary Terminology Auditing (ETA). We describe (...) here the application of this evaluation method to changes between BFO 1.0, BFO 1.1, and BFO 2.0. We discuss the issues raised by this application and describe the extensions which we added to the original evaluation schema in order to account for changes in an ontology of this type. Our results show that BFO has undergone eight types of changes that can be systematically explained by the extended evaluation schema. Finally, we discuss problematic cases, possible pitfalls and certain limits of our study that we propose to address in future work. (shrink)