Quality assurance in large terminologies is a difficult issue. We present two algorithms that can help terminology developers and users to identify potential mistakes. We demon­strate the methodology by outlining the different types of mistakes that are found when the algorithms are applied to SNOMED-CT. On the basis of the results, we argue that both formal logical and linguistic tools should be used in the development and quality-assurance process of large terminologies.

To enhance the treatment of relations in biomedical ontologies we advance a methodology for providing consistent and unambiguous formal definitions of the relational expressions used in such ontologies in a way designed to assist developers and users in avoiding errors in coding and annotation. The resulting Relation Ontology can promote interoperability of ontologies and support new types of automated reasoning about the spatial and temporal dimensions of biological and medical phenomena.

One of the tasks of ontology in information science is to support the classification of entities according to their kinds and qualities. We hold that to realize this task as far as entities such as material objects are concerned we need to distinguish four kinds of entities: substance particulars, quality particulars, substance universals, and quality universals. These form, so to speak, an ontological square. We present a formal theory of classification based on this idea, including both a semantics for the (...) theory and a provably sound axiomatization. (shrink)

The present essay is devoted to the application of ontology in support of research in the natural sciences. It defends the thesis that ontologies developed for such purposes should be understood as having as their subject matter, not concepts, but rather the universals and particulars which exist in reality and are captured in scientific laws. We outline the benefits of a view along these lines by showing how it yields rigorous formal definitions of the foundational relations used in many influential (...) ontologies, illustrating our results by reference to examples drawn from the domain of the life sciences. (shrink)

Electronic Health Records (EHRs) are organized around two kinds of statements: those reporting observations made, and those reporting acts performed. In neither case does the record involve any direct reference to what such statements are actually about. They record not: what is happening on the side of the patient, but rather: what is said about what is happening. While the need for a unique patient identifier is generally recognized, we argue that we should now move to an EHR regime in (...) which all clinically salient particulars – from the concrete disorder on the side of the patient and the body parts in which it occurs to the concrete treatments given – should be uniquely identified. This will allow us to achieve interoperability among different systems of records at the level where it really matters: in regard to what is happening in the real world. It will also allow us to keep track of particular disorders and of the effects of particular treatments in a precise and unambiguous way. We discuss the ontological and epistemological aspects of our claim and describe a scenario for implementation within EHR systems. (shrink)