An explicit formal-ontological representation of entities existing at multiple levels of granularity is an urgent requirement for biomedical information processing. We discuss some fundamental principles which can form a basis for such a representation. We also comment on some of the implicit treatments of granularity in currently available ontologies and terminologies (GO, FMA, SNOMED CT).

Biomedical ontologies are generally very large and complex. Their size and complexity make quality assurance a difficult and timeconsuming task. Compact networks called abstraction networks can be derived to summarize the content and structure of ontologies and support their quality assurance. The Biomedical Layout Utility for SNOMED CT (BLUSNO) is a system for automatically deriving and visual...

We consider the problem of computing the logical difference between distinct versions of description logic terminologies. For the lightweight description logic EL, we present a tractable algorithm which, given two terminologies and a signature, outputs a set of concepts, which can be regarded as the logical difference between the two terminologies. As a consequence, the algorithm can also decid...

The development of a histopathology day book based on a microcomputer is described. The system has the capacity to search on file records for details of previous specimens from current patients. It also possesses a SNOP/SNOMED input and search system that can aggregate data for analytical and other purposes. The system is relatively inexpensive and is user friendly. It has been developed within...

We present an analysis of SNOMED CT ‘bleeding’ concepts – those concepts with descriptions that include ‘hematoma’, ‘hemorrhage’, or ‘bleeding’; or that are descended from ‘Bleeding (finding)’ in the Is-a hierarchy; or that have Hematomas or Hemorrhages as their associated morphology – to assess how consistently they are used in the ontology. Keywords—SNOMED CT, terminology, ontology

SNOMED CT is a very large biomedical terminology supported by a concept-based ontology. In recent years it has been distributed under the new release format ‘RF2’. RF2 provides a more consistent and coherent mechanism for keeping track of changes over versions, even to the extent that – in theory at least – any release will contain enough information to allow reconstruction of all previous vers...

The ability to find highly related clinical concepts is essential for many applications such as for hypothesis generation, query expansion for medical literature search, search results filtering, ICD-10 code filtering and many other applications. While manually constructed medical terminologies such as SNOMED CT can surface certain related concepts, these terminologies are inadequate as they de...

Health ontologies are commonly used as the standardization for interoperability between different health institutions. Since health knowledge changes rapidly, health ontology also evolves frequently. A mechanism of change propagation is needed to maintain the consistency between the ontology with the dependent artefacts. In this paper, we present the classification of health ontology changes ba...

SNODENT is a dental diagnostic vocabulary incompletely integrated in SNOMED-CT. Nevertheless, SNODENT could become the de facto standard for dental diagnostic coding. SNODENT's manageable size, the fact that it is administratively self-contained, and relates to a well-understood domain provides valuable opportunities to formulate and test, in controlled experiments, a series of hypothesis conce...

The analysis of the structure of SNOMED CT revealed several ontological and knowledge-engineering errors. The authors propose a methodology based on the formal top-level ontology DOLCE to correct these errors.