Ontology customization for biomedical informatics

Abstract

The overall aim of the research is to explore to which extent existing semantic technologies, particularly biomedical ontologies and ontology mappings, can be customized to better serve biomedical applications. Justification for the Research Topic Semantic technologies, such as comprehensive biomedical ontologies (e.g., SNOMED CT [1]), and ontology mappings linking several ontologies (e.g., SNOMED CT, NDF-RT [2], and RxNorm [3]), are offered as valuable tools to the biomedical community. However, due to the high degree of specialization in health care and life sciences, two undesirable scenarios might occur: (i) the whole breadth of an extended biomedical ontology might be excessive for some applications and users or, on the other hand, (ii) information of interest might be missed because it is scattered among several ontologies.

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Affiliation 1. Published results [ 7 ]: Graph-traversal heuristics provided high coverage (71-96% of terms in the test sets of discharge summaries) at the expense of subset size (17-51% of the size of SNOMED CT). Logic-based techniques extracted small subsets (1%), but coverage was limited (24-55%). Filtering reduced the size of large subsets to 10% while still providing 80% coverage. The study shows that the requirements of preserving ontology entailments (key to ontology modularization), and achieving high precision (key to annotation), necessarily conflict. Attempting to satisfy the two aims simultaneously can lead to unsatisfactory results.

References

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