Difference between revisions of "Applied ontology"

Revision as of 14:46, 5 December 2011

Applied ontology (subfield of ontology) concerns itself with the application of such principles to build knowledge frameworks for specific domains such as medicine, biology, geography, etc.

Ontology

The main motivations for building ontologies are to make propositions with precise meaning and to enable computers to automate data processing. ICD, SNOMED, and MeSH often serve as examples, but the principles of building those terminologies are not often explored. This article will review the main themes in Applied Ontology, a book by Katherine Munn and Barry Smith, which provides an overview of the philosophies and principles involved in the tasks of knowledge development and curation.

Use Case

The goal of specifying these relations is to establish the most general language possible with which to perform set operations or searches on the data, such as first order logic (FOL). FOL is composed of individual terms, predicates, logical connectives, and quantifiers. Using RxNorm as an example, RxNorm has a set of terms related to medications and the relationships between them. So, we can search for: SBDF has_tradename(SCDF has_ingredient (x)) to find the semantic branded drug form of the semantic clinicial dose forms containing the ingredient x. Their representation is useful, not only in finding ingredients of a given drug, but also of being able to seamlessly transition between the different forms and representations a drug or drug order might take on.

Conclusion

Ontologies are similar to the applications in natural language processing (NLP), in that we develop them to discover, structure, and automate the knowledge we've thus far accumulated. The only difference is perhaps in their approach. Ontologies specify knowledge whereas NLP discovers knowledge, usually through the statistical interplay of words in a corpus. While ontologies concern themselves with the broader task of representing entities in the real world, they still play a key role in enabling the development of rich semantically rich user interfaces in clinical information systems. SNOMED, ICD-9, RxNorm, etc represent the initial steps in ultimately processing large volumes of clinical data feasible.

References

1. Munn K, Smith B. Applied ontology: an introduction. ontos verlag; 2008.
2. Cimino JJ, others. Desiderata for controlled medical vocabularies in the twenty-first century. Methods of Information in Medicine-Methodik der Information in der Medizin. 1998;37(4):394–403.

Submitted by Nathan Bahr