Applied ontology

Ontology

Ontology is the study of entities that exist and the properties of their existence. Applied ontology concerns itself with the application of such principles to build knowledge frameworks for specific domains such as medicine, biology, geography, etc. The main motivations for building ontologies are to make propositions with precise meaning and to enable computers to automate data processing. ICD-9, 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.

Philosophy

Munn and Smith, consider two prevailing schools of thought: metaphysics (term-oriented or realism) and epistemological (concept-oriented or conceptualism). “Aristotle believed that reality in its entirety could be represented with a single system.” Kant, however, believed that we reconcile reality through the concepts we form in our mind. For example, how can we perceive that a beaver has a flat tail without first defining a beaver as a rodent with a flat tail? The other main argument for conceptualism is that reality is too complex to be adequately specified under one terminology. An argument against conceptualism is that the ontology must fit in entities which have no actual representation in reality like the caloric or ether. The response to this was that realism can be fallible and that multiple perspectives can be true. Fallible realism states that can assert propositions we believe to be true, and change our beliefs, or the ontology, when we find evidence indicating otherwise. Perspectivism acknowledges that facts can be partitioned in ways that are different but nonetheless true concerning reality. The realist perspective also deals with terms about non-existent entities an empty terms. Given this, the Munn and Smith have a preferred stance towards realism when developing a knowledgebase. Definition An ontology is made up of its entities and the relationships between them. An entity is specified by its preferred term, synonyms, and definition. Terms can either refer to real-world entities or concepts concerning real world entities. If we want to build a usable ontology, Cimino’s desiderata proposes some useful properties to adhere to:

• Concepts which form the nodes of the terminology must correspond to at least one meaning (non-vagueness)
• Concepts must correspond to no more than one meaning (non-ambiguity)
• Meanings must themselves correspond to no more than one concept (non-redundancy)

Munn and Smith identify Cimino’s usage of the phrase concept to mean a “plurality of words” and that context can influence the meaning of those words.

The other aspects to consider is whether a reference to an entity is in general or specific, and whether it is an existant or continuant. A general entity or term refers to the universal representations of itself, whereas a specific entity is tied to a particular instantiation. For example, if one were to consider the term cancer, they would think of all the general characteristics ascribed to that disease. If one were to consider an individual’s cancer, then they view the disease with respect to its instantiation in the individual. An existant refers to items that have no temporal constraint, or beginning and ending. An occurant applies a time frame to the entity. The concept of a horse is timeless in the realm of the mind until one considers a particular horse that has a concrete birth and death associated with it. These aspects are actually part of a more general set of ontological relations: inhere_in, characterize, instantiate, and exemplify. These represent interactions between existants (universals) and occurants (individuals) as they occur in Aristotle’s Ontological square. Other relationships exist, such as is_a, is_not_a, part_of, has_part, located_in, has_participant, etc. Other relationships can be devised if they make sense within the context of the problem the ontology is addressing.

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