find it difficult to repair errors in an
ontology and may even start to doubt
the correctness of inferences. Such an
explanation typically involves computing a (hopefully small) subset of the
ontology that still entails the inference
in question and, if necessary, presenting the user with a chain of reasoning
steps.
12 The explanation in Figure 3
(produced by the Protégé 4 ontology-development tool) describes the steps
that lead to the inference mentioned
earlier with respect to the inconsistency of OceanCrustLayer.
ontology applications
The availability of tools and reasoning
systems has contributed to the increasingly widespread use of OWL, which has
become the de facto standard for ontology development in fields as diverse
as biology,
19 medicine,
18 geography,
8
geology (the Semantic Web for Earth
and Environmental Terminology project, sweet.jpl.nasa.gov/), agriculture,
20
and defense.
15 Applications of OWL
are particularly prevalent in the life sciences where OWL is used by developers
of several large biomedical ontologies,
including SNOMED, GO, and BioPAX,
mentioned earlier, as well as the Foundational Model of Anatomy ( sig.biostr.
washington.edu/projects/fm/) and the
U.S. National Cancer Institute thesaurus ( www.cancer.gov/cancertopics/ter-minologyresourceshow).
The ontologies used in these applications might have been developed specifically for the purpose or without any
particular application in mind. Many
ontologies are the result of collaborative efforts within a given community
aimed at facilitating (Web-based) information sharing and exchange; some
commercially developed ontologies
are also subject to a license fee. Many
OWL ontologies are available on the
Web, identified by a URI and should,
in principle, be available at that location. There are also several well-known
ontology libraries and even ontology
search engines (such as SWOOGLE,
swoogle.umbc.edu/) that are useful for
locating ontologies. In practice, however, applications are invariably built
around a predetermined ontology or
set of ontologies that are well understood and known to provide suitable
coverage of the relevant domains.
The importance of reasoning support in ontology applications was highlighted in a paper describing a project
in which the Medical Entities Dictionary (MED), a large ontology ( 100,210
classes and 261 properties) used at the
Columbia Presbyterian Medical Center
in New York, was converted to OWL and
checked using an OWL reasoner.
13 As reported in the paper, this check revealed
“systematic modeling errors” and a
significant number of missed subClassOf relationships that, if not corrected,
“could have cost the hospital many
missing results in various decision-sup-port and infection-control systems that
routinely use MED to screen patients.”
In another application, an extended
version of the SNOMED ontology was
checked using an OWL reasoner that
found a number of missing subClassOf relationships. This ontology is being used by the U.K. National Health
Service (NHS) to provide “a single and
comprehensive system of terms, cen-
trally maintained and updated for use
in all NHS organizations and in research” and as a key component of its
$6.2 billion “Connecting for Health” IT
program ( www.connectingforhealth.
nhs.ukhow). An important feature of
the system is that it can be extended
to provide more detailed coverage if
needed by specialized applications; for
example, a specialist allergy clinic may
need to distinguish allergies caused by
different kinds of nut so may need to
add new terms to the ontology (such as
AlmondAllergy):
Class: AlmondAllergy
equivalentTo: Allergy and
causedBy some Almond
Using a reasoner to insert this new
term into the ontology ensures it is
recognized as a subClassOf NutAllergy, something that is clearly of crucial
importance for ensuring that patients
with an AlmondAllergy are correctly
identified in the national records system as patients with a NutAllergy.
Ontologies are also widely used to
facilitate the sharing and integration
of information. The Neurocommons
project ( sciencecommons.org/proj-ects/data/) aims to provide a platform
for, for example, sharing and integrating knowledge in the neuroscience domain; a key component is an ontology
of annotations to be used to integrate
available knowledge on the Web, including major neuroscience databases.
Similarly, the Open Biomedical Ontologies Foundry ( www.obofoundry.org) is a
library of ontologies designed to facilitate international information sharing
and integration in the biomedical domain. In information-integration ap-