Transcript national center for ontological research University at Buffalo The Center for the Arts October 27, 2005

national
center for
ontological
research
University at Buffalo
The Center for the Arts
October 27, 2005
national
center for
ontological
research
national
center for
ontological
research
national
center for
ontological
research
Department of Philosophy now largest
group of core ontology faculty in the
world
New York State Center of Excellence in
Bioinformatics & Life Science
ORG: The Ontology Research Group
http://ncor.us
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Stanford Medical Informatics,
Director: Mark Musen
Protégé
Applied Ontology
National Center for Biomedical Ontology
http://ncor.us
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national
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From
chromosome
to disease
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genomics
proteomics
reactomics
metabonomics
phenomics
behavioromics
toxicopharmacogenomics
… legacy of Human Genome Project
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-omics data
biochemical disease pathway data
biomedical image data
electronic health record data
hospital management data
hospital insurance data
public health data
Chinese chicken data
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a vast new
problem of
communication
national
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ontological
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medical researchers, clinical
practitioners, first responders, customs
agencies, pharmaceutical companies,
disease control centers need to
communicate in ways which involve
huge amounts of data
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Problem
how to reason with data from different
sources each of which uses its own
system of classification
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Solution:
Ontology !
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Ontology (phil.)
The branch of metaphysics that deals with
the nature of being.
Ontologies (tech.)
Standardized classification systems which
enable data from different sources to be
combined
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The need
strong general purpose classification
hierarchies created by domain specialists
clear, rigorous definitions
thoroughly tested in real use cases
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The actuality (too often)
myriad special purpose ‘light’ ontologies,
prepared by ontology engineers and
deposited in internet ‘repositories’ or
‘registries’
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ontologies
for ‘agent’
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often do not generalize …
repeat work already done by others
are not interoperable
reproduce the very problems of
communication which ontology was
designed to solve
contain incoherent definitions
and incoherent documentation
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A tragic example
“Health Level 7 Reference Information
Model” (HL7 RIM)
– a standard for exchange of information
between clinical information systems
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The ultimate special purpose
ontology
A healthcare messaging system used as
the basis for an entire clinical record
architecture, extending as far as core
genomic data
Rather like using air-traffic control
messaging as starting point for a science
of airplane thermodynamics
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National Cancer Institute National
Biospecimen Network (NBN)
“The NBN bioinformatics system should be
standards-based (e.g., SNOMED, HL7, or
MIAME for data; Internet for
communications) to enable data and
information exchange among system
components and the researchers who use
them.”
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HL7 Glossary
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Animal
Definition: A subtype of Living Subject
representing any animal-of-interest to
the Personnel Management domain.
LivingSubject
Definition: A subtype of Entity representing
an organism or complex animal, alive or
not.
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HL7 Glossary
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Person
Definition: A Living Subject representing
single human being [sic] who is uniquely
identifiable through one or more legal
documents
– impossible to refer to undocumented persons
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national
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HL7’s backbone ‘Act’ class
Act
Definition: An Act is the record of an Act
An X is the Y of an X
“There is no difference between an activity
and its documentation”
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HL7 Incredibly
Successful
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adopted by Oracle as basis for its
Electronic Health Record technology;
supported by IBM, GE, Sun ...
embraced as US federal standard
central part of $18 billion program to
integrate all UK hospital information
systems
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What’s gone wrong?
People of good will are making mistakes
because of lack of expertise
Money is wasted on megasystems that
cannot be used
Even large ontologies are built in the
spirit of the amateur hobbyist
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GlaxoSmithKline *
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What we need is “industrial-strength” ontologies
with a consistent and rich representation
formalism that are amenable for use as an
integration framework, and support reasoning
capabilities. We anticipate that pharma’s need to
bring together mountains of data and information
and to properly analyse that information all
depend on having a stable, well-developed
semantic framework that links information/data
and that allows reasoning systems to perform
some of our more "mundane" analysis work.
*Robin McEntire
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Signs of hope
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founding of National Center for Biomedical
Ontology (an NIH Roadmap Center)
increased recognition of FMA
Open Biomedical Ontologies consortium
introduction of rigorous logical tools and
scientific methods in the creation of
content-rich ontologies for automatic
reasoning and seamless integration
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Why NCOR?
national
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NCOR will
advance ontology as a discipline
employing rigorous scientific methods
develop objective, empirical measures of
quality for ontologies in ways which will
lead to the establishment of best practices
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Why NCOR?
national
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ontological
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NCOR will
provide coordination and support for
investigators working on theoretical
ontology and its applications
engage in outreach endeavors designed to
foster the goals of high quality ontology in
both theory and practice
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ontologies are ambitious
classification systems
they rely on definitions,
on the logic of relations,
and on theories of high-level
categories such as function,
process, thing, event, constituent
if you want to build a good ontology …
WORK WITH A PHILOSOPHER
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