Transcript 10:30-12:00 How to Build an Ontology 1-2pm Best Practices and Lessons Learned 2-3pm BIRN Ontologies: An Overview.

10:30-12:00 How to Build an
Ontology
1-2pm Best Practices and Lessons
Learned
2-3pm BIRN Ontologies: An
Overview
How to Build an Ontology
http://ontology.buffalo.edu/smith
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General trend on the part of NIH, FDA and
other bodies to consolidate ontologybased standards for the communication
and processing of biomedical data.
NCIT / caBIG / NECTAR / BIRN / OBO ...
High quality shared ontologies
build communities
http://ontology.buffalo.edu/smith
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TWO STRATEGIES:
Ad hoc creation of new database
schemas for each research group /
research hypothesis
vs.
Pre-established interoperable
stable reference ontologies in
terms of which all database
schemas need to be defined
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How to create the conditions for a
step-by-step evolution towards gold
standard reference ontologies in the
biomedical domain
... and why we need to create
these conditions
OBO Core project
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Ontology =def
A representation of the types of entities
existing in a given domain of reality, and
of the relations between these types
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Types have instances
Ontologies are like science texts: they are
about types
(Diaries, databases, clinical records are
about instances)
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The need
strong general-purpose classification
hierarchies created by domain specialists
clear, rigorous definitions
thoroughly tested in real cases
ontologies teach us about the instances in
reality by supporting cross-disciplinary
(cross-ontology) reasoning about types
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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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these light ontologies 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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BIRN Ontology Experiences
In the short-term, users will probably download the
data or analyses and extract the results using their
preferred methods.
In the long term, however, that will become infeasible
– the databases will have to be made interoperable with
standard datamining software.
This is where the neuroanatomy ontologies come in.
– We will need to know what the ROI is and which naming
scheme it came from (e.g., a Brodmann’s area, or a
sulcal/gyral area, etc.). We’ll need to know how it was
defined (Talairach atlas? MNI atlas? LONI atlas? Or
subject-specific regions?) and what the statistic is.
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BIRN Ontology Experiences
In the short-term, users will probably download the
data or analyses and extract the results using their
preferred methods.
In the long term that will become infeasible
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The long term begins here
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A methodology for qualityassurance of ontologies
tested thus far in the biomedical domain on:
– FMA
– GO + other OBO Ontologies
– FuGO
– SNOMED
– UMLS Semantic Network
– NCI Thesaurus
– ICF (International Classification of Functioning,
Disability and Health)
– ISO Terminology Standards
– HL7-RIM
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A methodology for qualityassurance of ontologies
accepted need for application of this methodology:
– FMA
– GO + other OBO Ontologies
– FuGO
– SNOMED
– UMLS Semantic Network
– NCI Thesaurus
– ICF (International Classification of Functioning,
Disability and Health)
– ISO Terminology Standards
– HL7-RIM
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A methodology for qualityassurance of ontologies
signs of hope:
– FMA
– GO + other OBO Ontologies
– FuGO
– SNOMED
– UMLS Semantic Network
– NCI Thesaurus
– ICF (International Classification of
Functioning, Disability and Health)
– ISO Terminology Standards
– HL7-RIM
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We know that high-quality
ontologies built according to this
methodology can help in creating
high-quality mappings between
human and model organism
phenotypes
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“Alignment of Multiple Ontologies of
Anatomy: Deriving Indirect Mappings from
Direct Mappings to a Reference Ontology”
Songmao Zhang
Olivier Bodenreider
AMIA 2005
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We also know that OWL is not enough
to ensure high-quality ontologies
and that the use of a common syntax and
logical machinery and the careful
separating out of ontologies into
namespaces does not solve the problem
of ontology integration
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A basic distinction
type vs. instance
science text vs. clinical document
man vs. Musen
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Instances are not represented in
an ontology
It is the generalizations that are important
(but instances must still be taken into
account)
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A
B
C
515287
521683
521682
DC3300 Dust Collector Fan
Gilmer Belt
Motor Drive Belt
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Ontology
Types Instances
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Ontology = A Representation of Types
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Each node of an ontology
consists of:
• preferred term (aka term)
• term identifier (TUI, aka CUI)
• synonyms
• definition, glosses, comments
Ontology = A Representation of Types
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Nodes in an ontology are
connected by relations:
primarily: is_a (= is subtype of)
and part_of
designed to support search,
reasoning and annotation
Ontology = A Representation of Types
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types
substance
organism
animal
mammal
cat
leaf class
siamese
frog
instances
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Rules for formating terms
• Terms should be in the singular
• Terms should be lower case
• Avoid abbreviations even when it is clear in
context what they mean (‘breast’ for ‘breast
tumor’)
• Avoid acronyms
• Avoid mass terms (‘tissue’, ‘brain mapping’,
‘clinical research’ ...)
• Each term ‘A’ in an ontology is shorthand for a
term of the form ‘the type A’
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Motivation: to capture reality
Inferences and decisions we make are
based upon what we know of reality.
An ontology is a computable representation
of the underlying biological reality.
Designed to enable a computer to reason
over the data we derive from this reality in
(some of) the ways that we do.
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Concepts
Biomedical ontology integration will never be
achieved through integration of meanings
or concepts
The problem is precisely that different user
communities use different concepts
Concepts are in your head and will change
as your understanding changes
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Concepts
Ontologies represent types: not concepts,
meanings, ideas ...
Types exist, with their instances, in objective
reality
– including types of image, of imaging
process, of brain region, of clinical
procedure, etc.
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Rules on types
Don’t confuse types with words
Don’t confuse types with concepts
Don’t confuse types with ways of getting to
know types
Don’t confuse types with ways of talking
about types
Don’t confuses types with data about types
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Some other simple rules for high
quality ontologies
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Univocity
Terms should have the same meanings on
every occasion of use.
They should refer to the same kinds of
entities in reality
Basic ontological relations such as is_a and
part_of should be used in the same way
by all ontologies
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Positivity
Complements of types are not themselves types.
Hence terms such as
non-mammal
non-membrane
other metalworker in New Zealand
do not designate types in reality
There are also no conjunctive and disjunctive
types:
protoplasmic astrocyte and Schwann cell
Purkinje neuron or dendritic shaft
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Objectivity
Which types exist is not a function of our
knowledge.
Terms such as ‘unknown’ or ‘unclassified’ or
‘unlocalized’ do not designate types in
reality.
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Single Inheritance
No kind in a classificatory hierarchy
should have more than one is_a
parent on the immediate higher
level
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Multiple Inheritance
thing
blue thing
car
is_a1
is_a2
blue car
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is_a Overloading
serves as obstacle to integration with
neighboring ontologies
The success of ontology alignment demands
that ontological relations (is_a, part_of, ...)
have the same meanings in the different
ontologies to be aligned.
See “Relations in Biomedical Ontologies”,
Genome Biology May 2005.
 DISEASE MAPS
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General Rule
Formulate universal statements first
Move to A may be B in such and such a
context later
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Intelligibility of Definitions
The terms used in a definition should be
simpler (more intelligible) than the term to be
defined; otherwise the definition provides no
assistance
– to human understanding
– to machine processing
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Definitions should be intelligible to
both machines and humans
Machines can cope with the full formal
representation
Humans need clarity and modularity
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But
Some terms are primitive (cannot be defined)
AVOID CIRCULAR DEFINITIONS
Avoid definitions of the forms:
An A is an A which is B (person = person with
identity documents)
An A is the B of an A (heptolysis = the causes of
heptolysis)
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Case Study: The National Cancer
Institute Thesaurus (NCIT)
does not (yet) satisfy these and other simple
principles
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The NCIT reflects a recognition of
the need
for high quality shared ontologies and
terminologies the use of which by clinical
researchers in large communities can
ensure re-usability of data collected by
different research groups
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NCIT
“a biomedical vocabulary that provides
consistent, unambiguous codes and
definitions for concepts used in cancer
research”
“exhibits ontology-like properties in its
construction and use”.
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Goals
to make use of current terminology “best practices”
to relate relevant concepts to one another in a
formal structure, so that computers as well as
humans can use the Thesaurus for a variety of
purposes, including the support of automatic
reasoning;
to speed the introduction of new concepts and
new relationships in response to the emerging
needs of basic researchers, clinical trials,
information services and other users.
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Formal Definitions
of 37,261 nodes, 33,720 were stipulated to
be primitive in the DL sense
Thus only a small portion of the NCIT
ontology can be used for purposes of
automatic classification and error-checking
by using OWL.
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Verbal Definitions
About half the NCIT terms are assigned
verbal definitions
Unfortunately some are assigned more than
one
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Disease Progression
Definition1
Cancer that continues to grow or spread.
Definition2
Increase in the size of a tumor or spread of
cancer in the body.
Definition3
The worsening of a disease over time. This
concept is most often used for chronic and
incurable diseases where the stage of the
disease is an important determinant of therapy
and prognosis.
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To make matters worse Disease
Progression has as subclass:
Cancer Progression
Definition:
The worsening of a cancer over time. This
concept is most often used for incurable
cancers where the stage of the cancer is
an important determinant of therapy and
prognosis.
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Cancer
a process (of getting better or worse)
an object (which can grow and spread)
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Confuses definitions with
descriptions
Tuberculosis
Definition
A chronic, recurrent infection caused by the bacterium
Mycobacterium tuberculosis. Tuberculosis (TB) may affect almost
any tissue or organ of the body with the lungs being the most
common site of infection. The clinical stages of TB are primary or
initial infection, latent or dormant infection, and recrudescent or
adult-type TB. Ninety to 95% of primary TB infections may go
unrecognized. Histopathologically, tissue lesions consist of
granulomas which usually undergo central caseation necrosis. Local
symptoms of TB vary according to the part affected; acute
symptoms include hectic fever, sweats, and emaciation; serious
complications include granulomatous erosion of pulmonary bronchi
associated with hemoptysis. If untreated, progressive TB may be
associated with a high degree of mortality. This infection is
frequently observed in immunocompromised individuals with AIDS
or a history of illicit IV drug use.
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Confuses definitions with
descriptions
Tuberculosis
Definition
A chronic, recurrent infection caused by the bacterium
Mycobacterium tuberculosis. Tuberculosis (TB) may affect almost
any tissue or organ of the body with the lungs being the most
common site of infection. The clinical stages of TB are primary or
initial infection, latent or dormant infection, and recrudescent or
adult-type TB. Ninety to 95% of primary TB infections may go
unrecognized. Histopathologically, tissue lesions consist of
granulomas which usually undergo central caseation necrosis. Local
symptoms of TB vary according to the part affected; acute
symptoms include hectic fever, sweats, and emaciation; serious
complications include granulomatous erosion of pulmonary bronchi
associated with hemoptysis. If untreated, progressive TB may be
associated with a high degree of mortality. This infection is
frequently observed in immunocompromised individuals with AIDS
or a history of illicit IV drug use.
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A better definition
Tuberculosis
Definition:
A chronic, recurrent infection caused by the
bacterium Mycobacterium tuberculosis.
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NCIT inherits this ontological and terminological
incoherence from source vocabularies in UMLS
Conceptual Entities =def
An organizational header for concepts
representing mostly abstract entities.
Includes as subtypes:
action, change, color, death, event, fluid,
injection, temperature
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Conceptual Entities =def
An organizational header for concepts
representing mostly abstract entities.
Confuses use and mention (swimming is
healthy and has eight letters)
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Duratec, Lactobutyrin, Stilbene
Aldehyde
are classified by the NCIT as Unclassified
Drugs and Chemicals
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and problematic synonyms
Anatomic Structure, System, or Substance ~ Anatomic
Structures and Systems
Does ‘anatomic’ apply only to structure or also to system
and substance?
Biological Function ~ Biological Process
some biological processes are the exercises of biological
functions
others (e.g. pathological processes, side effects) not
Genetic Abnormality ~ Molecular Abnormality (with
subtype: Molecular Genetic Abnormality) (definitions
not supplied)
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Problematic synonyms
Diseases and Disorders ~ Disease ~ Disorder
Definition1 for Disease:
A disease is any abnormal condition of the body or mind
that causes discomfort, dysfunction, or distress to the
person affected or those in contact with the person. ...
Definition2 for Disease
A definite pathologic process with a characteristic set of
signs and symptoms. ...
Condition  Process
Definition2 contradicts NCIT’s own classification hierarchy
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Three disjoint classes of plants
Vascular Plant
Non-vascular Plant
Other Plant
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Three kinds of cells
Abnormal Cell is a top-level class (thus not
subsumed by Cell
Normal Cell is a subclass of Microanatomy.
Cell is a subclass of Other Anatomic Concept
(so that cells themselves are concepts)
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NCIT as now constituted will block
automatic reasoning
Neither Normal Cells nor Abnormal Cells are
Cells within the context of the NCIT
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Some consolations
NCIT is open source
NCIT has broad coverage
NCIT has some formal structure (OWL-DL)
NCIT is much, much better than (for
example) the HL7-RIM
NCIT has realized the errors of its ways
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The road ahead
http://www.cbdnet.com/index.php/search/show/938464
= “Review of NCI Thesaurus and
Development of Plan to Achieve OBO
Compliance”
and welcome to the Pre-NCIT:
http://nciterms.nci.nih.gov/NCIBrowser/Dic
tionary.do
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Fragment of Pre-NCIT Hierarchy
Murine Tissue Type
Body Fluids and Substances (MMHCC)
Cardiovascular System (MMHCC)
Blood Vessel (MMHCC)
Heart (MMHCC)
Digestive System (MMHCC)
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First step
Alignment of OBO ontologies through a
common system of formally defined
relations in the OBO-RO (OBO Relation
Ontology)
see “Relations in Biomedical Ontologies”,
Genome Biology Apr. 2005
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is_a (sensu UMLS)
A is_a B =def
‘A’ is narrower in meaning than ‘B’
grows out of the heritage of dictionaries
(which ignore the basic distinction between
types and instances)
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To build a high quality shared
ontology requires hard work and
staying power
You cannot cheat by borrowing
from UMLS
UMLS (= the UMLS
Metathesaurus) is not an ontology
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Concepts, Concept Names, and
their Identifiers in the UMLS
The Metathesaurus is organized by concept. One of its
primary purposes is to connect different names for the
same concept from many different vocabularies.
A concept is a meaning. A meaning can have many
different names. A key goal of Metathesaurus
construction is to understand the intended meaning of
each name in each source vocabulary and to link all the
names from all of the source vocabularies that mean the
same thing (the synonyms). This is not an exact science.
... Metathesaurus editors decide what view of synonymy
to represent in the Metathesaurus concept structure.
Please note that each source vocabulary’s view of
synonymy is also present in the Metathesaurus,
irrespective of whether it agrees or disagrees with the
Metathesaurus view.
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This strange mapping
between names as they appear in different
source vocabularies created for widely
different purposes can still be very useful
but the source vocabularies themselves are
of variable quality
(not all mappings are created equal)
and the sorts of search which the UMLS
supports reflects an already outmoded
technology
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is_a
congenital absent nipple is_a nipple
surgical procedure not carried out because of
patient’s decision is_a surgical procedure
cancer documentation is_a cancer
disease prevention is_a disease
living subject is_a information object representing
an animal or complex organism
individual allele is_a act of observation
limb is_a tissue
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is_a (sensu UMLS)
both testes is_a testis
plant leaves is_a plant
smoking is_a individual behavior
walking is_a social behavior
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is_a
A is_a B =def
For all x, if x instance_of A then x
instance_of B
cell division is_a biological process
adult is_a child ???
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Two kinds of entities
occurrents (processes, events, happenings)
cell division, ovulation, death
continuants (objects, qualities, ...)
cell, ovum, organism, temperature of
organism, ...
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is_a (for occurrents)
A is_a B =def
For all x, if x instance_of A then x
instance_of B
cell division is_a biological process
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is_a (for continuants)
A is_a B =def
For all x, t if x instance_of A at t then x
instance_of B at t
abnormal cell is_a cell
adult human is_a human
but not: adult is_a child
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part_of
Composes, with one or more other physical units,
some larger whole.
(UMLS Semantic Network)
what does this relation relate?
A is_a B =def A is narrower in meaning than B
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Part_of as a relation between
types is more problematic than
is standardly supposed
heart part_of human being ?
human heart part_of human being ?
human being has_part human testis ?
testis part_of human being ?
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Definition of part_of as a relation
between types
A part_of B =Def all instances of A are
instance-level parts of some instance of B
human testis part_of adult human being
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two kinds of parthood
1. between instances:
Mary’s heart part_of Mary
this nucleus part_of this cell
2. between types
human heart part_of human
cell nucleus part_of cell
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part_of (for occurrents)
A part_of B =def.
For all x, if x instance_of A then there is
some y, y instance_of B and x part_of y
where ‘part_of’ is the instance-level part
relation
EVERY A IS PART OF SOME B
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part_of (for continuants)
A part_of B =def.
For all x, t if x instance_of A at t then there
is some y, y instance_of B at t and x
part_of y
where ‘part_of’ is the instance-level part
relation
NOTE THE ALL-SOME STRUCTURE
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A part_of B, B part_of C ...
The all-some structure of such definitions
allows
cascading of inferences
(i) within ontologies
(ii) between ontologies
(iii) between ontologies and EHR
repositories of instance-data
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Cascading inferences
Whichever A you choose, the instance of B
of which it is a part will be included in
some C, which will include as part also the
A with which you began
The same principle applies to the other
relations in the OBO-RO:
located_at, transformation_of, derived_from,
adjacent_to, etc.
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is_a and part_of never cross
categorial divides
(cf. tripartite organization of GO)
if A is_a B
then A is an object type iff B is an object
type
then A is a process type iff B is a process
type
then A is a characteristic type iff B is a
characteristic type
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Kinds of relations
Between types:
– is_a, part_of, ...
Between an instance and a type
– this explosion instance_of the type explosion
Between instances:
– Mary’s heart part_of Mary
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Continuity
instance a continuous_with instance b
is always symmetric
But consider the types lymph node and
lymphatic vessel:
Each lymph node is continuous with some
lymphatic vessel, but there are lymphatic
vessels (e.g. lymphs and lymphatic trunks)
which are not continuous with any lymph
nodes
Continuity on the type level is not symmetric.
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Adjacency as a relation
between universals is not
symmetric
Consider
seminal vesicle adjacent_to urinary
bladder
Not: urinary bladder adjacent_to
seminal vesicle
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Instance level
this nucleus is adjacent to this cytoplasm
implies:
this cytoplasm is adjacent to this nucleus
Type level
nucleus adjacent_to cytoplasm
Not: cytoplasm adjacent_to nucleus
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Applications
Expectations of symmetry e.g. for proteinprotein interactions hmay hold only at the
instance level
if A interacts with B, it does not follow that B
interacts with A
if A is expressed simultaneously with B, it
does not follow that B is expressed
simultaneously with A
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Definitions of the all-some form
allow cascading inferences
If A R1 B and B R2 C, then we know that
every A stands in R1 to some B, but we
know also that, whichever B this is, it can
be plugged into the R2 relation
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GALEN: Vomitus contains carrot
All portions of vomit contain all portions of
carrot
All portions of vomit contain some portion of
carrot
Some portions of vomit contain some portion
of carrot
Some portions of vomit contain all portions
of carrot
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transformation_of
same instance
C
c at t
pre-RNA
child
http://ontology.buffalo.edu/smith
C1
c at t1
time
mature RNA
adult
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transformation_of
A transformation_of B =Def.
Every instance of A was at some earlier time an
instance of B
adult transformation_of child
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embryological development
C
c at t
http://ontology.buffalo.edu/smith
C1
c at t1
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tumor development
C
c at t
http://ontology.buffalo.edu/smith
C1
c at t1
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derives_from
C
C1
c at t
c1 at t1
time
C'
c' at t
instances
ovum
zygote derives_from
sperm
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Request from Bill Bug
How best to effectively bring together:
- spatial/morphological ontologies;
- neuroscience terminologies (e.g.,
NeuroNames) and;
- data-centric neuroanatomical indexing
systems (voxel-based 3D atlases);
to promote optimal integration of
neuroscience data sets that include a
spatial component, however coarse.
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A suite of defined relations between
universals
Foundational is_a
part_of
Spatial
Temporal
Participation
located_in
contained_in
adjacent_to
transformation_of
derives_from
preceded_by
has_participant
has_agent
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Logical Theory of Spatial Relations
RCC: Region-Connection Calculus
(Leeds Qualitative Spatial Reasoning
Group)
Cf. Dameron et al. Modeling
dependencies between relations to
ensure consistency of a cerebral
cortex anatomy knowledge base
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Principles
1 anatomical structure  1 region
has_location
Define the relationships of adjacency,
connectedness etc. using RCC-8 and its
extensions
PO
DC
EC
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TPP
NTPP
EQ
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Example 1
Reasoning with part and location at the
instance level:
Frontal Lobe
Inferior Frontal Gyrus
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Operc. Pars of
Inferior Frontal Gyrus
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Example 2
Reasoning with location, continuity and
external connection
Frontal Lobe
PreCentral Gyrus
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PostCentral Gyrus
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Extension to the 3-D case
B
x
y
time
SNAP-ti.
SPAN
substances x, y participate in process B
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slice of
x’s life
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Most ontologies are execrable
But some good ontologies do already
exist
• as far as possible don’t reinvent
• use the power of combination and collaboration
• ontologies are like telephones: they are valuable
only to the degree that they are used and
networked with other ontologies
• but choose working telephones
• most UMLS telephones were broken from the
start
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Why do we need rules/standards
for good ontology?
Ontologies must be intelligible both to humans (for
annotation) and to machines (for reasoning and
error-checking): unintuitive rules for
classification lead to errors
Intuitive rule facilitate training of curators and
annotators
Common rules allow alignment with other
ontologies
Logically coherent rules enhance harvesting of
content through automatic reasoning systems
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To the degree that basic rules
of good ontology are not
satisfied, error checking and
ontology alignment will be
achievable, at best, only
– with human intervention
– via force majeure
– with unstable results
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Current practice in the domain of
clinical research
Results of clinical trials are organized too
tightly around specific diagnostic criteria
imposed by specific, local, hypotheses
A change in criteria forces a costly reexamination and re-coding of all existing
records to make them usable in future
hypothesis generation and testing.
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How to solve this problem?
Just as clinical hypotheses need to be tied
to basic science, so special-purpose
application ontologies need to be tied to
general-purpose reference ontologies
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How to solve this problem?
We separate
data as interpreted in terms of current criteria
from
the structure of the underlying biomedical reality
and ensure that the first is stored and processed
always by using terms drawn from a shared,
stable representation (a reference ontology) of the
latter.
Diagnostic criteria for a disease can then be
changed but we will still maintain access to the
data relevant to all prior diagnosed cases of the
disease in question.
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Not only data needs to be aligned
through pre-established reference
ontologies, so also does software
Currently, application ontologies are built afresh
for each new application
They commonly introduce new idiosyncrasies of
terminology, format or logic, plus simplifications
or distortions of their subject-matters.
This may do no harm in relation to the specific
application (for example radiology, tissue
classification, cancer staging) – and keeps the
software simple
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But what happens
when other applications want to use the data
annotated in their terms, or when we need to
extend to a larger portion of biomedical reality?
Now the expanded ontology will no longer be
compatible with the software designed for its
original application.
Different groups now need to start working with
different and incompatible versions of an
ontology, engendering a spiralling complexity as
these different versions themselves become
revised and extended for different purposes.
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The solution
The methodology of always developing
application ontologies against the
backgrund of formally robust reference
ontologies can both counteract these
tendencies toward ontology proliferation
and ensure the interoperability of
application ontologies as they become
further developed in the future.
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The methodology of reference
ontologies
can provide locally developed application
ontologies with cross-granular
understanding of the ways processes at
the gene and protein level are linked to
clinically salient processes at coarser
granularity
and it can allow them take advantage of
existing logical tools and methods for
reasoning across large bodies of data.
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An application ontology
is comparable to an engineering artifact
such as a software tool. It is constructed
for a specific practical purpose.
Examples:
NCIT
FuGO Functional Genomics Investigation
Ontology
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A reference ontology
A reference ontology has a unified subject-matter,
which consists of entities existing independently
of the ontology, and it seeks to optimize
descriptive or representational adequacy to this
subject matter.
A reference ontology is analogous to a scientific
theory. Thus it consists of representations of
biological reality which are correct when viewed
in light of our current understanding of reality,
and it must be subjected to updating in light of
scientific advance.
Example: The Foundational Model of Anatomy
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Current Best Practice
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Anatomical
Structure
Anatomical Space
Organ Cavity
Subdivision
Organ
Cavity
Organ
Serous Sac
Cavity
Subdivision
Serous Sac
Cavity
Serous Sac
Organ
Component
Organ
Subdivision
Pleural Sac
Pleural
Cavity
Parietal
Pleura
Interlobar
recess
Organ Part
Mediastinal
Pleura
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Tissue
Pleura(Wall
of Sac)
Visceral
Pleura
Mesothelium
of Pleura
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The Foundational Model of Anatomy
Follows formal rules for ‘Aristotelian’ definitions
When A is_a B, the definition of ‘A’ takes the form:
an A =def. a B which ...
a human being =def. an animal which is rational
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FMA Example
Cell =def. an anatomical structure which
consists of cytoplasm surrounded by a
plasma membrane with or without a cell
nucleus
Plasma membrane =def. a cell part that
surrounds the cytoplasm
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The FMA regimentation
Brings the advantage that each definition
reflects the position in the hierarchy to which
a defined term belongs.
The position of a term within the hierarchy
enriches its own definition by incorporating
automatically the definitions of all the terms
above it.
The entire information content of the FMA’s
term hierarchy can be translated very cleanly
into a computer representation
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GO now adopting structured definitions
which contain both genus and differentiae
Species =def Genus + Differentiae
neuron cell differentiation =def
differentiation by which a cell acquires features of a neuron
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Ontology alignment
One of the current goals of GO is to align:
Cell Types in GO
with
cone cell fate commitment
Cell Types in the Cell Ontology
retinal_cone_cell
keratinocyte differentiation
keratinocyte
adipocyte differentiation
fat_cell
dendritic cell activation
dendritic_cell
lymphocyte proliferation
lymphocyte
T-cell homeostasis
T_lymphocyte
garland cell differentiation
garland_cell
heterocyst cell differentiation
heterocyst
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Alignment of the two ontologies will permit the
generation of consistent and complete definitions
GO
id: CL:0000062
name: osteoblast
def: "A bone-forming cell which secretes an extracellular matrix.
Hydroxyapatite crystals are then deposited into the matrix to form
bone." [MESH:A.11.329.629]
is_a: CL:0000055
relationship: develops_from CL:0000008
relationship: develops_from CL:0000375
+
Cell type
=
Osteoblast differentiation: Processes whereby an
osteoprogenitor cell or a cranial neural crest cell
acquires the specialized features of an osteoblast, a
bone-forming cell which secretes extracellular matrix.
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New Definition
126
Other Ontologies to be aligned
with GO
Chemical ontologies
– 3,4-dihydroxy-2-butanone-4-phosphate synthase
activity
Anatomy ontologies
– metanephros development
GO itself
– mitochondrial inner membrane peptidase activity
 OBO core
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eventually to comprehend all of
OBO
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is_a
Anatomical
Structure
Anatomical Space
Organ Cavity
Subdivision
Organ
Cavity
Organ
Serous Sac
Cavity
Subdivision
Serous Sac
Cavity
Serous Sac
Organ
Component
Organ
Subdivision
Pleural Sac
Pleural
Cavity
Parietal
Pleura
Interlobar
recess
Organ Part
Mediastinal
Pleura
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Tissue
Pleura(Wall
of Sac)
Visceral
Pleura
Mesothelium
of Pleura
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Anatomical Entity
Physical
Anatomical Entity
Conceptual
Anatomical Entity
-is a-
Anatomical
Relationship
Material Physical
Anatomical Entity
Body
Substance
Biological
Macromolecule
Non-material Physical
Anatomical Entity
Anatomical
Space
Anatomical
Structure
Cell
Cell
Organ
Tissue
Part
Part
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Organ
Organ
System
Body
Part
Human
Body
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The Anatomy Reference Ontology
is organized in a graph-theoretical structure
involving two sorts of links or edges:
is-a (= is a subtype of )
(pleural sac is-a serous sac)
part-of
(cervical vertebra part-of vertebral column)
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at every level of granularity
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What do the kidneys do?
Modularity
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NEPHRONHow
does a kidney work?
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FUNCTIONAL
SEGMENTS
Nephron Functions
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Top-Level Categories in the FMA
anatomical
entity
physical
anatomical entity
material physical
anatomical entity
anatomical
structure
body
substance
non-physical
anatomical entity
non-material physical
anatomical entity
body
space
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boundary
anatomical
attribute
anatomical
relationship
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anatomical structure (cell, lung, nerve,
tooth)
result from the coordinated expression of
structural genes
have their own 3-D shape
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portion of body substance
inherits its shape from container
portion of urine
portion of menstrual fluid
portion of blood
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anatomical space
cavities, conduits
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anatomical attribute
mass
weight
temperature
your temperature
its value now
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anatomical relationship
located_in
contained_in
adjacent_to
connected_to
surrounds
lateral_to (West_of)
anterior_to
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boundary
bona fide / fiat
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www.enel.ucalgary.ca/ People/Mintchev/stomach.htm
Connectedness and
Continuity
The body is a highly connected entity.
Exceptions: cells floating free in blood
continuous_with,
attached_to (muscle to bone)
synapsed_with (nerve to nerve and nerve to
muscle)
Two continuants are continuous on the instance
level if and only if they share a fiat boundary.
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Anatomical
Structure
Anatomical Space
Organ Cavity
Subdivision
Organ
Cavity
Organ
Serous Sac
Cavity
Subdivision
Serous Sac
Cavity
Serous Sac
Organ Part
Organ
Component
Organ
Subdivision
basis for generalization
to other species
Pleural Sac
Pleural
Cavity
Parietal
Pleura
Interlobar
recess
Mediastinal
Pleura
http://ontology.buffalo.edu/smith
Tissue
Pleura(Wall
of Sac)
Visceral
Pleura
Mesothelium
of Pleura
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Anatomical
Structure
Anatomical Space
Organ Cavity
Subdivision
Organ
Cavity
Organ
Serous Sac
Cavity
Subdivision
Serous Sac
Cavity
Serous Sac
Organ
Component
Organ
Subdivision
Pleural Sac
Pleural
Cavity
Parietal
Pleura
Interlobar
recess
Organ Part
Mediastinal
Pleura
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Tissue
Pleura(Wall
of Sac)
Visceral
Pleura
Mesothelium
of Pleura
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Web-Based Representations of Neuroanatomy
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includes Neuronames
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with thanks to Christine
Fennema-Notestine and
Jessica Turner
Human Morphometry and
Function BIRN Testbeds
CBiO/BIRN Workshop 2006
BIRN Ontology Needs
GOAL: User will employ BIRN interface and Mediator
to perform scientific queries on data from
•
•
•
•
structural and functional MRI experiments
clinical assessments
psychiatric interviews
and/or behavioral experiments
BIRN needs for common vocabularies
– Mediator needs to talk across databases to find
relevant/similar information; this requires linking of
concepts to table columns and values
– Query interface needs semantic network to find related
information
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Example queries:
– Find all datasets of schizophrenics with
structural and functional imaging data related
to working memory
– Find the correlation between hippocampal
volume and working memory performance in
AD subjects
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MBIRN priorities
“To relate clinical assessments, cognitive
function, and neuroanatomy within mBIRN’s
multi-site AD sample, with future branching into
neuropsychiatric measures”
– Only a high quality reference ontology of
neuro(patho)anatomy from the macroscopic to
the subcellular levels of granularity can give you
this
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Existing neuroanatomical ontology
Brain
…
Cerebellum
Cerebrum
Cerebral white matter
Frontal cortex
…
…
Need to create
related “function”based ontology
Cerebral cortex
CVLT
Temporal cortex
Memory
Superior temporal
…
…
Mesial temporal
Amygdala
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Hippocampus
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‘Need to create related “function”based ontology’
UMLS: mental process is_a organism function
Function vs. functioning
Many entities have functions which they never
realise
A has function B = A can realise B (under
which circumstances?)
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‘Need to create related “function”based ontology’
A function is a disposition of an independent
continuant to engage in corresponding
processes.
To what extent are the various functions identified
by BIRN are in fact complex processes with
many less complex processes as their parts.
How are functions different from disfunctions /
malfunctions ?
Are all function such that their execution is good
for the organism?
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‘Need to create related “function”based ontology’
“You cannot classify parts of the brain on the basis
of which parts can think, remember, effect
movement or perceive various kinds of
sensations, just as you cannot sort anatomical
entities on the basis of which can pump, digest,
secrete, fertilize or stabilize.”
“It is impossible to create an inheritance class
subsumption hierarchy of neuroanatomical
entities at any meaningful depth on the basis of
function.”
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Assessment
Brain
Neuropsychology
Cerebrum
Amnesia
Cognition
Cerebral cortex
Frontal
Temporal
Memory
Learning
Cognitive
impairment
Mesial temporal
CVLT
Hippocampus
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Task and score description158
Can we reason on the basis of a graph of this sort?
Behavioral
Paradigm
Assessment
SCID-Patient
CVLT
SIRP
Working memory
Attention
Breathhold
Long Term memory
Memory
Cognitive
Process
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Action
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Bonfire Relations
relation:
the type of relation between the concept to the left and the concept to the ri
PAR = Parent
CHD = Child
SIB = Sibling
RB = Broader Relationship
RN = Narrower Relationship
RO = Other Relationship
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BIRN Relations
UMLS (PAR, CHD, RN, RO, RB, SY).
RB: has a broader relationship
RN: has a narrower relationship
RO: has relationship other than
synonymous, narrower, or broader
CHD: has child relationship in a
Metathesaurus
SIB: has sibling relationship in a
Metathesaurus source vocabulary
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“Circular Hierarchical Relationships in the UMLS:
Etiology, Diagnosis, Treatment, Complications and Prevention”
Olivier Bodenreider
Topographic regions: General terms
Physical anatomical entity
Anatomical spatial entity
Anatomical surface
Body regions
Topographic regions
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MeSH
MeSH Descriptors
Index Medicus Descriptor
Anthropology, Education, Sociology and
Social Phenomena (MeSH Category)
Social Sciences
Political Systems
National Socialism
National Socialism is_a Political Systems
National Socialism is_a Anthropology ...
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MeSH
National Socialism is_a MeSH Descriptor
Cf. NeuroNames:
Ontology =def a codification of the
relationships between words and concepts
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Human BIRN data includes:
Participant demographics such as age, gender, …
Clinical and psychiatric information
– Assessments used, data type
– Diagnostic information
Behavioral data during fMRI tasks
– Need to know how to interpret that (“is a button 1 response
a yes or a no?”)
Raw structural and functional images
– Need information about data collection and preprocessing
methods
Single-subject and group level analyses and results
– Need information about analytic methods used
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Areas where application ontologies will be needed
Participant demographics such as age, gender, …
Clinical and psychiatric information
– Assessments used, data type
– Diagnostic information
Behavioral data during fMRI tasks
– Need to know how to interpret that (“is a button 1 response
a yes or a no?”)
Raw structural and functional images
– Need information about data collection and preprocessing
methods
Single-subject and group level analyses and results
– Need information about analytic methods used
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Bottom-up search:
User’s dataset contains the CVLT – what does it measure?
• Search for CVLT
• Related to PARENT concepts like “Neuropsychological
tests” or “Assessment Scales” or SIBLING concepts of
other tests
• What is the CVLT? This doesn’t answer the user’s
question.
• Need relationship links to function: memory and
learning
• Need relationship links to structure: anatomical
regions reflected in change of performance on this
measure  hippocampus
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Top-down search:
User interested in studying the relationship
between hippocampal volume and memory
performance in Alzheimer’s disease.
• Search for measures of memory
• Would like to see memory linked to CVLT
• Would like to see memory linked to hippocampus at a
very basic level
• Would like to see links to potential disorders assessed,
e.g., amnesia or AD
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Ontology/Terminology Infrastructure
GOAL: to allow database mediation and
scientific queries for multi-site clinical
neuroimaging studies. This requires the
relationship of database tables to
concepts and to relate brain structure
and function through neuroanatomical
regions, neuropsychological and
cognitive terms, and clinical
assessments.
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Ontology/Terminology Infrastructure
– To do this, the Mediator relies in part on
defined terms/concepts to define relationships
between similar terms from different
databases.
– If a user is interested in data related to “long
delay free recall," it is important to also
include information related to “memory." This
type of relational knowledge is critical to find
other values in other databases that have
similar information.
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Ontology/Terminology Infrastructure
In addition, the ontology will provide a semantic
network; for a user searching for “memory"
information, related information would include
– Cognitive terms, e.g., recall, recognition, short and
long term memory
– Assessment terms, e.g., California Verbal Learning
Test
– “Disorders of” terms, e.g., Alzheimer’s disease is a
disorder of memory
How block information overload?
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Bottom-up search:
User’s resultant dataset contains the MMSE – the user
asks what does it measure?
• Search for MMSE concept
• Related to PARENT concepts like Neuropsychological tests”
or “Assessment Scales” or SIBLING concepts of other tests
• What is the MMSE? This doesn’t answer the user’s question.
• Need relationship links to function: general cognitive ability,
cognitive impairment, dementia severity, brain damage …
• Need relationship links to structure: anatomical regions
reflected in change of performance on this measure, although
a relatively non-specific measure
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Top-down search:
What variables exist that would provide a measure
of general cognitive function and dementia
severity?
• Search for measures of (general) cognitive function
• Would like to see general cognitive ability, cognitive
impairment, dementia severity linked to MMSE
• Would like to see general cognitive ability, cognitive
impairment, dementia severity linked to
neuroanatomical regions, simply brain in this case
• Would like to see links to potential disorders
measured, e.g., AD
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NeuroNames (with thanks to Onard Mejino)
has a limited scope.
It deals with neuroanatomical structures only at the
gross level. No cellular, subcellular or
macromolecular entities are represented.
The peripheral nervous system and the spinal cord
are not included.
It represents structures from different species
(human, macaque and rodent) in the same
hierarchy.
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NN’s main hierarchy
•
•
•
•
is a partonomy based on mutually exclusive and exhaustive
volumetric partitions, the equivalent of regional partition in the
FMA.
The partonomy supports only ONE partition view and therefore
does not accommodate
other recognized regional partitions like Brodman areas (treated
as “ancillary structures”)
constitutional parts like the internal pyramidal layer of neocortex
and the vasculature of neuraxis (entities that have important
clinical significance)
new partitions advanced by new technology like gene
expression mappings or radiologic imaging techniques
partitions determined by formal spatial region-based ontologies
like RCC
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The Neuronames partonomy
will serve at best as an application ontology for
annotating segmented images of the brain.
But it will still be very difficult to link the
annotated image data to all the other types of
data which will BIRN will need to describe
 a reference ontology of neuroanatomy is a first
priority.
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Neuronames
• Since univocity is not enforced in the
literature of neuroanatomy, e.g. the term
‘Basal ganglia’ represents different
structures when used in association with
anatomic, functional and clinical views.
• How will NN resolve or clarify this?
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Neuronames
• entities are primarily identified on the basis of stains that
distinguish gray matter from white matter
• thus not on principles or rules that define the type of the
entity in question, thereby yielding a partition not in
accord with the standards commonly accepted for
representing the rest of the body.
• gray matter and white matter are viewed as tissues. But
tissue is usually defined as an aggregate of similarly
specialized cells and intercellular matrix.
• yet gray matter consists not of cells but of cell bodies,
white matter not of cells but of neurites
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Neuronames
• gives no explicit definitions, and the representations it
gives (e.g. of the Fourth Ventricle*) are often at odds with
consensual usage
• hence scalability, extendability, combinability with other
ontologies is limited – how then can it be used to bridge
research efforts at the genomic / proteomic level with
those at the clinical level?
• Information unique to neuroanatomical entities such as
axonal input/output relationships, connectivity, neuron
type, neurotransmitter and receptor types are
indispensable in establishing and understanding both
structural and physiological relationships among
neuroanatomical entities and their relationship with the
rest of the body.
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BIRNLex
does provide definitions, normally taken
over from UMLS
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Rules for definitions
‘A’ = child term
‘B’ = parent term
an A =def a B which Cs
If a definition is correct it should always make
sense to substitute ‘a B which Cs’ for ‘an A’
“A human being is subject to processes of aging”
“A rational animal is subject to processes of aging”
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BIRNLex
The eye =def.
The eyeball and its constituent parts, e.g. retina
mouse =def.
common name for the species mus musculus
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BIRNLex
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BIRNLex
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BIRNLex
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BIRNLex
bear in mind always that
your ontology needs
to be interoperable with
other ontologies
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BIRNLex
bear in mind always that
your ontology needs
to be interoperable with
other ontologies
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BIRNLex
surface =def 3D segmentation obtained by
fitting a polygonal mesh around the boundary
of an object of interest, creating a 3D surface
Concept =def Generic ideas or categories
derived from common properties of objects,
events, or qualities, usually represented by
words or symbols
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BIRNLex
brain imaging =def none; synonymous with
positrocephalogram, nos
CA1 =def CA1 cytoarchitectonic field of
hippocampus
cognitive process = def. conceptual function
or thinking in all its forms
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BIRNLex and UMLS-SN
Rest =SN Daily or Recreational Activity
Principal Investigator =SN Professional or
Occupational Group
Left handedness =SN Organism Attribute
Ambidextrous =SN Finding
Brain Imaging =SN Diagnostic Procedure
Brain Mapping =SN Diagnostic Procedure & Research
Activity
Healthy Adult =SN Finding
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BIRNLex
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Mouse BIRN: Ontologies
Mouse
BIRN:
Maryann Martone
and Ontologies
Maryann Martone and Bill Bug
Bill Bug
2005 All Hands Meeting
Use of Ontologies in BIRN
•Databases
•Enforces semantic consistency within a database
•Data Sharing
•Establishes semantic relationship among concepts contained in
distributed databases
•Data integration
•Bridging across multiscale and multimodal data
•Concept-based queries:
•Ontologies can be used to alter semantic context to present a view of the
conceptual aspects of a data set or meta-analysis result most relevant to a
particular neuroscientist
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Objectives of Working
Group
Educate BIRN participants on the use of ontologies and
associated tools for data integration
– Tuesday (PM) and Wednesday (AM)
Develop a set of ontology resources for BIRN
participants, based on existing ontologies where
possible
Identify areas that are not well covered by existing
ontologies for possible development.
***Develop a clear set of policies and procedures for
working with ontologies
– Including curation, addition of core ontologies, extension of
ontologies, mapping of databases to ontologies
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Goals of OTF
•Provide a dynamic knowledge infrastructure to support integration and
analysis of BIRN federated data sets, one which is conducive to
accepting novel data from researchers to include in this analysis.
•Identify and assess existing ontologies and terminologies for
summarizing, comparing, merging, and mining datasets. Relevant
subject domains include clinical assessments, demographics, cognitive
task descriptions, imaging parameters/data provenance in general, and
derived (fMRI) data.
•Identify the resources needed to achieve the ontological objectives of
individual test-beds and of the BIRN overall. May include finding other
funding sources, making connections with industry and other consortia
facing similar issues, and planning a strategy to acquire the necessary
resources.
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BIRN Ontology Resources
Bonfire Ontology
Browser and
Extension Tool
Mouse BIRN Ontology Resource Page
http://ontology.buffalo.edu/smith
http://nbirn.net/Resources/Users/Ontologies/
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Current Ontology Development
by Mouse BIRN Participants
Developmental Ontology
• Seth Ruffins, Cal Tech
Subcellular Anatomy
• Maryann Martone and Lisa Fong, UCSD
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Ontology for Subcellular Anatomy of Nervous System
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CCDB Dictionary
Term
Ontology
ConceptID
Semantic Type
Definition
Cerebellum
UMLS
C0007765
Body Part, Organ,
or Organ
Component
Part of the metencephalon that
lies in the posterior cranial fossa
behind the brain stem. It is
concerned with the coordination
of movement. (MSH)
Glial Fibrillary
Acidic Protein
UMLS
C0017626
Amino Acid, Peptide,
or Protein,
Biologically Active
Substance
An intermediate filament protein
found only in glial cells or cells of
glial origin. MW 51,000. (MSH)
Medium Spiny
Neuron
Bonfire
BID000012
Cell
Small (10-15 µm in diameter)
projection neurons found in
neostriatum, possessing a
rougly spherical dendritic tree
composed of 3-5 primary
dendrites. Dendrites are
covered with dendritic spines.
Purkinje cell
UMLS
C0034143
Cell
large branching neurons of the
middle layer of cerebellar cortex,
characterized by vast arrays of
dendrites; the output neurons of
the cerebellar cortex.
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Some Areas of Interest to
BIRN
Navigating through Multi-resolution
information
Linking animal and human imaging
data
brain
Entopeduncular
nucleus
Globus pallidus,
internal segment
Animal Model
Disease Process
cerebellum
cerebellar cortex
Purkinje cell
•***Map between Human and Animal models
dendritic spine
•Functional assessment
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Anatomical Knowledge Sources
Foundational model of anatomy
Neuronames (Brain Info)***
BAMS***
Adult Mouse Anatomical Dictionary
(Edinburgh/GO)
“Although BIRN is an open, diverse and fluid environment, the use of
ontologies for enhanced interoperability will be pointless if we allow
random use of ontologies. The OTF recommends that there be a set of
ontologies that are approved for use and a set of policies and procedures
for adding or creating additional knowledge sources. Current knowledge
sources that are currently in use include UMLS, GO, LOINC, SNOMED,
NEURONAMES.”
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Other Resources Likely of Use
Mouse Phenome Project:
a collection of phenotypic and genotypic
data for the laboratory mouse
anatomy
behavior
biological factors
blood
cancer
diet effects
drug effects, toxicity
genotype
heart, lung
intake, metabolism
musculoskeletal
neurosensory
reproduction
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Neuronames-UMLS-Smart Atlas
•Mapping of rodent nomenclature onto UMLS
•Neuronames has now included many of the terms
•Using concepts in Neuronames and Paxinos to create new
hierarchy
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What do we need to do in the
next year
Identify areas of mouse BIRN not covered
– Do ontologies exist?
– If not, do we develop them
What known ontologies should be added to BIRN
ontology resources
– Who will handle semantic concordance
– How do we represent these in BIRN?
Mapping databases to ontologies
– Time frame
– What should be mapped?
– Who will do this at each site
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Mouse BIRN Global Conceptual
Schema
Project
Experiments
Molecular
Distributions
Atlas
Subject
Experimental
Data
Microarray
Results
Region of
Interest
Animal Type
Anatomical
Properties
Images
Worked with Data Integration group to define global schema
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