10:30-12:00 how to build an ontology 1-2pm best practices and lessons learned 2-3pm birn ontologies:...
TRANSCRIPT
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10:30-12:00 How to Build an Ontology 1-2pm Best Practices and Lessons Learned 2-3pm BIRN Ontologies: An Overview
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http://ontology.buffalo.edu/smith2
How to Build an Ontology
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http://ontology.buffalo.edu/smith3
High quality shared ontologies build communities
General trend on the part of NIH, FDA and other bodies to consolidate ontology-based standards for the communication and processing of biomedical data.
NCIT / caBIG / NECTAR / BIRN / OBO ...
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http://ontology.buffalo.edu/smith4
TWO STRATEGIES:
Ad hoc creation of new database schemas for each research group / research hypothesisvs.
Pre-established interoperable stable reference ontologies in terms of which all database schemas need to be defined
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http://ontology.buffalo.edu/smith5
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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http://ontology.buffalo.edu/smith6
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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http://ontology.buffalo.edu/smith7
Types have instances
Ontologies are like science texts: they are about types
(Diaries, databases, clinical records are about instances)
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http://ontology.buffalo.edu/smith8
The need
strong general-purpose classification hierarchies created by domain specialists clear, rigorous definitionsthoroughly tested in real casesontologies teach us about the instances in reality by supporting cross-disciplinary (cross-ontology) reasoning about types
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http://ontology.buffalo.edu/smith9
The actuality (too often)
myriad special purpose ‘light’ ontologies, prepared by ontology engineers and deposited in internet ‘repositories’ or ‘registries’
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http://ontology.buffalo.edu/smith10
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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http://ontology.buffalo.edu/smith11
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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http://ontology.buffalo.edu/smith12
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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http://ontology.buffalo.edu/smith13
The long term begins here
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http://ontology.buffalo.edu/smith14
A methodology for quality-assurance 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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http://ontology.buffalo.edu/smith15
A methodology for quality-assurance 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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http://ontology.buffalo.edu/smith16
A methodology for quality-assurance 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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http://ontology.buffalo.edu/smith17
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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http://ontology.buffalo.edu/smith18
“Alignment of Multiple Ontologies of Anatomy: Deriving Indirect Mappings from Direct Mappings to a Reference Ontology”
Songmao ZhangOlivier Bodenreider
AMIA 2005
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http://ontology.buffalo.edu/smith19
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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http://ontology.buffalo.edu/smith20
A basic distinction
type vs. instance
science text vs. clinical document
man vs. Musen
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http://ontology.buffalo.edu/smith21
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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http://ontology.buffalo.edu/smith22
A 515287 DC3300 Dust Collector Fan
B 521683 Gilmer Belt
C 521682 Motor Drive Belt
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http://ontology.buffalo.edu/smith23
Ontology Types Instances
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http://ontology.buffalo.edu/smith24
Ontology = A Representation of Types
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http://ontology.buffalo.edu/smith25
Ontology = A Representation of Types
Each node of an ontology consists of:
• preferred term (aka term)
• term identifier (TUI, aka CUI)
• synonyms
• definition, glosses, comments
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http://ontology.buffalo.edu/smith26
Ontology = A Representation of Types
Nodes in an ontology are connected by relations:
primarily: is_a (= is subtype of) and part_of
designed to support search, reasoning and annotation
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http://ontology.buffalo.edu/smith27
siamese
mammal
cat
organism
substancetypes
animal
instances
frogleaf class
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http://ontology.buffalo.edu/smith28
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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http://ontology.buffalo.edu/smith29
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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http://ontology.buffalo.edu/smith30
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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http://ontology.buffalo.edu/smith31
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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http://ontology.buffalo.edu/smith32
Rules on types
Don’t confuse types with wordsDon’t confuse types with conceptsDon’t confuse types with ways of getting to
know typesDon’t confuse types with ways of talking
about typesDon’t confuses types with data about types
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http://ontology.buffalo.edu/smith33
Some other simple rules for high quality ontologies
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http://ontology.buffalo.edu/smith34
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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http://ontology.buffalo.edu/smith35
PositivityComplements 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 cellPurkinje neuron or dendritic shaft
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http://ontology.buffalo.edu/smith36
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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http://ontology.buffalo.edu/smith37
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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http://ontology.buffalo.edu/smith38
Multiple Inheritance
thing
carblue thing
blue car
is_a1 is_a2
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http://ontology.buffalo.edu/smith39
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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http://ontology.buffalo.edu/smith40
General Rule
Formulate universal statements first
Move to A may be B in such and such a context later
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http://ontology.buffalo.edu/smith41
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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http://ontology.buffalo.edu/smith42
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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http://ontology.buffalo.edu/smith43
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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http://ontology.buffalo.edu/smith44
Case Study: The National Cancer Institute Thesaurus (NCIT)
does not (yet) satisfy these and other simple principles
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http://ontology.buffalo.edu/smith45
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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http://ontology.buffalo.edu/smith46
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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http://ontology.buffalo.edu/smith47
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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http://ontology.buffalo.edu/smith48
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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http://ontology.buffalo.edu/smith49
Verbal Definitions
About half the NCIT terms are assigned verbal definitions
Unfortunately some are assigned more than one
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http://ontology.buffalo.edu/smith50
Disease Progression
Definition1Cancer 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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http://ontology.buffalo.edu/smith51
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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http://ontology.buffalo.edu/smith52
Cancer
a process (of getting better or worse)
an object (which can grow and spread)
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http://ontology.buffalo.edu/smith53
Confuses definitions with descriptions
Tuberculosis DefinitionA 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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http://ontology.buffalo.edu/smith54
Confuses definitions with descriptions
Tuberculosis DefinitionA 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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http://ontology.buffalo.edu/smith55
A better definition
Tuberculosis
Definition:
A chronic, recurrent infection caused by the bacterium Mycobacterium tuberculosis.
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http://ontology.buffalo.edu/smith56
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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http://ontology.buffalo.edu/smith57
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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http://ontology.buffalo.edu/smith58
Duratec, Lactobutyrin, Stilbene Aldehyde
are classified by the NCIT as Unclassified Drugs and Chemicals
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http://ontology.buffalo.edu/smith59
and problematic synonymsAnatomic Structure, System, or Substance ~ Anatomic
Structures and Systems
Does ‘anatomic’ apply only to structure or also to system and substance?
Biological Function ~ Biological Processsome biological processes are the exercises of biological
functionsothers (e.g. pathological processes, side effects) not
Genetic Abnormality ~ Molecular Abnormality (with subtype: Molecular Genetic Abnormality) (definitions not supplied)
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http://ontology.buffalo.edu/smith60
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 DiseaseA definite pathologic process with a characteristic set of
signs and symptoms. ...
Condition ProcessDefinition2 contradicts NCIT’s own classification hierarchy
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http://ontology.buffalo.edu/smith61
Three disjoint classes of plants
Vascular Plant
Non-vascular Plant
Other Plant
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http://ontology.buffalo.edu/smith62
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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http://ontology.buffalo.edu/smith63
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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http://ontology.buffalo.edu/smith64
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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http://ontology.buffalo.edu/smith65
The road ahead
http://www.cbd-net.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/Dictionary.do
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http://ontology.buffalo.edu/smith66
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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http://ontology.buffalo.edu/smith67
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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http://ontology.buffalo.edu/smith68
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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http://ontology.buffalo.edu/smith69
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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http://ontology.buffalo.edu/smith70
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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http://ontology.buffalo.edu/smith71
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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http://ontology.buffalo.edu/smith72
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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http://ontology.buffalo.edu/smith73
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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http://ontology.buffalo.edu/smith74
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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http://ontology.buffalo.edu/smith75
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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http://ontology.buffalo.edu/smith76
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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http://ontology.buffalo.edu/smith77
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 celladult human is_a humanbut not: adult is_a child
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http://ontology.buffalo.edu/smith78
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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http://ontology.buffalo.edu/smith79
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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http://ontology.buffalo.edu/smith80
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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http://ontology.buffalo.edu/smith81
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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http://ontology.buffalo.edu/smith82
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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http://ontology.buffalo.edu/smith83
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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http://ontology.buffalo.edu/smith84
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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http://ontology.buffalo.edu/smith85
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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http://ontology.buffalo.edu/smith86
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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http://ontology.buffalo.edu/smith87
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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http://ontology.buffalo.edu/smith88
Continuityinstance 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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http://ontology.buffalo.edu/smith89
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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http://ontology.buffalo.edu/smith90
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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http://ontology.buffalo.edu/smith91
Applications
Expectations of symmetry e.g. for protein-protein 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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http://ontology.buffalo.edu/smith92
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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http://ontology.buffalo.edu/smith93
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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http://ontology.buffalo.edu/smith94
c at t1
C
c at t
C1
time
same instance
transformation_of
pre-RNA mature RNA
adultchild
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http://ontology.buffalo.edu/smith95
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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http://ontology.buffalo.edu/smith96
embryological development C
c at t c at t1
C1
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http://ontology.buffalo.edu/smith97
C
c at t c at t1
C1
tumor development
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http://ontology.buffalo.edu/smith98
C
c at t
C1
c1 at t1
C'
c' at t
time
instances
zygote derives_fromovumsperm
derives_from
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http://ontology.buffalo.edu/smith99
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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http://ontology.buffalo.edu/smith100
A suite of defined relations between universals
Foundational is_apart_of
Spatial located_incontained_inadjacent_to
Temporal transformation_ofderives_frompreceded_by
Participation has_participanthas_agent
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http://ontology.buffalo.edu/smith101
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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http://ontology.buffalo.edu/smith102
Principles
1 anatomical structure 1 regionhas_location
Define the relationships of adjacency, connectedness etc. using RCC-8 and its extensions
DC EC PO TPP NTPP EQ
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http://ontology.buffalo.edu/smith103
Example 1Reasoning with part and location at the
instance level:
Inferior Frontal Gyrus Operc. Pars of Inferior Frontal Gyrus
Frontal Lobe
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http://ontology.buffalo.edu/smith104
Example 2
Reasoning with location, continuity and external connection
PreCentral Gyrus PostCentral GyrusFrontal Lobe
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http://ontology.buffalo.edu/smith105
Extension to the 3-D case
x
y
substances x, y participate in process B
time
Bx
y
SNAP-ti.
time
SPAN
B
slice of x’s life
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http://ontology.buffalo.edu/smith106
Most ontologies are execrableBut 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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http://ontology.buffalo.edu/smith107
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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http://ontology.buffalo.edu/smith108
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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http://ontology.buffalo.edu/smith109
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 re-examination and re-coding of all existing records to make them usable in future hypothesis generation and testing.
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http://ontology.buffalo.edu/smith110
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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http://ontology.buffalo.edu/smith111
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.
How to solve this problem?
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http://ontology.buffalo.edu/smith112
Not only data needs to be aligned through pre-established reference ontologies, so also does softwareCurrently, 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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http://ontology.buffalo.edu/smith113
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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http://ontology.buffalo.edu/smith114
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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http://ontology.buffalo.edu/smith115
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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http://ontology.buffalo.edu/smith116
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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http://ontology.buffalo.edu/smith117
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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http://ontology.buffalo.edu/smith118
Current Best Practice
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http://ontology.buffalo.edu/smith119
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http://ontology.buffalo.edu/smith120
Pleural Cavity
Pleural Cavity
Interlobar recess
Interlobar recess
Mesothelium of Pleura
Mesothelium of Pleura
Pleura(Wall of Sac)
Pleura(Wall of Sac)
VisceralPleura
VisceralPleura
Pleural SacPleural Sac
Parietal Pleura
Parietal Pleura
Anatomical SpaceAnatomical Space
OrganCavityOrganCavity
Serous SacCavity
Serous SacCavity
AnatomicalStructure
AnatomicalStructure
OrganOrgan
Serous SacSerous Sac
MediastinalPleura
MediastinalPleura
TissueTissue
Organ PartOrgan Part
Organ Subdivision
Organ Subdivision
Organ Component
Organ Component
Organ CavitySubdivision
Organ CavitySubdivision
Serous SacCavity
Subdivision
Serous SacCavity
Subdivision
part
_of
is_a
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http://ontology.buffalo.edu/smith121
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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http://ontology.buffalo.edu/smith122
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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http://ontology.buffalo.edu/smith123
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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http://ontology.buffalo.edu/smith124
GO now adopting structured definitions which contain both genus and differentiae
Species =def Genus + Differentiae
neuron cell differentiation =defdifferentiation by which a cell acquires features of a neuron
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http://ontology.buffalo.edu/smith125
Ontology alignmentOne of the current goals of GO is to align:
cone cell fate commitment 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
Cell Types in GO Cell Types in the Cell Ontologywith
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http://ontology.buffalo.edu/smith126
Alignment of the two ontologies will permit the generation of consistent and complete definitions
id: CL:0000062name: osteoblastdef: "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:0000055relationship: develops_from CL:0000008relationship: develops_from CL:0000375
GO
Cell type
New Definition
+
=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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http://ontology.buffalo.edu/smith127
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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http://ontology.buffalo.edu/smith128
eventually to comprehend all of OBO
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http://ontology.buffalo.edu/smith129
Pleural Cavity
Pleural Cavity
Interlobar recess
Interlobar recess
Mesothelium of Pleura
Mesothelium of Pleura
Pleura(Wall of Sac)
Pleura(Wall of Sac)
VisceralPleura
VisceralPleura
Pleural SacPleural Sac
Parietal Pleura
Parietal Pleura
Anatomical SpaceAnatomical Space
OrganCavityOrganCavity
Serous SacCavity
Serous SacCavity
AnatomicalStructure
AnatomicalStructure
OrganOrgan
Serous SacSerous Sac
MediastinalPleura
MediastinalPleura
TissueTissue
Organ PartOrgan Part
Organ Subdivision
Organ Subdivision
Organ Component
Organ Component
Organ CavitySubdivision
Organ CavitySubdivision
Serous SacCavity
Subdivision
Serous SacCavity
Subdivision
part
_of
is_a
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http://ontology.buffalo.edu/smith130
Anatomical Entity
Physical Anatomical Entity
Material Physical Anatomical Entity
-is a-
Non-material Physical Anatomical Entity
ConceptualAnatomical Entity
AnatomicalStructure
BodySubstance
BodyPart
HumanBody
OrganSystem
OrganCell
OrganPart
AnatomicalSpace
Anatomical Relationship
CellPart
Biological Macromolecule
Tissue
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http://ontology.buffalo.edu/smith131
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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http://ontology.buffalo.edu/smith132
at every level of granularity
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http://ontology.buffalo.edu/smith133
What do the kidneys do?Modularity
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http://ontology.buffalo.edu/smith134
How does a kidney work?NEPHRON
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http://ontology.buffalo.edu/smith135
Nephron FunctionsFUNCTIONAL SEGMENTS
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http://ontology.buffalo.edu/smith136
Top-Level Categories in the FMAanatomical
entity
non-physicalanatomical entity
physicalanatomical entity
anatomical relationship
body substance
material physical anatomical entity
anatomical structure
non-material physical anatomical entity
body space
boundary anatomical attribute
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http://ontology.buffalo.edu/smith137
anatomical structure (cell, lung, nerve, tooth)
result from the coordinated expression of structural genes
have their own 3-D shape
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http://ontology.buffalo.edu/smith138
portion of body substance
inherits its shape from container
portion of urine
portion of menstrual fluid
portion of blood
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http://ontology.buffalo.edu/smith139
anatomical space
cavities, conduits
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http://ontology.buffalo.edu/smith140
anatomical attribute
mass
weight
temperature
your temperature
its value now
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http://ontology.buffalo.edu/smith141
anatomical relationship
located_in
contained_in
adjacent_to
connected_to
surrounds
lateral_to (West_of)
anterior_to
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http://ontology.buffalo.edu/smith142
boundary
bona fide / fiat
www.enel.ucalgary.ca/ People/Mintchev/stomach.htm
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http://ontology.buffalo.edu/smith143
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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http://ontology.buffalo.edu/smith144
Pleural Cavity
Pleural Cavity
Interlobar recess
Interlobar recess
Mesothelium of Pleura
Mesothelium of Pleura
Pleura(Wall of Sac)
Pleura(Wall of Sac)
VisceralPleura
VisceralPleura
Pleural SacPleural Sac
Parietal Pleura
Parietal Pleura
Anatomical SpaceAnatomical Space
OrganCavityOrganCavity
Serous SacCavity
Serous SacCavity
AnatomicalStructure
AnatomicalStructure
OrganOrgan
Serous SacSerous Sac
MediastinalPleura
MediastinalPleura
TissueTissue
Organ PartOrgan Part
Organ Subdivision
Organ Subdivision
Organ Component
Organ Component
Organ CavitySubdivision
Organ CavitySubdivision
Serous SacCavity
Subdivision
Serous SacCavity
Subdivisionbasis for generalization to other species
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http://ontology.buffalo.edu/smith145
Pleural Cavity
Pleural Cavity
Interlobar recess
Interlobar recess
Mesothelium of Pleura
Mesothelium of Pleura
Pleura(Wall of Sac)
Pleura(Wall of Sac)
VisceralPleura
VisceralPleura
Pleural SacPleural Sac
Parietal Pleura
Parietal Pleura
Anatomical SpaceAnatomical Space
OrganCavityOrganCavity
Serous SacCavity
Serous SacCavity
AnatomicalStructure
AnatomicalStructure
OrganOrgan
Serous SacSerous Sac
MediastinalPleura
MediastinalPleura
TissueTissue
Organ PartOrgan Part
Organ Subdivision
Organ Subdivision
Organ Component
Organ Component
Organ CavitySubdivision
Organ CavitySubdivision
Serous SacCavity
Subdivision
Serous SacCavity
Subdivision
part
_of
is_a
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http://ontology.buffalo.edu/smith146
Web-Based Representations of Neuroanatomy
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http://ontology.buffalo.edu/smith147
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http://ontology.buffalo.edu/smith148
includes Neuronames
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http://ontology.buffalo.edu/smith149
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Human Morphometry and Function BIRN Testbeds
with thanks to Christine Fennema-Notestine and Jessica Turner
CBiO/BIRN Workshop 2006
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http://ontology.buffalo.edu/smith151
BIRN Ontology NeedsGOAL: 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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http://ontology.buffalo.edu/smith152
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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http://ontology.buffalo.edu/smith153
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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http://ontology.buffalo.edu/smith154
Existing neuroanatomical ontology
Need to create related “function”-based ontology
Brain
Cerebellum Cerebrum
Cerebral white matter …
Frontal cortex Temporal cortex
Superior temporal Mesial temporal
Amygdala Hippocampus
…
Cerebral cortex
…
…
…
Memory
CVLT
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http://ontology.buffalo.edu/smith155
‘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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http://ontology.buffalo.edu/smith156
‘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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http://ontology.buffalo.edu/smith157
‘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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http://ontology.buffalo.edu/smith158
Brain
Cerebrum
Temporal
Mesial temporal
Hippocampus
Cerebral cortex
CVLT
Task and score description
Frontal Cognitiveimpairment
Cognition
Assessment
Neuropsychology
Amnesia
Memory Learning
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http://ontology.buffalo.edu/smith159
Memory
CVLT SIRP
Assessment
Behavioral Paradigm
Cognitive Process
Attention
Working memory Long Term memory
SCID-Patient
Breathhold
Action
Can we reason on the basis of a graph of this sort?
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http://ontology.buffalo.edu/smith160
Bonfire Relations
relation: the type of relation between the concept to the left and the concept to the rightPAR = ParentCHD = ChildSIB = SiblingRB = Broader RelationshipRN = Narrower RelationshipRO = Other Relationship
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http://ontology.buffalo.edu/smith161
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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http://ontology.buffalo.edu/smith162
“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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http://ontology.buffalo.edu/smith163
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 SystemsNational Socialism is_a Anthropology ...
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http://ontology.buffalo.edu/smith164
MeSH
National Socialism is_a MeSH Descriptor
Cf. NeuroNames:
Ontology =def a codification of the relationships between words and concepts
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http://ontology.buffalo.edu/smith165
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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http://ontology.buffalo.edu/smith166
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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http://ontology.buffalo.edu/smith167
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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http://ontology.buffalo.edu/smith168
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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http://ontology.buffalo.edu/smith169
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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http://ontology.buffalo.edu/smith170
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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http://ontology.buffalo.edu/smith171
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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http://ontology.buffalo.edu/smith172
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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http://ontology.buffalo.edu/smith173
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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http://ontology.buffalo.edu/smith174
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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http://ontology.buffalo.edu/smith175
NN’s main hierarchyis 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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http://ontology.buffalo.edu/smith176
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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http://ontology.buffalo.edu/smith177
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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http://ontology.buffalo.edu/smith178
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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http://ontology.buffalo.edu/smith179
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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http://ontology.buffalo.edu/smith180
BIRNLex
does provide definitions, normally taken over from UMLS
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http://ontology.buffalo.edu/smith181
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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http://ontology.buffalo.edu/smith182
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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http://ontology.buffalo.edu/smith183
BIRNLex
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http://ontology.buffalo.edu/smith184
BIRNLex
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http://ontology.buffalo.edu/smith185
BIRNLex
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http://ontology.buffalo.edu/smith186
BIRNLex
bear in mind always that your ontology needs
to be interoperable with other ontologies
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http://ontology.buffalo.edu/smith187
BIRNLex
bear in mind always that your ontology needs
to be interoperable with other ontologies
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http://ontology.buffalo.edu/smith188
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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http://ontology.buffalo.edu/smith189
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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http://ontology.buffalo.edu/smith190
BIRNLex and UMLS-SN
Rest =SN Daily or Recreational ActivityPrincipal Investigator =SN Professional or Occupational Group
Left handedness =SN Organism AttributeAmbidextrous =SN Finding
Brain Imaging =SN Diagnostic ProcedureBrain Mapping =SN Diagnostic Procedure & Research Activity
Healthy Adult =SN Finding
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http://ontology.buffalo.edu/smith191
BIRNLex
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Mouse BIRN: Ontologies
Maryann Martone
and
Bill Bug
2005 All Hands Meeting
Mouse BIRN: Ontologies
Maryann Martone and Bill Bug
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http://ontology.buffalo.edu/smith193
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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http://ontology.buffalo.edu/smith194
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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http://ontology.buffalo.edu/smith195
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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http://ontology.buffalo.edu/smith196
BIRN Ontology Resources
Mouse BIRN Ontology Resource Page
http://nbirn.net/Resources/Users/Ontologies/
Bonfire Ontology Browser and Extension Tool
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http://ontology.buffalo.edu/smith197
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 DictionaryTerm 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
cerebellum
cerebellar cortex
Purkinje cell
dendritic spine
Entopeduncular nucleus
Globus pallidus, internal segment
Animal Model Disease Process
•***Map between Human and Animal models
•Functional assessment
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Anatomical Knowledge SourcesFoundational model of anatomyNeuronames (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.”
-OTF report to BEC 8/05
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Other Resources Likely of Use
Mouse Phenome Project: a collection of phenotypic and genotypic data for the laboratory mouse
anatomybehaviorbiological factorsbloodcancerdiet effectsdrug effects, toxicitygenotype 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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http://ontology.buffalo.edu/smith204
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
ExperimentalData
MolecularDistributions
Subject
Animal Type
Experiments
AnatomicalProperties
MicroarrayResults
Images
Atlas
Region ofInterest
Worked with Data Integration group to define global schema