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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1030-1200 How to Build an Ontology 1-2pm Best
Practices and Lessons Learned 2-3pm BIRN
Ontologies An Overview
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How to Build an Ontology
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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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TWO STRATEGIESAd 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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• 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 Brodmanns
  area, or a sulcal/gyral area, etc.). Well 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 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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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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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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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 OntologySongmao ZhangOlivier
BodenreiderAMIA 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 515287 DC3300 Dust Collector Fan
B 521683 Gilmer Belt
C 521682 Motor Drive Belt

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Ontology Types Instances




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Ontology A Representation of Types




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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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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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types
mammal
frog
leaf class
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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

• Dont confuse types with words
• Dont confuse types with concepts
• Dont confuse types with ways of getting to know
  types
• Dont confuse types with ways of talking about
  types
• Dont 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 NCITs 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.cbd-net.com/index.php/search/show/9384
  64
• 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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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 powerYou cannot cheat by
borrowing from UMLSUMLS ( 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
  vocabularys 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
  patients 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

• between instances
• Marys heart part_of Mary
• this nucleus part_of this cell
• 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.

86
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

87
Kinds of relations

• Between types
• is_a, part_of, ...
• Between an instance and a type
• this explosion instance_of the type explosion
• Between instances
• Marys 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.

89
Adjacency as a relation between universals is not
symmetric

• Consider
• seminal vesicle adjacent_to urinary bladder
• Not urinary bladder adjacent_to seminal vesicle

90

• 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

91
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

92
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

93
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
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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
97
tumor development
98
derives_from
C1 c1 at t1
C c at t
time
C' c' at t
ovum
zygote derives_from
sperm
99
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.

100
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
101
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

102
Principles

• 1 anatomical structure ? 1 regionhas_location
• Define the relationships of adjacency,
  connectedness etc. using RCC-8 and its extensions

NTPP
PO
TPP
EQ
DC
EC
103
Example 1

• Reasoning with part and location at the instance
  level

Operc. Pars of Inferior Frontal Gyrus
Inferior Frontal Gyrus
104
Example 2

• Reasoning with location, continuity and external
  connection

PreCentral Gyrus
PostCentral Gyrus
105
Extension to the 3-D case
106
Most ontologies are execrableBut some good
ontologies do already exist

• as far as possible dont 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

107
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

108
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
109
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.

110
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

111

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.

112
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

113
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.

114
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.

115
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.

116
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

117
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 Space
Anatomical Structure
Organ Cavity Subdivision
Organ Cavity
Organ
Serous Sac
Organ Component
Serous Sac Cavity
Tissue
Serous Sac Cavity Subdivision
is_a
Pleural Sac
Pleura(Wall of Sac)
Pleural Cavity
part_of
Parietal Pleura
Visceral Pleura
Interlobar recess
Mediastinal Pleura
Mesothelium of Pleura
121
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

122
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 FMAs term
  hierarchy can be translated very cleanly into a
  computer representation

124
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
125
Ontology alignmentOne of the current goals of GO
is to align
Cell Types in GO
Cell Types in the Cell Ontology
with

• cone cell fate commitment

• retinal_cone_cell

keratinocyte
keratinocyte differentiation
fat_cell
adipocyte differentiation
dendritic_cell
dendritic cell activation
lymphocyte
lymphocyte proliferation
T_lymphocyte
T-cell homeostasis
garland_cell
garland cell differentiation
heterocyst
heterocyst cell differentiation
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Alignment of the two ontologies will permit the
generation of consistent and complete definitions
GO

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.
New Definition
127
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
129
Anatomical Space
Anatomical Structure
Organ Cavity Subdivision
Organ Cavity
Organ
is_a
Serous Sac
Organ Component
Serous Sac Cavity
Tissue
Serous Sac Cavity Subdivision
Pleural Sac
Pleura(Wall of Sac)
Pleural Cavity
part_of
Parietal Pleura
Visceral Pleura
Interlobar recess
Mediastinal Pleura
Mesothelium of Pleura
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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
133
Modularity
134
How does a kidney work?
NEPHRON
135
Nephron Functions
FUNCTIONAL SEGMENTS
136
Top-Level Categories in the FMA
137

• anatomical structure (cell, lung, nerve, tooth)
• result from the coordinated expression of
  structural genes
• have their own 3-D shape

138

• portion of body substance
• inherits its shape from container
• portion of urine
• portion of menstrual fluid
• portion of blood

139

• anatomical space
• cavities, conduits

140

• anatomical attribute
• mass
• weight
• temperature
• your temperature
• its value now

141

• 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

www.enel.ucalgary.ca/ People/Mintchev/stomach.htm
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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.

144
basis for generalization to other species
Anatomical Space
Anatomical Structure
Organ Cavity Subdivision
Organ Cavity
Organ
Serous Sac
Organ Component
Serous Sac Cavity
Tissue
Serous Sac Cavity Subdivision
Pleural Sac
Pleura(Wall of Sac)
Pleural Cavity
Parietal Pleura
Visceral Pleura
Interlobar recess
Mediastinal Pleura
Mesothelium of Pleura
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Anatomical Space
Anatomical Structure
Organ Cavity Subdivision
Organ Cavity
Organ
Serous Sac
Organ Component
Serous Sac Cavity
Tissue
Serous Sac Cavity Subdivision
is_a
Pleural Sac
Pleura(Wall of Sac)
Pleural Cavity
part_of
Parietal Pleura
Visceral Pleura
Interlobar recess
Mediastinal Pleura
Mesothelium of Pleura
146
Web-Based Representations of Neuroanatomy
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includes Neuronames
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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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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 mBIRNs
  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

• Need to create related function-based ontology

Cerebellum
Cerebrum
CVLT
Cerebral white matter

Cerebral cortex

Frontal cortex
Temporal cortex
Memory
Mesial temporal

Superior temporal

Amygdala
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.

158
Assessment
Brain
Neuropsychology
Cerebrum
Amnesia
Cognition
Cerebral cortex
Frontal
Temporal
Cognitive impairment
Memory
Learning
Mesial temporal
CVLT
Hippocampus
Task and score description
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Can we reason on the basis of a graph of this
sort?
Behavioral Paradigm
Assessment
SCID-Patient
SIRP
CVLT
Breathhold
Long Term memory
Working memory
Memory
Attention
Cognitive Process
Action
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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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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
UMLSEtiology, Diagnosis, Treatment,
Complications and PreventionOlivier 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

• Users 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 doesnt answer the users
  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 Alzheimers 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., Alzheimers disease
  is a disorder of memory

How block information overload?
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Bottom-up search

• Users 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 doesnt answer the users
  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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NNs 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

181
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

183
BIRNLex
184
BIRNLex
185
BIRNLex
186
BIRNLex
bear in mind always that your ontology needs to
be interoperable with other ontologies
187
BIRNLex
bear in mind always that your ontology needs to
be interoperable with other ontologies
188
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

191
BIRNLex
192
Mouse BIRN Ontologies
Mouse BIRN Ontologies

• Maryann Martone
• and
• Bill Bug

Maryann Martone and Bill Bug
2005 All Hands Meeting
193
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

194
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

195
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
Mouse BIRN Ontology Resource Page
http//nbirn.net/Resources/Users/Ontologies/
197
Current Ontology Development by Mouse BIRN
Participants

• Developmental Ontology
• Seth Ruffins, Cal Tech
• Subcellular Anatomy
• Maryann Martone and Lisa Fong, UCSD

198
Ontology for Subcellular Anatomy of Nervous System
199
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
Linking animal and human imaging data
Navigating through Multi-resolution information
brain
Entopeduncular nucleus
Globus pallidus, internal segment
cerebellum
Disease Process
Animal Model
cerebellar cortex
Purkinje cell

• Map between Human and Animal models
• Functional assessment

dendritic spine
201
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
ran