How to Build a Biomedical Ontology
Success StoriesThe Gene Ontology (GO)
SNOMED, ICD and other controlled vocabulariesOntology Design Principles
Ontology Applications
Barry Smithhttp://ontology.buffalo.edu/smith
Uses of ‘ontology’ in PubMed abstracts
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By far the most successful: GO (Gene Ontology)
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Hierarchical view of GO representing relations between represented types
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Gene Ontology
$100 mill. invested in literature and database curation using the Gene Ontology (GO)
based on the idea of annotation
over 11 million annotations relating gene products (proteins) described in the UniProt, Ensembl and other databases to terms in the GO
multiple secondary uses – because the ontology was not built to meet one specific set of requirements
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GO provides a controlled system of terms for use in annotating (describing, tagging)
data• multi-species, multi-disciplinary, open source
• contributing to the cumulativity of scientific results obtained by distinct research communities
• compare use of kilograms, meters, seconds in formulating experimental results
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Sample Gene Array Data
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where in the cell ?
what kind of molecular function ?
semantic annotation of data
what kind of biological process?
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natural language labels
to make the data cognitively accessible to human beings
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compare: legends for mapscompare: legends for maps
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compare: legends for diagrams
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ontologies are legends for data
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compare: legends for mapscompare: legends for maps
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ontologies are legends for images
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what lesion ?
what brain function ?
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ontologies are legends for databases
MouseEcotope GlyProt
DiabetInGene
GluChem
sphingolipid transporter
activity
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annotation using common ontologies yields integration of databases
MouseEcotope GlyProt
DiabetInGene
GluChem
Holliday junction helicase complex
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annotation using common ontologies can support comparison of data
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annotation with Gene Ontology
supports reusability of data
supports search of data by humans
supports comparison of data
supports aggregation of data
supports reasoning with data by humans and machines
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The goal: virtual science
• consistent (non-redundant) annotation
• cumulative (additive) annotation
yielding, by incremental steps, a virtual map of the entirety of reality that is accessible to computational reasoning
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This goal is realizable if we have a common ontology framework
data is retrievable
data is comparable
data is integratable
only to the degree that it is annotated using a common controlled vocabulary
– compare the role of seconds, meters, kilograms … in unifying science
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To achieve this end we have to engage in something like philosophy (?)
is this the right way to organize the top level of this portion of the GO?how does the top level of this ontology relate to the top levels of other, neighboring ontologies? 25
Strategy for doing this
see the world as organized via types/universals/categories which are hierarchically organized
and in relation to which statements can be formulated which are universally true of all instances:
cell membrane part_of cell26
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
Foundational Model of Anatomy Ontology27
siamese
mammal
cat
organism
substancespecies, genera
animal
instances
frog
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with thanks to http://dbmotion.com 30
the problem of continuity of care: patients move around
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synchronic and diachronic problems of semantic interoperability
(across space and across time)
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how can we link EHR 1 to EHR 2 in a reliable, trustworthy, useful way, which
both systems can understand ?
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EHR 1 EHR 2
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the ideal solution: WHO International Classification of
Diseases
fICD
EHR 1 EHR 2
ICDICDPRO: De facto US billing standardMultilanguageCON: De facto US billing standard (corrupts data)No definitions of terms, and so difficult to
judge accuracy of hierarchy and of codingInconsistent hierarchiesHard to reason with resultsHence few secondary uses e.g. for research
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ICD 11ICD 11The (ontology-based) planmultiple views including
◦billing ◦public health statistics◦research
◦SNOMED compatibility
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the ideal solution: a single universal clinical vocabulary
fSNOMED-CT
EHR 1 EHR 2
SNOMED CT: SNOMED CT: Systematized Nomenclature of Systematized Nomenclature of Medicine-Clinical TermsMedicine-Clinical Terms
PRO:International standard (sort of)Huge resourceFree for member countriesMulti-language (including Spanish)
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SNOMED CTSNOMED CTCONHuge (but redundant ... and gappy)Contains many examples of false synonymyStill in need of work
◦ No consistent interpretation of relations◦ Many erroneous relation assertions◦ Many idiosyncratic relations◦ Mixes ontology with epistemology◦ It contains numerous compound terms (e.g., test for
X) without the constituent terms (here: X), even where the latter are of obvious salience
(38
Coding with SNOMED-CT is unreliable and inconsistent
Multi-stage multi-committee process for adding terms that follows intuitive rules and not formal principles
Does there exist a strategy for evolutionary improvement?
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SNOMED CT
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fanf
above all: SNOMED CT cannot solve the problem of continuity of care because it has
too much redundancy
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EHR 1 EHR 2
SNOMED-CT
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fanf
AND because it is used only in certain countries
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EHR 1 EHR 2
SNOMED-CT
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link EHR 1 to EHR 2 through a snapshot of the patient’s condition which both systems
can understand
f Unified Medical Language System
(UMLS)
EHR 1 EHR 2
Unified Medical Language Unified Medical Language System (UMLS)System (UMLS)
UMLS is not unified, not a language, not a system (and not only medical); it is an aggregation If we use something like UMLS as reference terminology, we will not solve the translation problem
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EN
DE
New York State Center of Excellence in Bioinformatics & Life Sciences
R T U New York State Center of Excellence in Bioinformatics & Life Sciences
R T U
UMLS approach to countering silo formation– By ‘linking between different clinical or biomedical
vocabularies’
– However: ‘… the Metathesaurus does not represent a comprehensive NLM-authored ontology of biomedicine or a single consistent view of the world. The Metathesaurus preserves the many views of the world present in its source vocabularies because these different views may be useful for different tasks.’
http://www.nlm.nih.gov/pubs/factsheets/umlsmeta.html
New York State Center of Excellence in Bioinformatics & Life Sciences
R T U New York State Center of Excellence in Bioinformatics & Life Sciences
R T U
Prospective standardization is a good thing
Prospective standardization is the only thing which will work in mission critical domains
Prospective standardization means that certain limits to tolerance must be imposed,
Need for top-down governance to ensure common architecture and resolution of border disputes in areas of overlap between domains
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Principles of Best Practice in Ontology Development
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Problem of ensuring sensible cooperation in a massively interdisciplinary community
Consider multiple uses of technical terms such as
− type− concept− instance− model− representation− data
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Three Levels
L3. Words, models (published representations, ontologies, databases ...)
L2. Ideas (concepts, thoughts, memories, ...)
L1. Things (cells, planets, processes of cell division ...)
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Entity =def
anything which exists, including things and processes, functions and qualities, beliefs and actions, documents and software
(entities on levels 1, 2 and 3)
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First basic distinction among entities
type vs. instance
(science text vs. diary)
(human being vs. Tom Cruise)
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For ontologies
it is generalizations that are important = types, universals,
kinds, species
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A 515287 DC3300 Dust Collector Fan
B 521683 Gilmer Belt
C 521682 Motor Drive Belt
Catalog vs. inventory
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An ontology is a representation of types
We learn about types in reality from looking at the results of scientific experiments in the form of scientific theories
experiments relate to what is particular science describes what is general
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Ontology =def.
a representational artifact whose representational units (which may be drawn from a natural or from some formalized language) are intended to represent
1. types in reality
2. those relations between these types which obtain universally (= for all instances)
lung is_a anatomical structure
lobe of lung part_of lung
in accordance with our best current established science
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siamese
mammal
cat
organism
objecttypes
animal
frog
instances56
Domain =def
a portion of reality that forms the subject-matter of a single science or technology or mode of study or administrative practice:
proteomics
epidemiology
C2
M&S
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Representation =def
an image, idea, map, picture, name or description ... of some entity or entities.
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Ontologies are representational artifacts
comparable to science textsand subject to the same sorts of
constraints (including need for update)
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Representational units =def
terms, icons, alphanumeric identifiers ... which refer, or are intended to refer, to entities
and which are minimal (atoms)
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Composite representation =def
representation
(1) built out of representational units
which
(2) form a structure that mirrors, or is intended to mirror, the entities in some domain
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Periodic Table
The Periodic Table
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Ontologies are here
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or here
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Ontologies represent general structures in reality (leg)
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Ontologies do not represent concepts in people’s heads
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They represent types in reality
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How do we know which general terms designate types?
Types are repeatables:
cell, electron, weapon, F16 ...
Instances are one-off:
Bill Clinton, this laptop, this handwave
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Problem
The same general term can be used to refer both to types and to collections of particulars. Consider:
HIV is an infectious retrovirus
HIV is spreading very rapidly through Asia
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Class =def
a maximal collection of particulars determined by a general term (‘cell’, ‘electron’ but also: ‘ ‘restaurant in Palo Alto’, ‘Italian’)
the class A = the collection of all particulars x for which ‘x is A’ is true
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types vs. their extensions
types
{a,b,c,...} collections of particulars
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Extension
=def The extension of a type is the class of its instances
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types vs. classes
types
{c,d,e,...} classes
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types vs. classes
compare: ‘natural kinds’
types
extensions other sorts of classes
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types vs. classes
types
populations, ...
the class of all diabetic patients in Leipzig on 4 June 1952
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OWL is a good representation of classes
• F16s
• sibling of Finnish spy
• member of Abba aged > 50 years
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types, classes, concepts
types
classes
‘concepts’ ?
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types < classes < ‘concepts’ ?
Cases of ‘concepts’ which, some people say, do not correspond to classes:
‘Cancelled oophorectomy’‘Absent nipple’‘Unlocalized ligand’
A cancelled oophorectomy is not a special kind of conceptual oophorectory
Use: Information Artifact Ontology (IAO)
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Principle of Low Hanging Fruit
Include even absolutely trivial assertions (assertions you know to be universally true)
pneumococcal virus is_a virus
Computers need to be led by the hand
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Example: 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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Principle of Singular Nouns
Terms in ontologies represent types
Goal: Each term in an ontology should represent exactly one type
Thus every term should be a singular noun
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Principle: do not commit the use-mention confusion
mouse =def. common name for the species mus musculus
swimming is healthy and has eight letters
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Principle: do not commit the use-mention confusion
Avoid confusing between words and things
Avoid confusing between concepts in our minds and entities in reality
Recommendation: avoid the word ‘concept’ entirely
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Trialbank
‘information’ = def. ‘a written or spoken designation of a concept’
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‘Heparin therapy’ is an instance of ‘written or spoken designation of a concept’
What are the problems here?
1. misuse of quotation marks
2. confusion of instances and types
3. confusion of concept and reality
Trialbank
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Principle: beware of terminological baggage
For the sake of interoperability with other ontologies, do not give special meanings to terms with established general meanings
(Don’t use ‘cell’ when you mean ‘plant cell’)
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ICNP: International Classification of Nursing Procedures (old version)
water =def. a type of Nursing Phenomenon of Physical Environment with the specific characteristics: clear liquid compound of hydrogen and oxygen that is essential for most plant and animal life influencing life and development of human beings.
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Principle of definitions
Supply definitions for every term
1.human-understandable natural language definition
2.an equivalent formal definition
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Principle: definitions must be unique
Each term should have exactly one definition
it may have both natural-language and formal versions
(issue with ontologies which exist with different levels of expressivity)
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The Problem of Circularity
A Person =def. A person with an identity document
Hemolysis =def. The causes of hemolysis
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Principle of non-circularity
The term defined should not appear in its own definition
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Example: HL7
‘stopping a medication’ = def.
change of state in the record of a Substance Administration Act from Active to Aborted
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Principle of Increase in Understandability
A definition should use only terms which are easier to understand than the term defined
Definitions should not make simple things more difficult than they are
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Generalized Tarski principle (a good, general constraint on a
theory of meaning)
For each linguistic expression ‘E’
‘E’ means E
‘snow’ means: snow
‘pneumonia’ means: pneumonia
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HL7 Reference Information Model
‘medication’ does not mean: medication
rather it means:
the record of medication in an information system
‘disease’ does not mean: disease
rather it means:
the observation of a disease
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Principle of Acknowledging Primitives
In every ontology some terms and some relations are primitive = they cannot be defined (on pain of infinite regress)
Examples of primitive relations:
identity
instance_of
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Principle of Aristotelian Definitions
Use Aristotelian definitions
An A is a B which C’s.
A human being is an animal which is rational
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Rules for Formulating Terms
Avoid abbreviations even when it is clear in context what they mean (‘breast’ for ‘breast tumor’)
Avoid acronymsAvoid mass terms (‘tissue’, ‘brain mapping’,
‘clinical research’ ...)Treat each term ‘A’ in an ontology is
shorthand for a term of the form ‘the type A’
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Univocity Terms should have the same meanings on
every occasion of use.
(= They should refer to the same types)
Basic ontological relations such as is_a and part_of should be used in the same way by all ontologies
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Universality
Ontologies are made of relational assertions
They should include only those which hold universally
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Universality
Often, order will matter:
We can assert
adult transformation_of child
but not
child transforms_into adult
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Universality
viral pneumonia caused by virus
but not
virus causes pneumonia
pneumococcal virus causes pneumonia
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Principle of Universality
results analysis later_than protocol-design
but not
protocol-design earlier_than results analysis
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Principle of PositivityComplements of types are not themselves types.
Terms such as
non-mammal non-membrane other metalworker in New Zealand
do not designate types in reality
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Generalized Anti-Boolean Principle
There are no conjunctive and disjunctive types:
anatomic structure, system, or substance
musculoskeletal and connective tissue disorder
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Objectivity
Which types exist in reality is not a function of our knowledge.
Terms such as
unknown
unclassified
unlocalized
arthropathies not otherwise specified
do not designate types in reality.106
Keep Epistemology Separate from Ontology
If you want to say that
We do not know where A’s are located
do not invent a new class of
A’s with unknown locations
(A well-constructed ontology should grow linearly; it should not need to delete classes or relations because of increases in knowledge)
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If you want to say
I surmise that this is a case of pneumonia
do not invent a new class of surmised pneumonias
Confusion of ‘findings’ in medical terminologies
Keep Sentences Separate from Terms
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Single Inheritance
No kind in a classificatory hierarchy should be asserted to have more than one is_a parent on the immediate higher level
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Multiple Inheritance
thing
carblue thing
blue car
is_a is_a
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Multiple Inheritance
is a source of errors
encourages laziness
serves as obstacle to integration with neighboring ontologies
hampers use of Aristotelian methodology for defining terms
hampers use of statistical search tools
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Multiple Inheritance
thing
carblue thing
blue car
is_a1 is_a2
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Principle of asserted single inheritance
Each reference ontology module should be built as an asserted monohierarchy (a hierarchy in which each term has at most one parent)
Asserted hierarchy vs. inferred hierarchy
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Principle of normalization
Polyhierarchies should be decomposable into homogeneous disjoint monohierarchies
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Principle of instantiability
A term should be included in an ontology only if there is evidence that instances to which that term refers exist or have existed or can exist in reality.
Fist
Crowd
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Avoid mass nouns
Count nouns = an organism, a planet, a handshake
Mass nouns = tissue, information, discourse
Mass nouns almost always go hand in hand with ontological confusion
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is_a Overloading
The success of ontology alignment demands that ontological relations (is_a, part_of, ...) have the same meanings in the different ontologies to be aligned.
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Multiple Inheritance
thing
carblue thing
blue car
is_a1 is_a2
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How to solve this problem
Create two ontologies:
of cars
of colors
Link the two together via cross-products
(= factoring, normalization, modularization)
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Compositionality
The meanings of compound terms should be determined
1. by the meanings of component terms
together with
2. the rules governing syntax
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User feedback principle
An ontology should evolve on the basis of feedback derived from those who are using the ontology for example for purposes in annotation.
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