how to build an ontology

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How to Build an Ontology

Barry Smith

http://ontology.buffalo.edu/smith

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Mission of the NCBOTo create software and support services for

science-based ontology development and use in the biomedical domain

Science-based = ontologies for support of scientific research (taken as encompassing evidence-based medicine)

Science-based = using the scientific method as part of the process of ontology development and testing

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Scientific ontologies have special features

Every term in a scientific ontology must be such that the developers of the ontology believe it to refer to some entity on the basis of the best current evidence

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For scientific ontologies

reusability is crucial

compatibility with neighboring scientific ontologies is crucial it should not be too

easy to add new terms to an ontology

we want to introduce these features in clinical medicine ...

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An Ontological SquareUpper-level integrating ontologies

Domain ontologies

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Domain ontologies

Ontologies in support of science

Administrative ontologies

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Domain ontologies

Ontologies in support of science

BFO (Basic Formal Ontology)

DOLCE

SNOMED

SwissProt

FMA

Administrative ontologies(for e-commerce, etc.)

FOAF top level:

person, topic, document, primary topic ...

Amazon.com ontology

Library of Congress Catalog

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Problem of ensuring sensible cooperation in a massively interdisciplinary community

concepttypeinstancemodelrepresentationdata

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RetailPrice hasA Denomination InstanceOf Dollar (p. 101)

SI-Unit instanceof System-of-Units (p. 40)

from Handbook of Ontology(Semantic Web approach)

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from: Ontological Engineering(Semantic Web approach)

location =def. a spatial point identified by a name (p. 12)

arrivalPlace =def. a journey ends at a location (p. 13)

facet =def. ternary relation that holds between a frame, a slot, and the facet (p. 51)

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Entity =def

anything which exists, including things and processes, functions and qualities, beliefs and actions, documents and software (Levels 1, 2 and 3)

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First basic distinction

universal vs. instance

(science text vs. diary)

(man vs. Maximilian)

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Instances databases

For scientific ontologies

it is generalizations that are important = universals, types, 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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Catalog vs. inventory

Catalog of Universals/Types

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Ontology Universals Instances

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Ontology = A Representation of Universals

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Ontology = A representation of universals

Each node of an ontology consists of:

• preferred term (aka term)

• term identifier (TUI, aka CUI)

• synonyms

• definition, glosses, comments

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An ontology is a representation of universals

We learn about universals in reality from looking at the results of scientific experiments in the form of scientific theories – which describe not what is particular in reality but what is general

siamese

mammal

cat

organism

substanceuniversals

animal

instances

frogleaf class

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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

HIV

epidemiology

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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 texts

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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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What do ontologies represent?

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Ontologies do not represent concepts in people’s heads

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They represent universals in reality

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“leg” is not the name of a concept

concepts do not stand in

part_of

connectedness

causes

treats ...

relations to each other

A 515287 DC3300 Dust Collector Fan

B 521683 Gilmer Belt

C 521682 Motor Drive Belt

instances

universals

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Inventory vs. Catalog:Two kinds of composite representational artifacts

Databases represent instances

Ontologies represent universals

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How do we know which general terms designate universals?

Roughly: terms used by scientists to designate entities about which we have a plurality of different kinds of testable proposition

(cell, electron ...)

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Problem: fiat demarcations

male over 30 years of age with family history of diabetes

abnormal curvature of spine

participant in trial #2030

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Problem: roles

fist

patient

FDA-approved drug

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Administrative ontologies often need to go beyond universals

Fall on stairs or ladders in water transport injuring occupant of small boat, unpowered

Railway accident involving collision with rolling stock and injuring pedal cyclist

Nontraffic accident involving motor-driven snow vehicle injuring pedestrian

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universals vs. classes

universals

{a,b,c,...} classes

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Class =defa maximal collection of particulars determined by a general term (‘cell’. ‘electron’), (‘ ‘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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Problem

The same general term can be used to refer both to universals and to collections of particulars. Consider:

HIV is an infectious retrovirus

HIV is spreading very rapidly through Asia

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universals

{c,d,e,...} classes

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Extension =def

The extension of a universal A is the class: instance of the universal A

(it is the class of A’s instances)

(the class of all entities to which the term ‘A’ applies)

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universals

defined classes

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universals

populations, ...

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Defined class =def

a class defined by a general term which does not designate a universal

the class of all diabetic patients in Leipzig on 4 June 1952

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OWL is a good representation of defined classes

• sibling of Finnish spy

• member of Abba aged > 50 years

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Terminology =def.

a representational artifact whose representational units are natural language terms (with IDs, synonyms, comments, etc.) which are intended to designate universals together with defined classes.

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universals, classes, concepts

universals

defined classes

‘concepts’

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universals < defined classes < ‘concepts’

‘concepts’ which do not correspond to defined classes:

‘Surgical or other procedure not carried out because of patient's decision’

‘Absent nipple’

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(Scientific) 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. universals in reality

2. those relations between these universals which obtain universally (= for all instances)

lung is_a anatomical structure

lobe of lung part_of lung

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Part II

How to Build an Ontology

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How to build an ontology

work with scientists to create an initial top-level classification

find ~50 most commonly used terms corresponding to universals in reality

arrange these terms into an informal is_a hierarchy according to this Universality principle

A is_a B every instance of A is an instance of B

fill in missing terms to give a complete hierarchy

(leave it to domain scientists to populate the lower levels of the hierarchy)

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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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MeSHMeSH 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

Use singular nouns

Terms in ontologies represent universals

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Goal: Each term in an ontology represents exactly one universal

there are universals also of collectivities:

population

complex of cells

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the use-mention confusion

Conceptual Entities =Def.

An organizational header for concepts representing mostly abstract entities.

swimming is healthy and has eight letters

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Principle

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 universals

3. confusion of concept and reality

Trialbank

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Plant Ontology

cell = def. plant cell, consisting of protoplast and cell wall; ...

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Principle

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

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

Supply definitions wherever possible

(both human-understandable natural language definitions, and equivalent formal definitions)

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Principle

Each term should have at most one definition*

*which may have both natural-language and formal versions

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The Problem of Circularity

A Person = def. A person with an identity document

cell = def. plant cell, consisting of protoplast and cell wall; ...

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Principle

Avoid circular definitions

(The term defined should not appear in its own definition)

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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

A definition should use terms which are easier to understand than the term defined

(HL7 creates a topsy turvy world, in which simple things are made difficult)

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Principle

Use Aristotelian definitions

An A is a B which C’s.

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Principle

Do not seek to define everything

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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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Rules for formatting terms• 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’ ...)

• Treat each term ‘A’ in an ontology is shorthand for a term of the form ‘the universal A’

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Univocity Terms should have the same meanings on

every occasion of use.

(They should refer to the same universals)

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 should include only those relational assertions which hold universally

pneumococcal virus causes pneumonia

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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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Universality

protocol-design earlier_than results analysis

but not

results analysis later_than protocol-design

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Positivity

Complements of universals are not themselves universals.

Terms such as non-mammal non-membrane other metalworker in New Zealand

do not designate universals in reality

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Ontology of universals logic of terms

There are no conjunctive and disjunctive universals:

anatomic structure, system, or substance

musculoskeletal and connective tissue disorder

rheumatism, excluding the back

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Objectivity

Which universals exist in reality is not a function of our knowledge.

Terms such as

unknown

unclassified

unlocalized

arthropathies not otherwise specified

do not designate universals in reality.

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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

Keep Sentences Separate from Terms

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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

carblue thing

blue car

is_a is_a

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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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thing

carblue thing

blue car

is_a1 is_a2

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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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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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Why do we need rules/standards for good ontology?

Ontologies must be intelligible both to humans (for annotation and curation) and to machines (for reasoning and error-checking): the lack of rules for classification leads to human error and blocks automatic reasoning and error-checking

Intuitive rules facilitate training of curators and annotators

Common rules allow alignment with other ontologies

ontologies are legends for cartoons

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Randomized controlled trialshttp://rctbank.ucsf.edu/ontology/outline/index.htm

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Top-Level Class Hierarchy for RCT

Root Secondary-study

Trial-details

Trial

Concept • Generic-concept • Population-concept • Protocol-concept • Design-concept • Outcome-concept • Administrative-concept • Intervention-concept

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Trial DetailsRoot

Secondary-study Trial-details

• Erratum • Publication-details • Trial-entry-details • Administrative-details

– Secondary-administrative-details – Primary-administrative-details

» Executed-administrative-details » Intended-administrative-details

• Conclusion-details • Background-details

– Intended-background-details – Executed-background-details

• Stopping-details • Retraction-details • Correction-details • Fraud-details

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Top-Most Class Hierarchy for RCT


Trial-details

Trial


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Concept • Generic-concept

– Term-information – Time-entity – Rule-concept – Situation

• Population-concept – Subgroup – Recruitment-flowchart – Population – Recruitment – Site-enrollment

• Protocol-concept – Follow-up-compliance – Follow-up-activity – Follow-up – Protocol-change – Treatment-assignment – Protocol – Reason – Outcomes-followup – Secondary-study-protocol

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Concept • Design-concept

– Survival-analysis-and-results – Statistical-analysis-and-results – Sample-size-calculation – Trial-design – Hypothesis-concept – Study-objective – Study-monitoring – Regression-analysis-and-results – Stopping-rule

• Outcome-concept – Special-variable-information – Outcome-assessment – Miscellaneous-outcome-entity – Result-entity – Outcome-value-entity – Outcome

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Concept • Administrative-concept

– Publication-concept – Study-site – Person – Ethics – Study-committee – Funder – Institution – Registry-id

• Intervention-concept – Blinding-concept – Compliance-details – Intervention-step – Intervention-arm – Co-intervention – Intervention – Compliance-result – Intervention-logic

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Trial-details

Trial


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What the top level should look like

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Two kinds of entities

occurrents (processes, events, happenings)

continuants (objects, qualities, states...)

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Continuants (aka endurants)have continuous existence in timepreserve their identity through changeexist in toto whenever they exist at all

Occurrents (aka processes)have temporal partsunfold themselves in successive phasesexist only in their phases

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You are a continuant

Your life is an occurrent

You are 3-dimensional

Your life is 4-dimensional

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Dependent entities

require independent continuants as their bearers

There is no run without a runner

There is no grin without a cat

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Dependent vs. independent continuants

Independent continuants (organisms, buildings, environments)

Dependent continuants (quality, shape, role, propensity, function, status, power, right)

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All occurrents are dependent entities

They are dependent on those independent continuants which are their participants (agents, patients, media ...)

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BFO Top-Level Ontology

ContinuantOccurrent

(always dependent on one or more

independent continuants)

IndependentContinuant

DependentContinuant

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= A representation of top-level types

Continuant Occurrent


DependentContinuant

cell component

biological process

molecular function

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Top-Level Ontology



DependentContinuant

Functioning

Side-Effect, Stochastic Process, ...

Function

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Top-Level Ontology



DependentContinuant

Functioning Side-Effect, Stochastic Process, ...

Function

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Top-Level Ontology



DependentContinuant

Quality Function Spatial Region

Functioning Side-Effect, Stochastic Process, ...

instances (in space and time)

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CTO will be part of OBI

Ontology of Biomedical Investigations

http://obi.sourceforge.net

which is in turn part of the OBO Foundry

http://obofoundry.org

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Trial-details

Trial


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Amended Top-Level Class Hierarchy for RCT

EntityContinuant

PopulationProtocolDesign

OccurrentTrial

Secondary-study Intervention

?? Trial-details ?? Outcome-concept ?? Administrative-concept

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Concept • Generic-concept

– Term-information – Time-entity – Rule-concept

» Clinical-rule

Exclusion-rule

Inclusion-rule » Rule-entity

Recursive-rule

Base-rule » Ethnicity-language-rule » Age-gender-rule » Situation

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Concept • Protocol-concept

– Follow-up-compliance – Follow-up-activity – Follow-up – Protocol-change – Treatment-assignment – Protocol – Reason – Outcomes-followup – Secondary-study-protocol

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EntityContinuant

Protocol• Secondary-study-protocol

Reason Occurrent

• Treatment-assignment • Follow-up

– Follow-up-activity – Outcomes-follow-up

• Protocol-change

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Concept • Population-concept

– Subgroup – Recruitment-flowchart – Population – Recruitment – Site-enrollment

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EntityContinuant

Protocol• Secondary-study-protocol

Recruitment-flowchart Reason Population

• Subgroup

Occurrent• Priors

– Recruitment– Site-enrollment – Treatment-assignment

• Follow-up – Follow-up-activity – Outcomes-follow-up

• Protocol-change

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Concept • Administrative-concept

– Publication-concept – Study-site – Person – Ethics – Study-committee – Funder – Institution – Registry-id

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Continuant• Information object

– Publication – Registry-ID

• Study-site • Person • Institution

– Study-committee – Funder

???Ethics

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Concept • Intervention-concept

– Blinding-concept – Compliance-details – Intervention-step – Intervention-arm – Co-intervention – Intervention – Compliance-result – Intervention-logic

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Occurrent• Intervention

– Blinding– Intervention-step – Intervention-arm – Co-intervention

• ??? Intervention-logic

• ??? Compliance-result

• ??? Compliance-details

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END

how to build an ontology

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