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Semantics for biodiversity

Semantics for Biodiversity

  • Barry Smith

  • http://ontology.buffalo.edu/smith


A brief history of the semantic web

A brief history of the Semantic Web

  • html demonstrated the power of the Web to allow sharing of information

  • can we use semantic technology to create a Web 2.0 which would allow algorithmic reasoning with online information based on XLM, RDF and above all OWL (Web Ontology Language)?

  • can we use RDF and OWL to break down silos, and create useful integration of on-line data and information


People tried but the more they were successful they more they failed

people tried, but the more they were successful, they more they failed

OWL breaks down data silos via controlled vocabularies for the description of data dictionaries

Unfortunately the very success of this approach led to the creation of multiple, new, semantic silos – because multiple ontologies are being created in ad hoc ways


Ontology success stories and some reasons for failure

Ontology success stories, and some reasons for failure

A fragment of the “Linked Open Data” in the biomedical domain


What you get with mappings

What you get with ‘mappings’

all phenotypes (excess hair loss, duck feet)


What you get with mappings1

What you get with ‘mappings’

HPO: all phenotypes (excess hair loss, duck feet ...)

NCIT: all organisms


What you get with mappings2

What you get with ‘mappings’

all phenotypes (excess hair loss, duck feet)

all organisms

allose (a form of sugar)


What you get with mappings3

What you get with ‘mappings’

all phenotypes (excess hair loss, duck feet)

all organisms

allose (a form of sugar)

Acute Lymphoblastic Leukemia (A.L.L.)


Mappings are hard

Mappings are hard

They are fragile, and expensive to maintain

Need a new authority to maintain, yielding new risk of forking

The goal should be to minimize the need for mappings

Invest resources in disjoint ontology modules which work well together – reduce need for mappings to minimum possible


Why should you care

Why should you care?

  • you need to create systems for data mining and text processing which will yield useful digitally coded output

  • if the codes you use are constantly in need of ad hoc repair huge resources will be wasted

  • relevant data will not be found

  • serious reasoning will be defeated from the start


How to do it right

How to do it right?

  • how create an incremental, evolutionary process, where what is good survives, and what is bad fails

  • where the number of ontologies needing to be linked is small

  • where links are stable

  • create a scenario in which people will find it profitable to reuse ontologies, terminologies and coding systems which have been tried and tested


Uses of ontology in pubmed abstracts

Uses of ‘ontology’ in PubMed abstracts


Semantics for biodiversity

By far the most successful: GO (Gene Ontology)


Go provides a controlled system of terms for use in annotating describing tagging data

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


Semantics for biodiversity

Hierarchical view representing relations between represented types


Semantics for biodiversity

Organ Part

Organ

Subdivision

Anatomical Space

Anatomical

Structure

Organ Cavity

Subdivision

Organ

Cavity

Organ

Organ

Component

Serous Sac

Tissue

Serous Sac

Cavity

Subdivision

Serous Sac

Cavity

is_a

Pleural Sac

Pleura(Wall

of Sac)

Pleural

Cavity

part_of

Parietal

Pleura

Visceral

Pleura

Interlobar

recess

Mediastinal

Pleura

Mesothelium

of Pleura


Reasons why go has been successful

Reasons why GO has been successful

  • It is a system for prospective standardization built with coherent top level but with content contributed and monitored by domain specialists

  • Based on community consensus

  • Updated every night

  • Clear versioning principles ensure backwards compatibility; prior annotations do not lose their value

  • Initially low-tech to encourage users, with movement to more powerful formal approaches (including OWL-DL – though GO community still recommending caution)


Go has learned the lessons of successful cooperation

GO has learned the lessons of successful cooperation

  • Clear documentation

  • Fully open source (allows thorough testing in manifold combinations with other ontologies)

  • Subjected to considerable third-party critique

  • Rapid turnaround tracker and help desk

  • Usable also for education

  • The terms chosen are already familiar


Semantics for biodiversity

natural language labels

to make the data cognitively

accessible to human beings


Go has been amazingly successful in overcoming the data balkanization problem

GO has been amazingly successful in overcoming the data balkanization problem

but it covers only generic biological entities of three sorts:

  • cellular components

  • molecular functions

  • biological processes

    and it does not provide representations of diseases, symptoms, …


Semantics for biodiversity

Original OBO Foundry ontologies

(Gene Ontology in yellow)


Semantics for biodiversity

environments are here

Environment Ontology


Semantics for biodiversity

http://obofoundry.org


Semantics for biodiversity

http://obofoundry.org


The obo foundry a step by step evidence based approach to expand the go

The OBO Foundry: a step-by-step, evidence-based approach to expand the GO

  • Developers commit to working to ensure that, for each domain, there is community convergence on a single ontology

  • and agree in advance to collaboratewith developers of ontologies in adjacent domains.

    http://obofoundry.org


Obo foundry principles

OBO Foundry Principles

  • Common governance (coordinating editors)

  • Common training

  • Common architecture

    • simple shared top level ontology (Basic Formal Ontology)

    • shared Relation Ontology: www.obofoundry.org/ro


Open biomedical ontologies foundry

Open Biomedical Ontologies Foundry

Seeks to create high quality, validated terminology modules across all of the life sciences which will be

  • close to language use of experts

  • evidence-based

  • incorporate a strategy for motivating potential developers and users

  • revisable as science advances

  • modularity: one ontology for each domain


Modularity

Modularity

  • ensures

    • annotations can be additive

    • no need for mappings

    • division of labor amongst domain experts

    • high value of training in any given module

    • lessons learned in one module can benefit work on other modules

    • incentivization of those responsible for individual modules


The modular approach

The Modular Approach

  • Create a small set of plug-and-play ontologies as stable monohierarchies with a high likelihood of being reused

  • Create ontologies incrementally

  • Reuse existing ontology resources

  • Use these ontologies incrementally in annotating heterogeneous data

  • Annotating = arms length approach; the data and data-models themselves remain as they are


Logical standards can be only part of the solution

Logical standards can be only part of the solution

OWL … bring benefits primarily on the side of syntax (language)

What we need are standards on the semantics (content) side (via top-level ontologies), including standards for

  • top-level ontologies

  • common relations (part_of …)

  • relation of lower-level ontologies to each other and to the higher levels


120 ontology projects using bfo

120+ ontology projects using BFO

http://www.ifomis.org/bfo/

  • Open Biomedical Ontologies Foundry

  • Ontology for General Medical Science

  • eagle-I, VIVO, CTSAconnect

  • AstraZeneca

  • Elsevier


How a common upper level ontology can help resist ontology chaos

How a common upper level ontology can help resist ontology chaos

  • something to teach

  • training (expertise) is portable

  • each new ontology you confront will be more easily understood at the level of content

    • and more easily criticized, error-checked

  • provides starting-point for domain-ontology development

  • provides platform for tool-building and innovations

    • lessons learned in building and using one ontology can potentially benefit other ontologies

    • promote shareability of data across discilinary and other boundaries


Semantics for biodiversity

Basic Formal Ontology (BFO)

top level

mid-level

domain level

OBO Foundry Modular Organization


Semantics for biodiversity

BFO

A simple top-level ontology to support information integration in scientific research

No overlap with domain ontologies (organism, person, society, information, …)

Based on realism

No abstracta

Tested in many natural science domains


Basic formal ontology

Basic Formal Ontology

Continuant

Occurrent

process, event

Independent

Continuant

entity

Dependent

Continuant

property

property depends

on bearer


Depends on

depends_on

Continuant

Occurrent

process, event

Independent

Continuant

thing

Dependent

Continuant

property

event depends

on participant


Basic formal ontology1

Basic Formal Ontology

continuant

occurrent

biological

processes

independent

continuant

cellular

component

dependent

continuant

molecular

function


Roles qualities

roles, qualities

Continuant

Occurrent

process, event

Independent

Continuant

Dependent

Continuant

Quality

Disposition


Instance of

instance_of

types

Continuant

Occurrent

process, event

Independent

Continuant

thing

Dependent

Continuant

property

.... ..... .......

instances


Semantics for biodiversity

RELATION TO TIME

GRANULARITY

rationale of OBO Foundry coverage


Example the cell ontology

Example: The Cell Ontology


Example ontology for general medical science

Example: Ontology for General Medical Science


Semantics for biodiversity

http://code.google.com/p/ogms/


Semantics for biodiversity

coronary heart disease

in nature, no sharp boundaries here

CHD in phase of early lesions and small fibrous plaques

CHD in phase of asymptomatic (‘silent’) infarction

CHD in phase of surface disruption of plaque

unstable angina

stable angina

instantiates at t1

instantiates at t2

instantiates at t3

instantiates at t4

instantiates at t5

John’s coronary heart disease


Semantics for biodiversity

human

in nature, no sharp boundaries here

embryo

fetus

neonate

infant

child

adult

instantiates at t1

instantiates at t2

instantiates at t3

instantiates at t4

instantiates at t5

instantiates at t6

John


Semantics for biodiversity

A disease is a disposition

produces

bears

realized_in

etiological process

disorder

disposition

pathological process

produces

diagnosis

interpretive process

signs & symptoms

abnormal bodily features

produces

used_in

recognized_as


Cirrhosis environmental exposure

Cirrhosis - environmental exposure

  • Symptoms & Signs

    • used_in

  • Interpretive process

    • produces

  • Hypothesis - rule out cirrhosis

    • suggests

  • Laboratory tests

    • produces

  • Test results - elevated liver enzymes in serum

    • used_in

  • Interpretive process

    • produces

  • Result - diagnosis that patient X has a disorder that bears the disease cirrhosis

  • Etiological process - phenobarbitol-induced hepatic cell death

    • produces

  • Disorder - necrotic liver

    • bears

  • Disposition (disease) - cirrhosis

    • realized_in

  • Pathological process - abnormal tissue repair with cell proliferation and fibrosis that exceed a certain threshold; hypoxia-induced cell death

    • produces

  • Abnormal bodily features

    • recognized_as

  • Symptoms - fatigue, anorexia

  • Signs - jaundice, splenomegaly


Dispositions and predispositions

Dispositions and Predispositions

Some dispositions are predispositions to other dispositions.


Hnpcc genetic pre disposition

HNPCC - genetic pre-disposition

  • Etiological process - inheritance of a mutant mismatch repair gene

    • produces

  • Disorder - chromosome 3 with abnormal hMLH1

    • bears

  • Disposition (disease) - Lynch syndrome

    • realized_in

  • Pathological process - abnormal repair of DNA mismatches

    • produces

  • Disorder - mutations in proto-oncogenes and tumor suppressor genes with microsatellite repeats (e.g. TGF-beta R2)

    • bears

  • Disposition (disease) - non-polyposis colon cancer

    • realized in

  • Symptoms (including pain)


Ontology modules extending of ogms

Ontology modules extending of OGMS

Sleep Domain Ontology (SDO)

Ontology of Medically Relevant Social Entities (OMRSE)

Vital Sign Ontology (VSO)

Mental Disease Ontology (MD)

Neurological Disease Ontology (ND)

Infectious Disease Ontology (IDO)


Infectious disease ontology ido

Infectious Disease Ontology (IDO)

  • IDO Core:

    • General terms in the ID domain.

    • A hub for all IDO extensions.

  • IDO Extensions:

    • Disease specific.

    • Developed by subject matter experts.

  • Provides:

    • Clear, precise, and consistent natural language definitions

    • Computable logical representations (OWL, OBO)


  • How ido evolves

    How IDO evolves

    IDOMAL

    IDOCore

    IDOHIV

    CORE and

    SPOKES:

    Domain

    ontologies

    IDOFLU

    IDORatSa

    IDORatStrep

    IDOStrep

    IDOSa

    SEMI-LATTICE:

    By subject matter experts in different communities of interest.

    IDOMRSa

    IDOAntibioticResistant

    IDOHumanSa

    IDOHumanStrep

    IDOHumanBacterial


    Ido core

    IDO Core

    • Contains general terms in the ID domain:

      • E.g., ‘colonization’, ‘pathogen’, ‘infection’

    • A contract between IDO extension ontologies and the datasets that use them.

    • Intended to represent information along several dimensions:

      • biological scale (gene, cell, organ, organism, population)

      • discipline (clinical, immunological, microbiological)

      • organisms involved (host, pathogen, and vector types)


    Sample ido definitions

    Sample IDO Definitions

    • Host of Infectious Agent (BFO Role): A role borne by an organism in virtue of the fact that its extended organism contains an infectious agent.

    • Extended Organism (OGMS):An object aggregate consisting of an organism and all material entities located within the organism, overlapping the organism, or occupying sites formed in part by the organism.

    • Infectious Agent:A pathogen whose pathogenic disposition is an infectious disposition.


    Ido and ido sa

    IDO and IDOSa

    • Scale of the infection (disorder)

    from Shetty, Tang, and Andrews, 2009


    Semantics for biodiversity

    Differentiated by:

    Staphylococcus aureus (Sa)

    {

    MSSa

    MRSa

    Antibiotic Resistance

    {

    HA-MRSa

    CA-MRSa

    Pathogenesis Location Type

    {

    Geographic Region

    UK CA-MRSa

    Australian

    CA-MRSa

    {

    Various Differentia

    Specific Strains


    Sample application a lattice of infectious disease application ontologies from narsa isolate data

    Sample Application: A lattice of infectious disease application ontologies from NARSA isolate data

    Network on Antimicrobial Resistance in Staphylococcus aureus

    • http://www.narsa.net/content/staphLinks.jsp

      True personalized medicine – YourDiseaseOntology


    Ways of differentiating staphylococcus aureus infectious diseases

    Ways of differentiating Staphylococcus aureus infectious diseases

    • Infectious Disease

      • By host type

      • By (sub-)species of pathogen

      • By antibiotic resistance

      • By anatomical site of infection

    • Bacterial Infectious Disease

      • By PFGE (Strain)

      • By MLST (Sequence Type)

      • By BURST (Clonal Complex)

    • Sa Infectious Disease

      • By SCCmec type

        • By ccr type

        • By mec class

      • spa type

    International Working Group on the Staphylococcal Cassette Chromosome elements


    Nrs701 s resistance to clindamycin

    NRS701’s resistance to clindamycin

    ido.owl

    narsa.owl

    ndf-rt

    narsa-isolates.owl


    Plant ido

    Plant IDO

    • Virulence

    • Resistance

    • Symbiont


    Bfo the very top

    BFO: The Very Top

    continuant

    occurrent

    independent

    continuant

    dependent

    continuant

    quality

    function

    role

    disposition


    Basic formal ontology2

    Basic Formal Ontology

    types

    Continuant

    Occurrent

    process, event

    Independent

    Continuant

    thing

    Dependent

    Continuant

    quality

    .... ..... .......

    instances


    Basi s of bfo in go

    Basisof BFO in GO

    Continuant

    Occurrent

    biological

    process

    Independent

    Continuant

    cellular

    component

    Dependent

    Continuant

    molecular

    function

    ..... ..... ........


    How a common upper level ontology can help resist ontology chaos1

    How a common upper level ontology can help resist ontology chaos

    • something to teach

    • training (expertise) is portable

    • each new ontology you confront will be more easily understood at the level of content

      • and more easily criticized, error-checked

    • provides starting-point for domain-ontology development

    • provides platform for tool-building and innovations

      • lessons learned in building and using one ontology can potentially benefit other ontologies

      • promote shareability of data across discilinary and other boundaries


    Entity def

    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)


    First basic distinction among entities

    First basic distinction among entities

    • type vs. instance

    • (science text vs. diary)

    • (human being vs. Tom Cruise)


    For ontologies

    For ontologies

    • it is generalizations that are important = types, types, kinds, species


    Semantics for biodiversity

    Catalog vs. inventory


    Catalog vs inventory

    Catalog vs. inventory


    Catalog of types types

    Catalog of types/Types


    Semantics for biodiversity

    types vs. instances


    Names of instances

    names of instances


    Names of types

    names of types


    An ontology is a representation of types

    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


    Types

    object

    organism

    animal

    cat

    siamese

    types

    mammal

    frog

    instances


    Semantics for biodiversity

    Organ Part

    Organ

    Subdivision

    Anatomical Space

    Anatomical

    Structure

    Organ Cavity

    Subdivision

    Organ

    Cavity

    Organ

    Organ

    Component

    Serous Sac

    Tissue

    Serous Sac

    Cavity

    Subdivision

    Serous Sac

    Cavity

    is_a

    Pleural Sac

    Pleura(Wall

    of Sac)

    Pleural

    Cavity

    part_of

    Parietal

    Pleura

    Visceral

    Pleura

    Interlobar

    recess

    Mediastinal

    Pleura

    Mesothelium

    of Pleura


    3 kinds of binary relations

    3 kinds of (binary) relations

    • Between types

      • human is_a mammal

      • human heart part_ofhuman

    • Between an instance and a type

      • this human instance_of the type human

      • this human allergic_to the type tamiflu

    • Between instances

      • Mary’s heart part_of Mary

      • Mary’s aorta connected_to Mary’s heart


    Type level relations presuppose the underlying instance level relations

    Type-level relations presuppose the underlying instance-level relations

    • A is_a B =def. A and B are types and all instances of A are instances of B

    • A part_of B =def. All instances of A are instance-level-parts-of some instance of B


    The assertions linking terms in ontologies must hold universally

    The assertions linking terms in ontologies must hold universally

    Hence all type-level relations in RO are provided with

    All-Some definitions

    If you know A part_of B, and B part_of C then 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


    Semantics for biodiversity

    part_of

    for continuant classes is time-indexed

    A part_of B =def.

    given any particular a and any time t,

    if a is an instanceof A at t,

    then there is some instance b of B

    such that

    a is an instance-level part_ofb at t


    Derives from ovum sperm zygote

    instances

    derives_from (ovum, sperm  zygote ... )

    C1

    c1att1

    C

    c att

    time

    C'

    c' att


    Transformation of

    same instance

    C1

    C

    c att

    c att1

    time

    transformation_of

    pre-RNA  mature RNAchild  adult


    Transformation of1

    transformation_of

    C2 transformation_of C1 =def. any instance of C2 was at some earlier time an instance of C1


    Semantics for biodiversity

    C1

    C

    c att

    c att1

    embryological development


    Tumor development

    tumor development

    C1

    C

    c att

    c att1


    The granularity gulf

    The Granularity Gulf

    most existing data-sources are of fixed, single granularity

    many (all?) clinical phenomena cross granularities


    Transformation of2

    C1

    C

    c att

    c att1

    transformation_of


    Universality

    universality

    • Often, order will matter:

    • We can assert

    • adult transformation_of child

    • but not

    • child transforms_into adult


    Representation def

    Representation =def

    • an image, idea, map, picture, name or description ... of some entity or entities.

    • Ontologies are structured representations of the types in a certain domain of reality


    Ontologies are here

    Ontologies are here


    Or here

    or here


    Ontologies represent general structures in reality leg

    Ontologies represent general structures in reality (leg)


    Ontologies do not represent concepts in people s heads

    Ontologies do not represent concepts in people’s heads


    They represent types in reality

    They represent types in reality


    Semantics for biodiversity

    instances

    types


    Inventory vs catalog two kinds of representational artifact

    Inventory vs. Catalog:Two kinds of representational artifact

    • Databases represent instances

    • Ontologies represent types


    How do we know which general terms designate types

    How do we know which general terms designate types?

    • Types are repeatables:

    • cell, electron, weapon, mouse ...

    • Instances are one-off: Bill Clinton, this mouse …


    Problem

    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


    Class def

    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


    Types vs their extensions

    types vs. their extensions

    types

    {a,b,c,...} collections of particulars


    Extension

    Extension

    • =def The extension of a type is the class of its instances


    Types vs classes

    types vs. classes

    types

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


    Types vs classes1

    types vs. classes

    types

    extensions other sorts of classes


    Types vs classes2

    types vs. classes

    types

    populations, ...

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


    Owl is a good representation of classes

    OWL is a good representation of classes

    • F16s

    • sibling of Finnish spy

    • member of Abba aged > 50 years


    Types classes concepts

    ?

    types, classes, concepts

    types

    classes

    ‘concepts’


    Types classes concepts1

    types < classes < ‘concepts’

    • Cases of ‘concepts’ which do not correspond to classes:

    • ‘Cancelled manoeuvre’

    • ‘Planned manoeuvre’

    • ‘Fake terrorist’

    • Such terms do not represent anything

    • See Information Artifact Ontology (IAO)


    Ontology def

    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


    Bfo top level ontology

    BFO Top-Level Ontology

    Continuant

    Occurrent

    (always dependent

    on one or more

    independent

    continuants)

    Independent

    Continuant

    Dependent

    Continuant


    Two kinds of entities

    Two kinds of entities

    • occurrents (processes, events, happenings)

    • continuants (objects, qualities, states...)


    Semantics for biodiversity

    • Continuants (aka endurants)

      • have continuous existence in time

      • preserve their identity through change

      • exist in toto whenever they exist at all

    • Occurrents (aka processes)

      • have temporal parts

      • unfold themselves in successive phases

      • exist only in their phases


    You are a continuant

    You are a continuant

    • Your life is an occurrent

    • You are 3-dimensional

    • Your life is 4-dimensional


    Dependent entities

    Dependent entities

    • require independent continuants as their bearers

    • There is no run without a runner

    • There is no grin without a cat


    Dependent vs independent continuants

    Dependent vs. independent continuants

    • Independent continuants (organisms, buildings, environments)

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


    All occurrents are dependent entities

    All occurrents are dependent entities

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


    Bfo top level ontology1

    BFO Top-Level Ontology

    Continuant

    Occurrent

    (always dependent

    on one or more

    independent

    continuants)

    Independent

    Continuant

    Dependent

    Continuant


    Obo foundry organized in terms of basic formal ontology

    OBO Foundry organized in terms of Basic Formal Ontology

    Each Foundry ontology can be seen as an extension of a single upper level ontology (BFO)

    either post hoc, as in the case of the GO

    or in virtue of creation ab initio via downward population from BFO


    How to build an ontology

    How to build an ontology

    • import BFO into ontology editor

    • work with domain experts to create an initial mid-level classification

    • find ~50 most commonly used terms corresponding to types 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 experts to populate the lower levels of the hierarchy)


    Example the cell ontology1

    Example: The Cell Ontology


    Semantics for biodiversity

    IAO:measurement datum

    is_about

    concretized_by

    quality: John’s blood glucose level

    has_specified

    _output

    quality: ‘120 mg/dL’-shaped pattern

    inheres_in

    OBI process:

    this specific assay

    portion of blood

    participates_in

    inheres_in

    derived_from

    participates_in

    device

    screen

    part_of

    John

    Numerical Value Example


    Quality of portion of blood

    Quality of portion of blood

    elements of an ontological analysis:

    • the portion of blood (material entity)

    • the blood sugar level (quality) referred to by means of

    • an expression (information artifact, thus a BFO:generically dependent continuant) ‘100 mg/dL’.


    Bfo 2 0

    BFO 2.0


    Bfo 2 01

    BFO 2.0


    Semantics for biodiversity

    IAO:measurement datum

    is_about

    concretized_by

    process: John’s heart beating

    has_specified

    _output

    quality: ‘120 bpm’-shaped pattern

    has_participant

    OBI process:

    this specific assay

    has_participant

    inheres_in

    device

    screen

    has_part

    has_participant

    Beat Measurement

    John


    Process measurement

    Process measurement

    heart beating at constant rate, elements of an ontological analysis:

    • the heart (object)

    • the process of beating

    • the temporal region occupied by this process

    • the spatiotemporal region that is occupied by this process (trajectory of the beating process)

    • the rate, referred to by means of

    • an expression (information artifact, thus a BFO:generically dependent continuant) such as ‘63 beats/minute’.


    Semantics for biodiversity

    IAO:measurement datum

    is_about

    concretized_by

    process: John’s heart beating

    has_specified

    _output

    quality: ‘120 bpm’-shaped pattern

    has_participant

    measurement process:

    this specific assay

    has_participant

    inheres_in

    screen

    device

    has_part

    John

    has_participant

    Beat Measurement


    The information artifact ontology

    The Information Artifact Ontology

    • credit card numbers are not integers

    • names are not strings

    • serial numbers are not strings

  • Rather, they are artifacts, human creations.

  • If my Social Security Number is the same integer as your Credit Card Number, they are still different Numbers

  • If my name is the same string as your name, they are still different names


  • Information artifacts in science

    Information Artifacts in Science

    protocol

    database

    theory

    ontology

    gene list

    publication

    result

    ...


    Information entity labeling

    Information Entity (labeling)

    serial number

    batch number

    grant number

    person number

    name

    address

    email address

    URL

    ...


    Http code google com p information artifact ontology

    http://code.google.com/p/information-artifact-ontology/


    What is a datum

    What is a datum?

    Continuant

    Occurrent

    process

    Independent

    Continuant

    laptop, book

    Dependent

    Continuant

    quality

    datum: a pattern in some medium with a certain kind of provenance

    .... ..... .......


    Type or instance

    type or instance

    Continuant

    Occurrent

    (Process)

    Independent

    Continuant

    human being,

    protocol

    document

    Dependent

    Continuant

    pattern of

    ink marks

    Applying

    the protocol

    Side-Effect …

    ...... ..... .... .....


    Semantics for biodiversity

    Continuant

    Occurrent

    Independent

    Continuant

    Dependent

    Continuant

    Action

    creating a datum

    Information

    Entity

    .... ..... .......


    Types and instances

    types and instances

    Type: human being

    Instance: Leon Tolstoy

    Type: novel

    Instance: War and Peace

    Type: book

    Instance: this copy of War and Peace


    What is a work of literature

    What is a work of literature?

    • Is War and Peace a type or an instance?

    • If War and Peace were a type, and the copies of War and Peace in my library and in your library were instances, then

    • there would be many War(s) and Peaces.

    • Hence War and Peace is an instance.


    There are not two declarations of independence

    There are not two Declarations of Independence

    There can be two copies of the Declaration of Independence

    There cannot be two Declarations of Independence


    Syntactic rule of thumb for types

    Syntactic rule of thumb for types

    Their names are pluralizable

    There can be three people

    There cannot be three Condoleezza Rices

    • Information Entities = entities which can exist in many perfect copies

    • Your genome is an information entity, but not an information artifact


    Specific dependence

    Specific dependence

    Continuant

    Occurrent

    process

    Independent

    Continuant

    thing

    Dependent

    Continuant

    quality

    headache depends

    on human being

    .... ..... .......


    Generically dependent continuants

    Generically Dependent Continuants

    Generically

    Dependent

    Continuant

    if one bearer ceases to exist, then the entity can survive, because there are other bearers (copyability)

    the pdf file on my laptop

    the DNA (sequence) in this chromosome

    Information

    Entity

    Sequence


    Generically dependent continuants1

    Generically dependent continuants

    • are realized through being concretized in specifically dependent continuants

    • (the plan in your head, the protocol being realized by your research team)


    Types vs generically dependent continuants

    Types vs. generically dependent continuants

    types have subtypes (kinds): if you can have a kind of something, then it’s a type

    you can’t have a kind of Bill Clinton

    you can’t have a kind of The Constitution of the United States


    Generically dependent continuants2

    Generically Dependent Continuants

    Generically

    Dependent

    Continuant

    Sequence

    Information

    Entity

    .pdf file

    .doc file

    instances


    Generically dependent continuants3

    Generically dependent continuants

    • are concretized in specifically dependent continuants

    • Beethoven’s 9th Symphony is concretized in the pattern of ink marks which make up this score in my hand


    Generically dependent continuants4

    Generically dependent continuants

    • do not require specific media (paper, silicon, neuron …)


    Realizable dependent continuants

    Realizable Dependent Continuants

    Specifically

    Dependent

    Continuant

    Quality, Pattern

    Realizable

    Dependent

    Continuant

    Occurrent

    inert ert


    Examples

    Examples

    performance of a symphony

    projection of a film

    utterance of a sentence

    application of a therapy

    course of a disease

    increase of temperature

    Occurrent

    Realizable

    Dependent

    Continuant


    Semantics for biodiversity

    Entity

    is_a

    is_a

    is_a

    Information Content Entity

    Property

    Key:

    Ontology Elements

    Relations

    Data Elements

    Geospatial Entity

    is_a

    is_a

    Designative Information Content Entity

    Physical Property

    is_a

    is_a

    Road Intersection

    is_a

    has_role

    has_property

    Physical Location

    Geospatial Reference Point

    Ontology

    Data Model Elements

    denotes

    denotes

    denotes

    denotes

    denotes

    String: Amazai and

    Nawagai Sura Road

    Intersection

    Lat: 34.40393540678018

    Long: 72.50272750854492

    TRP: AB 001

    WPT: EZ497

    MGRS: TF 4679 5792


    Bfo role

    BFO: role

    • a realizable dependent continuant that is not the consequence of the nature of the independent continuant entity which bears the role (contrast: disposition)

    • the role is optional (someone else assigns it, the entity acquires it by moving it into a specific context)

    • roles often come in pairs (husband/wife)


    Semantics for biodiversity

    Continuant

    Occurrent

    Generically

    Dependent

    Continuant

    Specifically

    Dependent

    Continuant

    Independent

    Continuant

    Realizable

    Dependent

    Continuant

    Realization

    Quality

    Disposition

    Role


    Roles

    Roles

    • standard examples: nurse, student, patient;

    • in each case something holds (that a person plays a role) because of some socially vehiculated decision. Functions never exist purely because people decide that they exist; this is because functions rest in each case on some underlying physical structure with relevant causal powers.


    Principle of low hanging fruit

    Principle of Low Hanging Fruit

    • Include even absolutely trivial assertions (assertions you know to be universally true)

    • pneumococcal bacterium is_a bacterium

    • Computers need to be led by the hand


    Principle of singular nouns

    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


    Count vs mass nouns

    Count vs. mass nouns

    • Count

    • suitcase

    • cow

    • datum

    • Mass

    • luggage

    • beef

    • information


    Principle avoid mass nouns

    Principle: Avoid mass nouns

    • Brenda Tissue Ontology

    • blood is_a hematopoietic system

    • hematopoietic system is_a whole body

    • whole_body is_a animal


    Principle of definitions

    Principle of definitions

    • Supply definitions for every term

    • human-understandable natural language definition

    • an equivalent formal definition


    Principle definitions must be unique

    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)


    The problem of circularity

    The Problem of Circularity

    • A Person =def. A person with an identity document

    • Hemolysis =def. The causes of hemolysis


    Principle of non circularity

    Principle of non-circularity

    • The term defined should not appear in its own definition


    Principle of increase in understandability

    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


    Principle of acknowledging primitives

    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


    Principle of aristotelian definitions

    Principle of Aristotelian definitions

    • Use two-part definitions

    • An A is a B which C’s.

    • A human being is an animal which is rational

    • Here A is the child term, B is its immediate parent in the ontology is_a hierarchy


    Rules for formulating terms

    Rules for formulating 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 type A’


    Universality1

    universality

    • Often, order will matter:

    • We can assert

    • adult transformation_of child

    • but not

    • child transforms_into adult


    Universality2

    universality

    • viral pneumonia caused by virus

    • but not

    • virus causes pneumonia

    • pneumococcal virus causes pneumonia


    Principle of universality

    Principle of Universality

    • results analysis later_than protocol-design

    • but not

    • protocol-design earlier_than results analysis


    Principle of positivity

    Principle of positivity

    • Complements of types are not themselves types.

    • Terms such as

    • non-mammal

    • non-membrane

    • other metalworker in New Zealand

    • do not designate types in reality


    Generalized anti boolean principle

    Generalized Anti-Boolean Principle

    • There are no conjunctive and disjunctive types:

    • anatomic structure, system, or substance

    • musculoskeletal and connective tissue disorder


    Objectivity

    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.


    Keep epistemology separate from ontology

    Keep Epistemology Separate from Ontology

    • If you want to say that

    • We do not know where A’sare 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)


    Keep sentences separate from terms

    Keep Sentences Separate from Terms

    • 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


    Principle do not commit the use mention confusion

    Principle: do not commit the use-mention confusion

    • mouse =def. common name for the species mus musculus


    Principle do not commit the use mention confusion1

    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


    Species

    Species

    • species = reproductively isolated units that persist as continuants over time.

    • (one problem area: bacteria, noclear "reproductive isolation" and horizontal gene transfer.)


    Semantics for biodiversity

    • Core and Extensions

    • IDO

    • GBIF -- Germ Plasm Repository Extension of Darwin Core


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