http://ontologist.com1 from knowledge representation to reality representation barry smith

http://ontologist.com 1

From Knowledge Representation to Reality Representation

Barry Smith


2002

Institute for Formal Ontology and Medical Information Science (Germany)

initially: work on formal ontology

and on ontology-based quality control in medical terminologies

(UMLS, SNOMED, NCI Thesaurus, etc.)


Fruit

Orange

VegetableSimilarTo

ApfelsineSynonymWith

NarrowerThan

Goble & Shadbolt

Problem: Associative approach to word meanings


both testes is_a testis

plant leaves is_a plant

menopause part_of death

bacterium causes experimental model of disease

not normal cell is_a cell

not abnormal cell is_a cell


move from associative relations between meanings to ontological

relations between the entities themselves

supplementing data mining approaches with1) better data2) better annotations3) better integration4) the possibility of strong logical reasoning


First crack in the wall

Digital Anatomist Foundational Model of Anatomy(Department of Biological Structure, University of Washington, Seattle)

Virtual Soldier Project: Reference Ontology of AnatomyReference Ontology of PhysiologyReference Ontology of Disease Pathways


Second Crack in the Wall

Gene Ontology Consortium

Open Biological Ontologies


NCOR: National Center for Ontological Research

Buffalo Center of Excellence in Bioinformatics)

Stanford Medical Informatics (Protégé 2000)

Berkeley Drosophila Genome Project (Model Organism Phenotype Ontology

Project)


NCOR: National Center for Ontological Research

plus industrial parners

Ontology Works...


NCOR Methodology

work with content developers to ensure rigorous conformity with good principles of classification and definition

use formally defined categories and relations to ensure interoperability and support automatic reasoning

and to move beyond mere statistical / associative techniques


Goal in Biomedical Informatics

use the methodology of formally defined relations and a common top-level ontology to bridge the granularity gap between genomics and proteomics data and phenotype (clinical, pharmacological, patient centered) data

From molecules to diseases


Examples of simple formal-ontological structures

is_a hierarchies

part_of hierarchies

dependence relations


A Window on Reality


Medical Diagnostic Hierarchy

a hierarchy in the realm of diseases


Dependence Relations

Organisms Diseases


A Window on Reality

Organisms Diseases


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


A Window on Reality


We can reason across such hierarchies and combinations

but only if the top-level categories and associated formal-ontological relations are well-defined and used consistently


Formal-Ontological Categories

object

process

site

layer

fragment

quality

function

relation

boundary

region


Formal-Ontological Relationsis_identical_to

is_a

part_of

develops_ from

derives_ from

located_at

depends_on

is_boundary_of

has_participant

has_agent

adjacent_to

contained_in

precedes

is_functioning_of

has_function

intends


To support integration of ontologies

relational expressions such as

is_a

part_of

...

should be used in the same way by all the ontologies to be integrated

NCOR goal


to define these relations properly

we need to take account of reality

If we remain in the realm of concepts we will forever face problems of interoperability


to define these relations properly

we need to take account not of concepts,

but of universals and instances in reality


Tom Gruber

“An ontology is a specification of a conceptualization”


The Concept Orientation

Work on biomedical ontologies grew out of work on medical dictionaries and thesauri

led to the assumption that all that need be said about concepts can be said without appeal to time or instances.

& fostered an impoverished regime of definitions


‘Concept’ in ontology runs together:

a) the meaning that is shared in common by a collection of synonymous terms

b) an idea shared in common in the minds of those who use synonymous terms (psycho-linguistic view)

c) a universal, feature or property shared by entities in the world which fall under the concept


Problem of evaluation

if an ontology is a mere “specification of a conceptualization,” then the distinction between good and bad ontologies loses its foothold in reality


There are more word meanings than there are types of entities in

reality

unicorn

devil

cancelled performance

avoided meeting

prevented pregnancy

imagined mammal ...


A is_a B =def.

‘A’ is more specific in meaning than ‘B’


unicorn is_a one-horned mammal

alien implant removal is_a surgical process

Chios energy healing is_a therapeutic process


This linguistic reading

yields a more or less coherent reading of relations like:

‘is_a’

‘synonymous_with’

‘associated_to’


but it fails miserably when it comes to relations of other types

part_of = def. composes, with one or more other physical units, some larger whole

contains =def. is the receptacle for fluids or other substances.


for how can concepts, on the linguistic reading, figure as relata of

relations like: part_of

adjacent_to

connected_to


connected_to =def. Directly attached to another physical unit as tendons are

connected to muscles.

How can a meaning or concept be directly attached to another physical unit as tendons are connected to muscles ?


is_a

human is_a mammal

all instances of the universal human are as a matter of necessity instances of the universal mammal


Evaluation

Good ontologies are those whose general terms correspond to universals in reality, and thereby also to corresponding instances.


Kinds of relations

<universal, universal>: is_a, part_of, ...

<instance, universal>: this explosion instance_of the universal explosion

<instance, instance>: Mary’s heart part_of Mary


Instance-level relations

part_of

is_located_at

has_participant

has_agent

earlier

. . .


part_of

For instances:part_of = instance-level parthood

(for example between Mary and her heart)

For universals:A part_of B =def. given any instance a of

A there is some instance b of B such that a part_of b


C

c at t c at t1

C1

transformation_of


transformation_of

fetus transformation_of embryo

adult transformation_of child

C2 transformation_of C1 =def. any instance

of C2 was at some earlier time an instance

of C1


derives_from

c derives_from c1

=def c and c1 are non-identical

and exist in continuous succession


the new component detaches itself from the initial component, which itself continues to exist

C c at t

C

c at t

C1

c1 at t1

c at t1

C1

c1 at t

the initial component ceases to exist with the formation of the new component


two initial components fuse to form a new component

C

c at t

C1

c1 at t1

C'

c' at t


Functions

your heart has the function: to pump blood

= your heart is predisposed (has the potential or casual power) to realize a process of the type pumping blood.

has_agent (instance-level relation)

p is_functioning_of c p has_agent c


Example: Spatially Coinciding Objects with thanks to Maureen Donnelly


Two entities coincide (partially) when they overlap (share parts)

my hand coincides with my body

the European Union coincides with the British Commonwealth

(United Kingdom … Malta, Cyprus)


Some entities coincide even though they share no parts

any material object coincides with its spatial region

a portion of food coincides with my stomach cavity


Holes may coincide with material objects

The hole in the chunk of amber coincides completely with, but does not overlap, the encapsulated insect which fills it

Sometimes holes and objects are moving independently (a bullet flying through a railway carriage moving through a tunnel)


Layers

layers

co-located objects

The region layer


Layered Ontology of Lakes

L1. a region layer

L2. a lake layer, consisting of a certain concave portion of the earth’s surface together with a body of water

L3. a fish layer

L4. a chemical contaminant layer


Layered Epidemiology Ontology

L1. a two-dimensional region layer in some undisclosed location

L2. a topographical layer, consisting of mountains, valleys, deserts, gullies

L3. a storm-system occupying sub-regions of L2

L4: an airborne cloud of smallpox virus particles.


Layered Mereology

= modified General Extensional Mereology (GEM)


Parthood (P)

Parthood is a partial ordering:

(P1) Pxx (reflexive)(P2) Pxy & Pyx x = y (antisymmetric)(P3) Pxy & Pyz Pxz (transitive)(P4) ~Pxy z(Pzx & ~Ozy) (the remainder principle: if x is not part of y,

then x has a part that does not overlap y)


layers

co-located objects

The region layer


The Region Function

r(x) = the region at which x is exactly located.

r is a new primitive

r maps (collapses) entities on all higher layers onto the region layer


Axioms for the region function, e.g.

(R3) Pxy P r(x)r(y)


Some Theorems

Ry r(y) = y

(every region is located at itself)

(x & x( Rx) &

y (Oyz x ( & Oyx))) Rz

(every sum of regions is a region)


Defined Relations

ECxy =: Cxy & ~ Oxy

(x and y are externally connected)

Axy =: EC(r(x), r(y))

(x and y abut)


Towards Dynamic Spatial Ontology

From spatial coincidence to spatio-temporal coincidence


Objects move through space

An adequate ontology of motion requires at least two independent sorts of spatial entities:

1. locations, which remain fixed,

2. objects, which move relative to them.


Standard (RCC) approaches

sparrow 152 moves from one location (region A) to another (region B)

Becomes:

each member of this continuous sequence of sparrow-shaped regions, starting with A and ending with B, has at successive times, rufous-winged (etc.) attributes.

Instead of talking about sparrows flying through the sky, we talk of mappings of the form:

Sparrow152: time regular closed subsets of R3.


Region-based approaches (RCC, etc.)

have no means of distinguishing true overlap (i.e. the sharing of parts) from mere spatial co-location.

They identify the relation of a fish to the lake it inhabits with the relation of a genuine part of a lake (a bay, an inlet) to the lake as a whole.

They identify the genuine parts of the human body, such as the heart or lungs, with foreign occupants such as parasites or shrapnel.


The solution

is to recognize both objects and locations, on separate layers

and then we need a theory of coincidence and of layered mereotopology to do justice to the entities in these two categories


Some entities coincide spatially even though they share no parts

a portion of food coincides with my stomach cavity at a certain time


Some entities coincide spatio-temporally even though they

share no parts

the course of a disease coincides with the treatment of the disease


Processes may coincide with each other

The manouvres of the coalition troops coincide, but do not share parts in common, with the activities of the terrorists


Spatiotemporal Coincidence without Sharing of Parts

The Great Plague of 1664 coincides with, but does not overlap, the history of Holland in the 17th century

A process of deforestation coincides with, but does not overlap, the history of the forest


Objects and processes do not coincide

For they are of different dimension:

Objects are 3-dimensional

Processes are 4-dimensional

Object-layers are always 3-dimensional

Process-layers are always 4-dimensional


Two ontologies of motion and change

series of samples, or snapshots

object x1 is at region r1 at time t1



SNAP ontologies (ontologies indexed by times)


t1


t2


t3


SNAP vs SPAN

Continuants vs Occurrents

(Sampling vs. Tracking)


SPAN ontology


SPAN ontology

is an ontology which recognizes processes, changes, themselves

= four-dimensional (spatio-temporal) entities

not via a sequence of instantaneous samplings but via extended observations


Many different interconnections traverse the SNAP-SPAN divide But SNAP and SPAN entities are never related by part_of, connected_to or coincidence (layer) relations


SNAP


SPAN

SPANEntity extended in time

Portion of Spacetime

Fiat part of process *First phase of a clinical trial

Spacetime worm of 3 + Tdimensions

occupied by life of organism

Temporal interval *projection of organism’s life

onto temporal dimension

Aggregate of processes *Clinical trial

Process[±Relational]

Circulation of blood,secretion of hormones,course of disease, life

Processual Entity[Exists in space and time, unfolds

in time phase by phase]

Temporal boundary ofprocess *

onset of disease, death


There are layers in both the SNAP (object) ontology and the

SPAN (process) ontology

In SNAP the region layer = space

In SPAN the region layer = spacetime


But

distinguishing layers in the process realm of SPAN is a matter of gerrymandering (of fiat carvings) to a much greater degree than in the realm of SNAP


One big difference between SNAP and SPAN

In SNAP, higher layers are categorially well-distinguished nicely separated (physical objects, holes, administrative entities …)

In SPAN

everything is flux


E N D E

http://ontologist.com1 from knowledge representation to reality representation barry smith

Documents

cell slide

reality slide

formal ontology

ontological relations

associative relations

common toplevel ontology

defined relations

better data