VT
Realism, Concepts and
Categories
Or: how realism can be
pragmatically useful for
information systems
Barry Smith
Conceptual Spaces
Great book
Theory of conceptual spaces can be applied
not only to concepts
but also to the universals in reality
(concepts of shell shapes
+ invariants among shell shapes
themselves)
PG: I’M A CONCEPTUALIST
There is a reality (an unknowable Ding an
sich)
But I am concerned with perceptions, our
experiences
The concepts are in our heads
There may be things out there but they are
not very useful for us  pragmatic
standpoint
PG: I’M A CONCEPTUALIST
The concepts are in our heads
There may be universals out there
corresponding to our concepts, but they
are not very useful for us  pragmatic
standpoint
Do we need realism to solve practical
problems in the theory of conceptual
spaces?
PG: I’M A CONCEPTUALIST
The concepts are in our heads
There may be universals out there
corresponding to our concepts, but they
are not very useful for us  pragmatic
standpoint
Do we need realism to solve practical
problems in the theory of conceptual
spaces?
Experiments are a good way to
come into contact with reality
Thus it is significant that in describing his
experiment with shells PG distinguished
between the space of shells in reality and
the conceptual space of shells
Experiments are a good way to
come into contact with reality
But in appealing to experiments in general (by
other cognitive scientists) Peter is assuming that
these scientists, too, are able to apprehend
invariants in reality
-- in children’s responses to stimuli
-- in language uses
-- in shapes and patterns in stimuli themselves
(for example pictures of cups, jugs, bowls)
In talking about pigeons,
And in giving an argument for his own
doctrine of conceptualism, Peter
presupposed a distinction between the
universals green and mixture-of-blue-andyellow out there in the world
And also a distinction between pigeons and
humans
Therefore Peter is a realist
Peter’s response:
I am just pretending to believe that there is
such a mind-independent distinction
BUT THEN THIS DEPRIVES HIS
ARGUMENT OF ALL RHETORICAL
FORCE
Peter’s second response:
I believe that there is such a mindindependent distinction only when I’m
arguing for this point in my theory, but
when I’m arguing for other points in my
theory I don’t believe it any more
Indeed I deny it, because I want to remain
faithful to my conceptualism
PG: I’M A CONCEPTUALIST
But he needs realism about universals to
defend his argument
Do we need universals for other (pragmatic)
reasons?
Do we need realism to solve practical
problems?
Medicine
Medical learning
What medical students know
What doctors know
-- highly multi-dimensional concept spaces
What a typical patient knows
-- a lower-dimensional concept space
Medicine
What a doctor knows
-- a multi-dimensional concept space
What there is to be known
-- many medical phenomena we just can’t
explain
-- an even more highly multi-dimensional
invariant space
A practical medical problem
How to choose a doctor?
What a doctor knows
vs.
What there is to be known
-- many medical phenomena we just can’t
explain
PG: I’M A CONCEPTUALIST
But he needs realism to defend his
argument
Do we need realism or not to solve practical
problems in information systems?
PG Axiom: Concept systems have
to be learnable
Therefore there is an upper limit on the
number of dimensions they can have, and
on the topography by which these
dimensions are organized
Universal Medical Language
System
5 million words
1.3 million concepts
divided into 135 dimensions corresponding to the
UMLS system of semantic types
Computers mean that we can break out of the
restraints of learnability
And then we do not break out at random – rather
we break out in reflection of the invariants we
find in the reality studied by medical scientists
Ontology
like cartography
must work with maps at different scales
How fit these maps (conceptual grids)
together into a single system?
Consider them
as grids
transparent to reality
allowing our directedness towards
objects beyond
Cartographic Projection
intentionality = the directedness
towards objects via conceptual grids
object
conceptual grids treated
always only as mediators
towards objects in reality
intentionality = the directedness
towards concepts
concepts
intentionality = the directedness
towards objects via conceptual grids
object
conceptual grids treated
always only as mediators
towards objects in reality
Intentional directedness
… is effected via conceptual grids
we are able to reach out to the objects
themselves because our conceptual grids
are transparent
Kantianism
= the inability to appreciate the fact that our
conceptual grids can be transparent to
reality beyond
= Midas touch epistemology
there are many compatible
map-like partitions
at different scales,
which are all transparent
to the reality beyond
animal
Universe/Periodic Table
bird
canary
ostrich
ontology of DNA space
fish
ontology of
biological species
animal
Universe/Periodic Table
bird
fish
canary
ostrich
both are transparent
partitions of one and the
same reality
Ontological Zooming
The job of the ontologist
is to understand how different partitions of
the same reality interrelate
Back to our practical problem
Why Neokantianism makes for
bad information systems
ontologies
IFOMIS
Institute for Formal Ontology
and Medical Information Science
http://ifomis.de
The problem
Different communities of medical
researchers use different and often
incompatible category systems in
expressing the results of their work
Example: Medical Nomenclature
UMLS:
blood is a tissue
MeSH:
blood is a body fluid
different concept systems
need not interconnect at all
for example they may relate to
entities of different granularity
we cannot make incompatible
terminology-systems interconnect
just by looking at concepts,
or knowledge or language
to decide which of a plurality of
competing definitions to accept
we need some tertium quid
we need, in other words,
to take the world itself into account
For medical students: patients are the
solution
In information systems ontology is the
solution’
Two alternative readings
Ontologies are special sorts of terminology
systems = currently popular IT conception,
with roots in KR
Ontologies are special sorts of theories about
entities in reality = traditional philosophical
conception, embraced by IFOMIS
Example: The Gene Ontology (GO)
hormone ; GO:0005179
%digestive hormone ; GO:0046659
%peptide hormone ; GO:0005180
%adrenocorticotropin ; GO:0017043
%glycopeptide hormone ; GO:0005181
%follicle-stimulating hormone ; GO:0016913
% = subsumption (lower term is_a higher term)
as tree
hormone
digestive hormone
adrenocorticotropin
peptide hormone
glycopeptide hormone
follicle-stimulating hormone
GO
is very useful for purposes of
standardization in the reporting of genetic
information
but it is not much more than a telephone
directory of standardized designations
organized into hierarchies
GO
can in practice be used only by trained
biologists
whether a GO-term stands in the
subsumption relationship depends on the
context in which the term is used
(for example on the type of organism)
A still more important problem:
GDB
Genome Database of Human Genome
Project
GenBank
National Center for Biotechnology
Information, Washington DC
etc.
What is a gene?
GDB: a gene is a DNA fragment that can be
transcribed and translated into a protein
GenBank: a gene is a DNA region of
biological interest with a name and that
carries a genetic trait or phenotype
GO uses ‘gene’ in its term hierarchy,
but it does not tell us which of these
definitions is correct
GO
has no robust formal organization
no capability to be aligned with systems
which would have the power to use it to
reason with genetic information
GO deals with basic ontological
notions very haphazardly
GO’s three main term-hierarchies are:
component, function and process
But GO confuses functions with structures,
and also with executions of functions
and has no clear account of the relation
between functions and processes
BFO vs. KR
In the knowledge engineering world in which
information systems ontology has its home
terms and concepts come first,
– the job is to validate them by building working
programs
In the BFO world robust ontology (with all its
reasoning power) comes first
and terms and term-hierarchies must be
subjected to the constraints of ontological
coherence
IFOMIS:
Get basic ontological organization right
and problems of formalization (consistency,
portability) will become easier to solve
later
Current orthodoxy
focuses instead on issues of
representation (XML)
and reasoning (Description logics)
Description logics
decidable logics, thus expressively weaker
than first-order predicate logic
used for ensuring consistency of definitions
of terms and for computing relations of
subsumption
ontologically neutral
(i.e. neutral as between good ontology and
ontological nonsense)
Concept hierarchy ontology:
Ontologies are inside the computer
thus subject to severe constraints on
expressive power
(effectively the expressive power of
description logic)
Concept hierarchy ontology cannot
solve the data-integration problem
because of its roots in knowledge
representation/knowledge mining
Concept hierarchy ontology
has its philosophical roots also in Quine’s
doctrine of ontological commitment and in
the ‘internal metaphysics’ of
Carnap/Putnam
Roughly, for a concept hierarchy ontology
the world and the semantic model are one
and the same
What exists = what the system says exists
Quineanism:
ontology is the study of the ontological
commitments or presuppositions
embodied in scientific theories (or in the
beliefs of experts)
Quineanism, too, faces the
integration problem
If an ontology is the set of ontological
commitments of a theory, how can we cope
with questions pertaining to the relations
between the objects to which different
theories are committed?
(Recall the Vienna Circle program of the
Unity of Science)
What is needed
is some sort of wider common framework
sufficiently rich and nuanced to allow concept
systems deriving from different
theoretical/data sources to be handcallibrated
What is needed
is not a Concept Hierarchy Ontology
but
a Reference Ontology
(something like old-fashioned,
realist,
metaphysics)
Reference Ontology
An ontology is a theory of a domain of
entities in the world
Ontology is outside the computer
seeks maximal expressiveness and
adequacy to reality
and sacrifices computational tractability for
the sake of representational adequacy
Reference Ontology
a theory of the tertium quid
– called reality –
needed to hand-callibrate
database/terminology systems
Methodology
Get ontology right first
(realism; descriptive adequacy; rather
powerful logic);
solve tractability problems later
DL: ontology deals with ‘simplified
models’
Tom Gruber (1993):
An ontology should make as few claims as
possible about the world being modeled …
specifying the weakest theory (allowing
the most models) and defining only those
terms that are essential to the
communication of knowledge consistent
with that theory.
Belnap
“it is a good thing logicians were around
before computer scientists;
“if computer scientists had got there first,
then we wouldn’t have numbers
because arithmetic is undecidable”
It is a good thing
Aristotelian metaphysics was around
before description logic, because
otherwise
we would have only hierarchies of
concepts/universals/classes and no
individual instances …
SNOMED-RT
Systematized Nomenclature of Medicine
A Reference Terminology with Legal Force
Example 2: SNOMED-RT
– 121,000 concepts,
– 340,000 relationships
– “common reference point for comparison and
aggregation of data throughout the entire
healthcare process”
Problems with both UMLS and
SNOMED
Each is a ‘fusion’ of several source
vocabularies, some of dubious quality
They were fused without an ontological system
being established first
They contain circularities, taxonomic gaps, and
unnatural ad hoc determinations
SNOMED RT (2000)
already has description logic definitions
but it also has some bad coding, which
derives from failure to pay attention to
ontological principles:
e.g.
both testes is_a testis
DL is supposed to allow future
SNOMED
to reason from data formulated in a
structured way
to handle multiple relationship types, in
addition to is_a
to take account of context-sensitivity in
use of terms
The long march of Description
Logic
Today SNOMED
Tomorrow THE WORLD
The Semantic Web Initiative
The Web is a vast edifice of
heterogeneous data sources
Needs the ability to query and integrate
across different conceptual systems
How resolve such incompatibilities?
enforce terminological compatibility via
standardized term hierarchies, with
standardized definitions of terms, which
1. satisfy the constraints of a description logic
(DL)
2. are applied as meta-tags to websites
Semantic Web effort
thus far devoted primarily to developing
systems for standardized representation of
web pages and web processes
(= ontology of web typography)
not to the harder task of developing of
ontologies (term hierarchies) for the
content of such web pages
Metadata: the new Silver Bullet
agree on a metadata standard for
washing machines as concerns
size, price, etc.
create machine-readable
databases and put them on the net
 consumers can query multiple
sites simultaneously
and search for highly specific,
reliable, context-sensitive results
A world of exhaustive, reliable
metadata would be a utopia.
Ontology Learning
Semi-automatic tuning of ontology with human
intervention
– cooperative paradigm
ontology
ontology
learning
candidate
new concepts
new
ontology
Verizon
The promise of Web Services, augmented with the
Semantic Web, is to provide THE major solution for
integration, the largest IT cost / sector, at $ 500 BN/year.
The Web Services and Semantic Web trends are
heading for a major failure (i.e., the most recent Silver
Bullet).
In reality, Web Services, as a technology, is in its infancy.
... There is no technical solution (i.e., no basis) other
than fantasy for the rest of the Web Services story.
Analyst claims of maturity and adoption (...) are already
false. ...
Verizon must understand it so as not to invest too heavily
in technologies that will fail or that will not produce a
reasonable ROI.
Dr. Michael L. Brodie, Chief Scientist, Verizon IT
OntoWeb Meeting, Innsbruck, December 16-18, 2002
PLAN
General problems with the Semantic Web
initiative
(Partial) solutions to these general problems
in the medical domain
Problems specific to the medical domain
The Semantic Web
General problems with the Semantic Web
initiative
(Partial) solutions to these general problems
in the medical domain
Problems specific to the medical domain
Problem 1: People lie
Meta-utopia is a world of reliable
metadata.
But poisoning the well can confer benefits
to the poisoners
Metadata exists in a competitive world.
Some people are crooks.
Some people are cranks.
Some people are Neokantians.
Problem 2: People are lazy
Half the pages on Geocities are called
“Please title this page”
Problem 3: People are stupid
The vast majority of the Internet's users
(even those who are native speakers of
English)
cannot spell or punctuate
Will internet users learn to accurately tag
their information with whatever DLhierarchy they're supposed to be using?
Problem 4: Multiple descriptions
“Requiring everyone to use the
same vocabulary denudes the
cognitive landscape, enforces
homogeneity in ideas.”
(Cary Doctorow)
Problem 5: Ontology Impedance
= semantic mismatch between ontologies
being merged
This problem recognized in Semantic Web
literature:
http://ontoweb.aifb.uni-karlsruhe.de
/About/Deliverables/ontoweb-del-7.6-swws1.pdf
Solution 1:
treat it as (inevitable) ‘impedance’
and learn to find ways to cope with the
disturbance which it brings
Suggested here:
http://ontoweb.aifb.uni-karls-ruhe.de/About/Deliverables/ontoweb-del-7.6-swws1.pdf
Solution 2: resolve the impedance
problem on a case-by-case basis
Suppose two databases are put on the
web.
Someone notices that "where" in the
friends table and "zip" in the places table
mean the same thing.
http://www.w3.org/DesignIssues/Semantic.html
Both solutions fail
treating mismatches as ‘impedance’ ignores
the problem of error propagation
(and is inappropriate in an area like
medicine)
2. resolving impedance on a case-by-case
basis defeats the very purpose of the
Semantic Web
The Semantic Web
General problems with the Semantic Web
initiative
(Partial) solutions to these general problems
in the medical domain
Problems specific to the medical domain
Solutions in the medical domain
Problem 1: People lie
Problem 2: People are lazy
Problem 3: People are stupid
None of these is true in the world of medical
informatics
Solutions in the medical domain
Problem 1: People lie
Problem 2: People are lazy
Problem 3: People are stupid
Achieve quality control via division of labour
Division of Labour
1. Clinical activities
2. Structured data representation
3. Software coding (e.g. for NLP)
Division of Labour
1. Clinical activities
2. Structured data representation
3. Software coding
4. Ontology building
Use 4. to constrain 2. and 3.
to achieve better data processing via quality
control
DL-Division of Labour
1. Clinical activities
2. Structured data representation
3. Software coding
4. Ontology building
For DL 4. is a special case of 3.
For DL
Ontologies are software tools
thus limited
in their expressive power
and in their effectiveness as quality
controls
IFOMIS idea:
distinguish two separate tasks:
- the task of developing computer
applications capable of running in real time
the task of developing an expressively rich
ontology of a sort which will allow
sophisticated quality control
The Semantic Web
General problems with the Semantic Web
initiative
(Partial) solutions to these general problems
in the medical domain
Problems specific to, or made more acute
within, the medical domain
Problem 4: Multiple descriptions
Requiring everyone to use the
same vocabulary to describe their
material is not always medically
practicable
Clinicians
often do not use category systems at all –
they use unstructured text
from which usable data has to be extracted
in a further step
Why?
Because every case is different, much
patient data is context-dependent
Problem 5: Ontology Impedance
= semantic mismatch between ontologies
‘gene’ used in websites issued by
biotech companies involved in gene
patenting
medical researchers interested in role of
genes in predisposition to smoking
insurance companies
Other problems with DL-based
ontologies
DL poor when dealing with contextdependent information/usages of terms
DL poor when it comes to dealing with
information about instances (rather than
concepts or classes)
 also DL poor when it comes to dealing
with time
SARS
is NOT
Severe Acute Respiratory Syndrome
it is THIS collection of instances of
Severe Acute Respiratory Syndrome
associated with THIS coronavirus and ITS
mutations
BFO
= basic formal ontology
BFO
ontology not the ‘standardization’ or
‘specification’ of concepts
(not a branch of knowledge or concept
engineering)
but an inventory of the types of entities
existing in reality
BFO goal:
to remove ontological
impedance by constraining
terminology systems with good
ontology
BFO not a computer application
but a reference ontology
(not a reference terminology
in the sense of SNOMED)
Recall:
GDB: a gene is a DNA fragment that can be
transcribed and translated into a protein
Genbank: a gene is a DNA region of
biological interest with a name and that
carries a genetic trait or phenotype
Ontology
‘fragment’, ‘region’, ‘name’, ‘carry’, ‘trait’,
‘type’
... ‘part’, ‘whole’, ‘function’, ‘inhere’,
‘substance’ …
are ontological terms in the sense of
traditional (philosophical) ontology
UMLS Semantic Network
a tool to find our way around the UMLS
Metathesaurus
(January 2003 version consists of 135
Semantic Types + 54 links)
can be arranged in the form of a graph
whose vertices are the Semantic Types
and whose edges are the links.
UMLS Semantic Network
arranged in a double tree structure, with
two superclasses: Entities and Events.
Entity = “A broad type for grouping
physical and conceptual entities”.
Event = “A broad type for grouping
activities, processes and states”.
Basic Formal-Ontological
Distinctions
1. Continuant vs. Occurrent (= SNAP
vs. SPAN)
2. Dependent vs. Independent
3. Universals vs. Particulars
Basic Formal-Ontological
Distinctions
1. Continuant vs. Occurrent (= SNAP
vs. SPAN)
2. Dependent vs. Independent
3. Universals vs. Particulars
Continuant vs. Occurrent
(= SNAP vs. SPAN)
continuants = entities which continue to exist
through time, e.g. organisms, cells,
chromosomes
occurrents = entities which unfold themselves
through time in successive temporal phases,
e.g. an intravenous drug infusion
continuant/occurrent = (roughly) UMLS
distinction between Entity and Event
Basic Formal-Ontological
Distinctions
1. Continuant vs. Occurrent (= SNAP
vs. SPAN)
2. Dependent vs. Independent
3. Universals vs. Particulars
Dependent vs. Independent
independent = has an inherent ability to
exist without reference to other entities –
e.g. molecules, organisms, planets
dependent = require a support from other
entities in order to exist – e.g. cellular
motion (which requires reference to a cell
which moves), or viral infection (which
requires reference to some carrier)
Need to find ways to deal with time
in medical informatics
Guidelines
Workflow
 need to be clear about the distinction
between continuants and occurrents
occurrents (in medicine) are always
dependent entities.
Thus of the four abstractly possible
combinations only three are instantiated
Independent and Dependent Continuants
Independent Continuants = substances,
objects, things
Dependent Continuants =
qualities (your height, your skin-color)
states or conditions (your diabetes)
roles (your role as student, as doctor)
functions (of a drug, of a machine)
UMLS Semantic Network
Conceptual Entity, with subclasses:
Organism Attribute
Finding
Idea or Concept
Occupation or Discipline
Organization
Group
Group Attribute
Intellectual Product
Language
Conceptual Entities
are dependent on minds
but Organism Attributes can exist without
minds
and Groups (e.g. a group of macac
monkeys) can exist without minds
UMLS Semantic Tree with root Event
Event has subclasses:
Activity
Phenomenon or Process
Natural Phenomenon or Process
Biologic Function
Physiologic Function
Pathologic Function
runs together functions, which are continuants,
with processes, which are occurrents
Functions are continuants
Functions exist self-identically through time; they
have no temporal phases and exist even when
not being exercised
The exercise of a function unfolds itself through
its temporal phases
The compilers of UMLS have confused what
exists dispositionally in a thing, and is the
product of design or evolution,
with what the thing does episodically, and is the
product of intentionality or immediate causal
influence
UMLS Semantic Type Collections
Chen, Perl et al. and Geller, Perl et al.
partition the UMLS Semantic Network into
more meaningful units called Semantic
Type Collections.
problems revealed by the BFO analysis
especially in:
Pathologic Function
Physiologic Function
Idea or Concept
Subclasses of Pathologic Function
Experimental Model of Disease
Cell or Molecular Dysfunction
Cell or Molecular Dysfunction
Disease or Syndrome
Mental or Behavioral Dysfunction
Subclasses of Physiologic Function
Organ or Tissue Function
Mental Process
Molecular Function
Mental Process
Genetic Function
Cell Function
Subclasses of Idea or Concept
Functional Concept
Body System
Temporal Concept
Qualitative Concept
Quantitative Concept
Spatial Concept
Geographic Area
Body Location or Region
Molecular Sequence
Carbohydrate Sequence
Amino Acid Sequence
Body Space or Junction
Nucleotide Sequence
CIRCULATORY
SYSTEM
bodily systems
are parts of
organisms
(like fingers and
hands)
UMLS has ontological problems, too
Idea or Concept
Functional Concept
Qualitative Concept
Quantitative Concept
Spatial Concept
Body Location or Region
Body Space or Junction
Geographic Area
Molecular Sequence
Amino Acid Sequence
Carbohydrate Sequence
Nucleotide Sequence
UMLS has ontological problems, too
Idea or Concept
Functional Concept
Qualitative Concept
Quantitative Concept
Spatial Concept
Body Location or Region
Body Space or Junction
Geographic Area
Molecular Sequence
Amino Acid Sequence
Carbohydrate Sequence
Nucleotide Sequence
Copenhagen
is an Idea or Concept
UMLS has ontological problems, too
Idea or Concept
Functional Concept
Qualitative Concept
Quantitative Concept
Spatial Concept
Body Location or Region
Body Space or Junction
Geographic Area
Molecular Sequence
Amino Acid Sequence
Carbohydrate Sequence
Nucleotide Sequence
Spatial Concept
Body Location or Region
An area, subdivision, or region of the body
demarcated for the purpose of topographical
description.
Body Space or Junction
An area enclosed or surrounded by body parts or
organs or the place where two anatomical
structures meet or connect.
Geographic Area
A geographic location, generally having definite
boundaries.
Idea or Concept
Functional Concept
A concept which is of interest because it
pertains to the carrying out of a process or
activity.
Body System
A complex of anatomical structures that
performs a common function.
Case Study: Regulation of Blood
Pressure
UMLS:
hypertension is a Disease or Syndrome or
a Sign or Symptom
blood pressure is an Organism Function.
Both are dependent SNAP entities: they
endure identically for a certain period of
time and they depend for their existence
on their bearer.
The hydraulic equation: BP = CO*PVR
arterial blood pressure is directly
proportional to the product of blood
flow (cardiac output, CO) and
peripheral vascular resistance (PVR).
UMLS:
blood flow is an Organism Function
cardiac output is a Laboratory or Test
Result (SNAP) and a Diagnostic
Procedure (SPAN)
Blood pressure is_proportional_to_a
laboratory or test result?
Blood pressure is_proportional_to_a
diagnostic procedure?
An amino acid sequence is_an idea or
concept
Copenhagen is_a spatial concept
How eliminate this nonsense?
Basic Formal-Ontological
Distinctions
1. Continuant vs. Occurrent (= SNAP
vs. SPAN)
2. Dependent vs. Independent
3. Universals vs. Particulars
Replace concepts in peoples’ heads
(e.g. in UMLS)
with universals in re
teach medical terminology systems the
distinction between universals and
particulars
distinguish clearly between ontology
(the study of reality) and
epistemology/psychology (the study
of peoples’ concepts)
UMLS confuses epistemology with
ontology
it confuses the results of our attempts to
gain knowledge of specific aspects of the
organism (functions, qualities, processes)
with those aspects themselves.
What would a better UMLS toplevel look like?
The Reference Ontology
Community
IFOMIS (Leipzig)
Laboratories for Applied Ontology (Trento/Rome,
Turin)
Foundational Ontology Project (Leeds)
Ontology Works (Baltimore)
Ontek Corporation (Buffalo/Leeds)
Language and Computing (L&C)
(Belgium/Philadelphia)
Domains of Current Work
IFOMIS Leipzig: Medicine, Bioinformatics
Laboratories for Applied Ontology
Trento/Rome: Ontology of Cognition/Language
Turin: Law
Foundational Ontology Project: Space, Physics
Ontology Works: Genetics, Molecular Biology
Ontek Corporation: Biological Systematics
Language and Computing: Natural Language
Understanding
Two basic BFO oppositions
Granularity
(of molecules, genes, cells, organs,
organisms ...)
SNAP vs. SPAN
getting time right of crucial importance for
medical informatics
BFO = SNAP/SPAN + Theory of
Granular Partitions +
– theory of universals and instances
– theory of part and whole
– theory of boundaries
– theory of functions, powers, qualities, roles
– theory of environments
– theory of spatial and spatiotemporal regions
MedO: medical domain ontology
– universals and instances and normativity
– theory of part and whole and absence
– theory of boundaries/membranes
– theory of functions, powers, qualities, roles,
(mal)functions, bodily systems
– theory of environments: inside and outside the
organism
– theory of spatial and spatiotemporal regions:
anatomical mereotopology
MedO: medical domain ontology
– theory of granularity relations
– between
–
molecule ontology
–
gene ontology
–
cell ontology
–
anatomical ontology
–
etc.