Transcript Document 7821241

R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Discovery Seminar 118330/UE 141 CC – Fall 2007
Difficult Problems, Easy Solutions:
Referent Tracking in Biomedicine
Session 3: 09/17/2007
Realism-based ontologies
Werner CEUSTERS
Center of Excellence in Bioinformatics and Life Sciences
Ontology Research Group
University at Buffalo, NY, USA
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
If, later, you can remember just one thing of this
presentation, then make sure it is this one:
If you use the word
“ontology”, ALWAYS be
specific about what you
mean by it.
R T U New York State
Center of
Excellence
in
Tom
Gruber’s
view
Bioinformatics & Life Sciences
•
The word "ontology" seems to generate a lot
of controversy in discussions about AI. It has
a long history in philosophy, in which it refers
to the subject of existence. It is also often
confused with epistemology, which is about
knowledge and knowing.
• In the context of knowledge sharing, I use the term ontology to mean a
specification of a conceptualization. That is, an ontology is a description (like
a formal specification of a program) of the concepts and relationships that can
exist for an agent or a community of agents. This definition is consistent with
the usage of ontology as set-of-concept-definitions, but more general. And it is
certainly a different sense of the word than its use in philosophy.
R T U New York State
Center of Excellence in
The O-word
in science
Bioinformatics
& Life Sciences
N. Guarino, P. Giaretta, "Ontologies and Knowledge Bases: Towards a
Terminological Clarification". In Towards Very Large Knowledge Bases:
Knowledge Building and Knowledge Sharing, N. Mars (ed.), pp 25-32.
IOS Press, Amsterdam, 1995.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
The O-word in buzz-speak
• “An ontology is a classification methodology for formalizing a subject's
knowledge or belief system in a structured way. Dictionaries and
encyclopedias are examples of ontologies.” (X1)
• “A terminology (or classification) is a kind of ontology by definition and
it should preserve (and "understand") the relationships between the
1,000s of terms in it or else it would become a mere dictionary (or at best
a thesaurus).”
(X2)
• “Ontologies are Web pages that contain a mystical unifying force that
gives differing labels common meaning.”
(X3)
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Today’s biggest problem:
a confusion between
“terminology” and “ontology”
• The conditions to be agreed upon when to use a certain
term to denote an entity, are often different from the
conditions which make an entity what it is.
– Trees would still be different from rabbits even if there were no
humans to agree on what names we should use to refer to them
• “ontos” means “being”. The link with reality tends to be
forgotten: one concentrates on the models instead of on
the reality.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Terminology
• A theory concerned with those aspects of the
nature and the functions of language which permit
the efficient representation and transmission of
items of knowledge (J. Sager)
• Precise and appropriate terminologies provide
important facilities for human communication (J.
Gamper)
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Ontology
• An ontology is a representation of some pre-existing
domain of reality which
– (1) reflects the properties of the objects within its domain in
such a way that there obtains a systematic correlation between
reality and the representation itself,
– (2) is intelligible to a domain expert
– (3) is formalized in a way that allows it to support automatic
information processing
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A division of labour
• Terminology:
– Communication amongst humans
– Communication between human and machine
• Ontology:
– Representation inside a machine of reality as it exists
outside the machine
• a representation is not a model or a simplification; ‘cats’ is
not a simplification of cats
– Communication amongst machines
– Interpretation by machines
R T U New York State
Center of Excellence in
An example:
Bioinformatics & Life Sciences
Electronic Health Records (1)
1. Particular patients, their disorders, their body
parts, their worries, ..., and the relationships
amongst them;
2. Statements about 1, made my people (physicians,
relatives, patient,...) and machines (lab
analysers), as well as statements thereof;
3. Electronic records as collections of 2, and
systems that manage these records;
R T U New York State
Center of Excellence in
Bioinformatics &An
Lifeexample:
Sciences
Electronic Health Records (2)
4. Terminologies, classification systems,
biomedical KBs, and “ontologies”,
•
•
forced upon the producers of statements, restricting the
semantics
designed on the basis of various theories on how reality can
be looked at
5. Architectures of record systems
•
•
Forced upon the producers of statements restricting the
syntax
Designed on the basis of various theories on how reality
can be looked at, AND how healtcare workers operate
therein.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Current “state of the art” in
biomedical informatics
• A pervasive bias towards “concepts”
– Content wise:
• Work based on ISO/TC37 that advocates the Ogden-Richards
theory of meaning
• Corresponds with a linguistic reading of “concept”
– Architecture wise:
• In Europe: work based on CEN/TC251 WG1 & WG2 that
follow ISO/TC37
• In the US: HL7, inspired by Speech Act Theory
• “Concepts” used as elements of information models, hence
mixing a linguistic and engineering reading.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
‘Concepts’ are the bad guys:
‘Concept’ used in ‘ontology’
is used for different things
• meaning shared in common by synonymous
terms
• idea shared in common in the minds of those
who use these terms
• unit of knowledge describing meanings
• universal, feature or property shared in common
by entities in the world
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A gradual shift in meaning
• ISO-1087 (1990) concept: a unit of thought
constituted through abstraction on the basis of
properties common to a set of objects.
• ISO-1087 (2000) concept: a unit of knowledge
created by a unique combination of
characteristics. Characteristic itself is defined
as: an abstraction of a property of an object or
of a set of objects.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A refinement of relationships
but at the wrong level
• Only associative relationships can hold between
concepts à la ISO-1987 in 1990.
• Both associative and generic relationships can
hold between concepts à la ISO-1987 in 2000.
• A partonomy relationship can hold for the 2000definition, but here the meaning is different than
partonomy at the level of the real world entities.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Realist Ontology
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Realist ontology
• describes what is fundamental in the totality of
what exists,
• defines the most general categories to which
we need to refer in constructing a description
of reality,
• tells us how these categories are related.
• is able to be used to describe reality at any
point in time.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Realist Ontology
Methodology
• Central are the “particulars” (p)
– Me, you, my heart, that patient’s fracture, that car accident
(which caused his fracture),…
– ‘Referent tracking’
• Particulars instanciate classes (c) distinguished on the
basis of ontological properties:
– Essence, dependency, identity, relationship with time, …
– Some classes are “universals” (u)
• Define relationships axiomatically at four levels:
– p – p, c – c, p – c, c – p
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
(multi-) trauma
• How many disorders
this case ?
– What we see was produced
cut of an axe:
•
•
•
•
Skin cut
Section of several arteries
Fracture
...
• Of what are the existing things instances ?
• How do they relate to each other ?
exist in
by one
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Images and their relation to reality
• “This is not a brain”
• This is a “Wrap around
artefact”:
– This artefact occurs in the
phase encoding direction of
an MRI image when the field
of view selected is not wide
enough. Structures outside
the field of view are therefore
assumed by the computer to
be on the other side of the
image.
• Statements about what is
seen on an image
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A visit to the operating theatre
A lot of
objects present
This
surgeon
with some relations
Part of
This mask
This
amputatio
n stump
This hand
Haydom Lutheran Hospital, Tanzania
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
A visit to the operating theatre
A lot of
processes going on
This wound
being closed
by holding ...
with some relations
Part of
That wound
fluid
drained
This kocher
being held in
that hand of
that surgeon
Haydom Lutheran Hospital, Tanzania
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
“Axiom” 1
epistemology
• If the picture is not a fake, we (i.e., me and this
audience) KNOW that that hand, that surgeon, ...
EXIST(ed), i.e. ARE (were) REAL.
• But importantly: that hand, surgeon, kocher, mask,
... EXIST(ed) independent of our knowledge
about them and also the part-relationship
between that hand and that surgeon, and the
processes going on, are (were) equally real.
ontology
R T U New York State
Center
Excellence
in
Theofrealist
ontological
Bioinformatics &(Ignacio
Life Sciences
Angelelli)
Substance
Universals
instance
differentia
exemplify
square
Quality
Universals
instance
inheres
Substance Particulars
Quality Particulars
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
How to differentiate
qualities from substances ?
• Language may fool us:
–
–
–
–
Being pale
Being human
Being a person
Being sick
But so does logic:
– Pale(x)
– Human(x)
– Person(x)
– Sick(x)
• Can all be properties of particulars, namely me and
you !
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Basic Ontological Notions
• Identity
– How are particulars distinguished from each other ?
• Unity
– How are all the parts of a particular isolated ?
• Essence
– Can a property change over time ?
• Dependence
– Can an entity exist without some others ?
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Identity & instanciation
person
Living
creature
child
adult
caterpillar
butterfly
animal
t
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Basic Formal Ontology
Basic Formal Ontology consists in a series of
sub-ontologies (most properly conceived as a
series of perspectives on reality), the most
important of which are:
– SnapBFO, a series of snapshot ontologies (Oti ),
indexed by times: continuants
– SpanBFO a single videoscopic ontology (Ov):
occurants.
Each Oti is an inventory of all entities existing at
a time. Ov is an inventory (processory) of all
processes unfolding through time.
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
SpanBFO
R T U New York State
Center of Excellence in
Bioinformatics & Life Sciences
Kinds of relations
• <instance, instance>:
– my heart part_of me
• <instance, class>:
– me instance_of human being
• <class, instance>:
– president of the US empowered_by US constitution (?)
• <class, class>:
– gene expression has_agent RNA polymerase