1 Ontology as Master Discipline of Information Science Barry Smith

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Ontology as Master Discipline of Information Science

Barry Smith

http://ifomis.de

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Ontology as Master Discipline of Information Science

Barry Smith

http://ifomis.de

Real

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Real Ontology is a branch of philosophy

the science of what is

the science of the kinds and structures of objects, properties, events, processes and relations in reality

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Real ontology seeks to provide a definitive and exhaustive classification of entities in all spheres of being.

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It seeks to answer questions like this:

What categories of entities are needed for a complete description and explanation of all the goings-on in the universe?

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Ontology is in many respects comparable to the theories produced by science

… but it is radically more general than these

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It can be regarded as a kind of generalized chemistry or zoology (Aristotle’s ontology grew out of biological classification)

(Russell: Logic is a zoology of facts)

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Aristotle

First ontologist

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First ontology

(from Porphyry’s Commentary on Aristotle’s Categories)

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Linnaean Ontology

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Ontologies are

(very roughly)

taxonomical trees

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Ontology is distinguished from the special sciences

it seeks to study all of the various types of entities existing at all levels of granularity

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and to establish how they hang together to form a single whole (‘reality’ or ‘being’)

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Unity achieved

via a good theory of relations

and via taxonomies

at different levels of granularity (atomic, molecular, cellular, organismic, etc.)

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Sources for ontological theorizing:

thought experiments

the study of ancient texts

development of formal theories

the results of natural science

now also: working with computers

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The existence of computers

and of large databases

allows us to express old philosophical problems in a new light

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The problem of the unity of science

The logical positivist solution to this problem addressed a world in which sciences are identified with

printed textsWhat if sciences are identified with

Large Databases ?

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Each family of databases

has its own idiosyncratic terms and concepts

by means of which it represents the information it receives

How to resolve the incompatibilities which result when databases need to be merged?

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The Database Tower of Babel Problem

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The term ‘ontology’came to be used by information scientists to describe the construction of standardized taxonomies designed to make databases mutually compatible

and thus to make data transportable from one environment to another

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An ‘ontology’is a dictionary of terms formulated in a canonical syntax and with commonly accepted definitions and axioms

designed to yield a shared framework

for use by different information systems communities.

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An ontology

is a concise and unambiguous description of the principal, relevant entities of an application domain and of their potential relations to each other

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SO FAR

SO GOOD

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But how was this idea in fact realized?

How did information systems engineers proceed to build ontologies?

By looking at the world, surely

Well, No

They built ontologies by looking at what people think about the world

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Quine

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For Quineans

Ontology studies, not reality,

but scientific theories

From ontology

… to ontological commitment

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Quine:

each natural science has its own preferred repertoire of types of objects to the existence of which it is committed

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Quineanism:

ontology is the study of the ontological commitments or presuppositions embodied in the different natural sciences

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Quine:

only natural sciences can be taken ontologically seriously

The way to do ontology is exclusively through the investigation of scientific theories

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Thus it is reasonable to identify ontology

– the search for answers to the question: what exists? –

with the study of the ontological commitments of natural scientists

All natural sciences are compatible with each other

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PROBLEM

The Quinean view of ontology becomes strikingly less defensible

when the ontological commitments of various non-scientists are allowed into the mix

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How, ontologically, are we to treat the commitments of

astrologists,

clairvoyants,

believers in voodoo?

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How, ontologically, are we to treat the commitments of

patients who believe that their illness is caused by evil spirits or magic spells?

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Growth of Quinean ontology outside philosophy:

Psychologists and cognitive anthropologists have sought to elicit the ontological commitments

(‘ontologies’, in the plural)

of different cultures and groups.

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This is not ontology

Not all the things that people believe in are genuine objects of ontological investigation

Only what exists is a genuine object of ontological investigation

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Why, then,

do information systems ontologists study peoples’ beliefs, thoughts, concepts

rather than the objects themselves?

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Arguments for Ontology as Conceptual Modeling

Ontology is hard.

Life is short.

Let’s do conceptual modeling instead

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programming real ontology into computers is hard

therefore:

we will simplify ontology

and not care about reality at all

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Painting the Emperor´s Palace is

h a r d

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therefore

we will not try to paint the Palace at all

... we will be satisfied instead with a grainy snapshot of some other building

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Ontological engineers

neglect the standard of truth to reality

in favor of other, putatively more practical, standards:

above all programmability

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They turn to substitutes:

to models,

to conceptualizations

because these are easier to handle

(… they move from messy noumenal reality to neatly packaged “phenomena” …)

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For an information system ontology

there is no reality other than the one created through the system itself,

so that the system is, by definition, correct

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Only those objects exist which are represented in the system

(constructivism)

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Tom Gruber (1995):

‘For AI systems what “exists” is

what can be represented’

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Ontological engineering

concerns itself with conceptualizations

It does not care whether these are true of some independently existing reality.

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In the world of information systems

there are many surrogate world models

and thus many ontologies

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… and all ontologies,

are equalboth good and bad,

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ATTEMPTS TO SOLVE THETOWER OF BABEL PROBLEM

VIA ONTOLOGIES AS“CONCEPTUAL MODELS”

HAVEFAILED

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HALF WAYTHROUGH

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Can Real Ontology do Better?

Test Domain:

Medical Terminology

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Example 1: UMLS

Universal Medical Language System

Taxonomy system maintained by National Library of Medicine in Washington DC

134 semantic types

800,000 concepts

10 million inter-concept relationships

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Example 2: SNOMED

Systematized Nomenclature of Medicine

Taxonomy system maintained by the College of American Pathologists

121,000 concepts

340,000 relationships

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SNOMED

designed to foster interoperability

to serve as a

“common reference point for comparison and aggregation of data throughout the entire healthcare process”

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Problems with UMLS and SNOMED

Each is a fusion of several source vocabularies

They were fused without an ontological system being established first

They contain circularities, taxonomic gaps, unnatural ad hoc determinations

… several billion dollars still being wasted in the making of retrospective fixes

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Example 3: GALEN

Two levels:

ontologically powerful model of clinical information inside the computer

plus

a range of terminological services for clinical tasks involving different coding systems, including natural language

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Problems with GALEN

Ontology is ramshackle and has been subject to repeated fixes

Its unnaturalness makes coding slow and expensive

Coding thus far limited in extent

(surgical processes)

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Blood

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Representation of Blood in UMLS

Blood

Tissue

EntityPhysical Object

Anatomical StructureFully Formed Anatomical Structure

An aggregation of similarly specialized cells and the associated intercellular substance.

Tissues are relatively non-localized in comparison to body parts, organs or organ components

Body SubstanceBody Fluid Soft Tissue

Blood as tissue

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Representation of Blood in SNOMED

Blood

Liquid Substance

Substance categorized by physical state

Body fluid

Body Substance

Substance

Blood as fluid

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Representation of Blood in GALEN

Blood

SoftTissue

DomainCategoryPhenomenon

Blood as SoftTissue with two states:LiquidBlood and CoagulatedBlood

SubstanceTissue

GeneralisedSubstance SubstanceorPhysicalStructure

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Plus attempts at Patients’ Ontology

based on WordNet = online lexical reference system for the English language

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Representation of Blood in WordNet

Blood

Humor

the four fluids in the body whose balance was believed to determine our emotional and physical state

along with phlegm, yellow and black bile

EntityPhysical Object

SubstanceBody Substance

Body Fluid

Blood as humor

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So what is the ontology of blood?

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We cannot solve this problem just by looking at concepts

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concept systems may be simply incommensurable

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the problem can only be solved

by taking the world itself into account

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This implies a view of ontology

not as a theory of concepts

but as a theory of reality

But how is this possible?

How can we get beyond our concepts?

answer: ontology must be maximally opportunistic

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Maximally opportunistic

means:

look at concepts and beliefs critically

and always in the context of a wider view which includes independent ways to access the objects themselves

at different levels of granularity

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Ontology must be maximally opportunistic

This means:

don’t just look at beliefs

look at the objects themselves

from every possible direction,

formal and informal

scientific and non-scientific …

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Maximally opportunistic

means:

look at the same objects at different levels of granularity:

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“skritek”

objects are in the worldnot all concepts correspond to objects

not all concepts are relevant to ontology

concepts are in the head

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problem of ‘merging’ ontologies

“skritek”

“blaznivy”

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How to solve the Tower of Babel Problem?

How to fuse these mutually incompatible ‘conceptual models’ of blood ?

By drawing on the results of philosophical work in ontology carried out over the last 2000 years

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First Step:

A good medical domain ontology

presupposes a good formal or top-level ontology

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Formal

part

hole

connected (spatially, causally)

substance

system

state

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Material

organism

tissue

symptom

circulatory system

organ

is the nose an organ?

is the circulatory system an organ

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Second step: select out the good conceptualizations

these have a reasonable chance of being integrated together into a single ontological system

• based on tested principles

• robust

• conform to natural science

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IFOMIS

Institute for Formal Ontology and Medical Information Science

University of Leipzig

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PARTNERS

Ontology Group, LADSEB-CNR, Padua/Trento

Institute of Cognitive Sciences and Technologies (ISTC-CNR), Rome

ONTEK Corporation

Language and Computing, Belgium www.landc.be

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Strategy: A Network of Domain Ontologies

Material (Regional) Ontologies

Basic Formal Ontology (BFO)

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A Network of Domain Ontologies

BFO

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B(Chem)O

BFO

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B(Med)O

B(Chem)O

BFO

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B(Cell)O

B(Med)O

B(Chem)O

BFO

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B(Gen)O B(Cell)O

B(Med)O

B(Chem)O

BFO

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B(Epidem)O

B(Gen)O B(Cell)O

B(Med)O

B(Chem)O

BFO

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Ontology

like cartography

must work with maps at different scales and with maps picking out different dimensions of invariants

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Thus ontology needs

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Ontological ZoomingOntological Zooming

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Universe/Periodic Table

animal

bird

canary

ostrich

fishontology of

biological species

ontology of DNA space

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Universe/Periodic Table

animal

bird

canary

ostrich

fish

both are transparent partitions of one and the same reality

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There are many compatible map-like partitions

many maps at different scales,

all transparent to the reality beyond

the mistake arises when one supposes

that only one of these partitions is a true map of what exists

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Medical ontologies

at different levels of granularity:

cell ontology

drug ontology *

protein ontology

gene ontology *

anatomical ontology *

epidemiological ontology

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Medical ontologies

disease ontology

therapy ontology

pathology ontology *

and also

physician’s ontology

patient’s ontology

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Medical ontologies

and even

hospital management (billing) ontology *

* = already exists (but in a variety of mutually incompatible forms)

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Partitions should be cuts through reality

a good medical ontology should NOT be compatible with the conceptualization of disease as:

caused by evil spirits and demons and cured by skritek

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IFOMIS TestsUniform top-level ontology for medicine

applicable at distinct granularities

Test-case development of partial medical domain ontologies applied to:

• Standardization of clinical trial protocols

• Clinical trial Merkmal-dictionary

• Processing of unstructured patient records (www.landc.be)

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Uniform top-level ontology for medicine

ONE YEAR

Applicable at distinct granularities (e.g. gene ontology)

FOUR YEARS

Standardization of clinical trial protocol

TWO YEARS

Clinical trial Merkmal-dictionary

TWO YEARS

Processing of unstructured patient records (www.landc.be)

THREE YEARS

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Uniform top-level ontology for medicine

NO COMPETITOR

applicable at distinct granularities

NO COMPETITOR

Standardization of clinical trial protocol

NO SERIOUS COMPETITOR

Clinical trial Merkmal-dictionary

NO COMPETITOR

Processing of unstructured patient records

MANY COMPETITORS, BUT GOOD MEASURES OF EFFECTIVENESS

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The End

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1 Ontology as Master Discipline of Information Science Barry Smith