1

How to test a philosophical theory empirically

Barry Smith

Institute for Formal Ontology and Medical Information Science

http://ifomis.de

sponsored by the Wolfgang Paul Program of the Alexander von

Humboldt Stiftung

2

and by the

des Bundes

3

Institute for Formal Ontology

and Medical Information Science

4

Ontology as a branch of philosophy

seeks to establish

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

5

Ontology a kind of generalized chemistry or zoology

(Aristotle’s ontology grew out of biological classification)

6

Aristotle

world’s first ontologist

7

World‘s first ontology

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

8

Linnaean Ontology

9

Medical Diagnostic Ontology

10

Ontology is distinguished from the special sciences

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

11

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

12

Sources for ontological theorizing:

the study of ancient texts

thought experiments (we are philosophers, after all)

the development of formal theories

the results of natural science

now also:

working with computers

13

The existence of computers

and of large databases

allows us to express old philosophical problems in a new light

14

Example: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 ?

15

16

The Database Tower of Babel Problem

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?

Compare: how to unify biology and chemistry?

17

The term ‘ontology’now used by information scientists to describe the building of standardized taxonomies

which are designed to make databases mutually compatible

18

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

19

How has this idea been realized?

How have information systems engineers built ontologies?

From where did they take the term ‘ontology’?

20

From Quine

21

Quine:

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

22

the study of ontology is identified with the study of such Ontological Commitments

… for Quineans, the ontologist studies, not reality,

but scientific theories

23

Quineanism:

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

24

In the hands of information scientists

this is transformed into the view that ontology should study

the concepts people use

Ontology becomes

the study of concept systems or conceptual models

(becomes almost a branch of psychology)

25

Arguments for Ontology as Conceptual Modeling

Ontology is hard.

Life is short.

Let’s do conceptual modeling instead

26

Ontological engineers

thus neglect the standard of truthin favor of other, putatively more practical standards:above all programmability

27

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

28

Ontological engineering

concerns itself with concept systems

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

29

‘Ontology’

is tremendously popular in information systems research today

… b u t

30

ATTEMPTS TO SOLVE THETOWER OF BABEL PROBLEM

VIA ONTOLOGIES ASCONCEPTUAL MODELS

HAVEFAILED

31

To see why

let us consider some examples of concept systems in the medical domain

32

Attempts at such standardization include:

1. UMLS

2. SNOMED

3. GALEN

33

Example 1: UMLS

Universal Medical Language System

Very large taxonomy maintained by National Library of Medicine in Washington DC

34

Example 1: UMLS

134 semantic types

800,000 concepts

10 million interconcept relationships

UMLS is the product of fusion of several source vocabularies

(built out of concept trees)

35

Example 2: SNOMED-RT

Systematized Nomenclature of Medicine

A Reference Terminology with Legal Force

36

Example 2: SNOMED-RT

121,000 concepts,

340,000 relationships

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

37

Problems with 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

38

Example 3: GALEN

Generalised Architecture for

Languages, Encyclopaedias and

Nomenclatures in Medicine

Applied especially to surgical procedures

39

Problems with GALEN

Ontology is ramshackle and has been subject to repeated fixes

Unnaturalness makes coding slow and expensive, hence narrow scope

Not gained wide acceptance

40

Blood

41

Representation of Blood in GALEN

Blood has two states, LiquidBlood and CoagulatedBlood

Blood

SoftTissue

DomainCategoryPhenomenon

SubstanceTissue

GeneralisedSubstance SubstanceorPhysicalStructure

42

Representation of Blood in UMLS

Blood

Tissue

EntityPhysical Object

Anatomical StructureFully Formed Anatomical Structure

Body SubstanceBody Fluid Soft Tissue

Blood as tissue

43

Representation of Blood in SNOMED

Blood

Liquid Substance

Substance categorized by physical state

Body fluid

Body Substance

Substance

Blood as Fluid

44

Database standardization

is desparately needed in medicine

… to enable the huge amounts of data

resulting from clinical trials by different groups working on the same drugs/therapies/diagnostic methods

…to be fused together

45

How make ONE SYSTEM out of this?

To reap the benefits of standardization we need to resolve such incompatibilities?

But how?

Not just by looking at the concepts underlying the respective systems

For how, just by looking at separate concepts, could we establish how these concept systems relate to each other?

46

different conceptual systems

47

need not interconnect at all

48

the only way to make them interconnect

is by looking not just at concepts

49but also at the reality beyond

50

How to solve the Tower of Babel Problem

51

Look not at concept systems alone

but at how concept systems relate to the world beyond

52

Concept systems which are transparent to reality

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

53

This means we need to return to the traditional view of

ontology

54

… as a maximally opportunistictheory of reality

Ontology should be modeled

not on psychology

but (as with Aristotle)

on biology or chemistry

55

Maximally opportunistic

means:

don’t just look at concepts

look at the objects themselves

towards which such concepts are directed

56

… look at the objectsfrom every available direction

both formal and informal

scientific and non-scientific

empirical and theoretical

attempting always to establish how these objects hang together ontologically

57

Maximally opportunistic

means:

look at concepts critically

and always in such a way as to

include independent ways to access the objects themselves

58

How to test a philosophical theory empirically?

59

IFOMIS

Institute for Formal Ontology and Medical Information Science

Faculty of Medicine

University of Leipzig

60

IFOMIS

in collaboration with those groups of ontological engineers who have recognized that they can improve their methods

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

61

… above all:

LADSEB, Padua/Trento

ITBM-CNR, Rome

ONTEK Corporation

Language and Computing EV, Belgium

62

It will develop medical ontologies

at different levels of granularity:

cell ontology

drug ontology *

protein ontology

gene ontology *

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

63

and also

anatomical ontology *

epidemiological ontology

disease ontology

therapy ontology

pathology ontology *

64

physician’s ontology

patient’s ontology

and even

hospital management ontology *

together with

65

Ontology

like cartography

must work with maps at different scales

How fit these maps (conceptual grids) together into a single system?

66

67

Consider them

as grids

transparent to reality

allowing our directedness towards objects beyond

68

Cartographic Projection

69

Optical Projection

70

conceptual grids treated always only as mediators towards objects in reality

intentionality = the directedness towards objects via conceptual grids

object

71

intentionality = the directedness towards concepts

concepts

72

conceptual grids treated always only as mediators towards objects in reality

intentionality = the directedness towards objects via conceptual grids

object

73

Intentional directedness

… is effected via conceptual grids

we are able to reach out to the objects themselves because our conceptual grids are transparent

74

Kantianism

= the inability to appreciate the fact that our conceptual grids can be transparent to reality beyond

75

there are many compatible map-like partitions

at different scales,

which are all transparent to the reality beyond

76

Universe/Periodic Table

animal

bird

canary

ostrich

fishontology of

biological species

ontology of DNA space

77

Universe/Periodic Table

animal

bird

canary

ostrich

fish

both are transparent partitions of one and the same reality

78

 

Ontological Zooming

79

The job of the ontologist

is to understand how different partitions of the same reality interrelate

80

IFOMIS partners in Leipzig

Coordination centre for clinical trials

Competence Network Malignant Lymphomas

Project group HaematoWork (Dynamic knowledge based Workflow Adaption)

Research project WISMA (Ontology-based Clinical Trial Management)

81

WISMA Motivation and Objectives

Faster and effective registration of clinical trial data

Improvement of data quality

Improvement of existing communication infrastructures

Support clinical trial management in regard to: Information Transactions

Definitions and Interpretations

Evaluation

better feedback and collaboration

higher quality

82

Competence Network Malignant Lymphomas

Established clinical trial groups

Hodgkin-Lymphomas

High-malignant Non-Hodgkin-Lymphomas

Low-malignant Non-Hodgkin-Lymphomas

with over 30 protocols

with up to 300 clinics/practitioners

about 10,000 new cases per year

data input,requests

information,acknowledgement

information and communication portal

notificationdocumentation

depositionsdocuments

participants'specific query

notificationfindings

material

clinical trial centres

clinical trial groups:

HD lgNHL hgNHL CLL

security area

DB DB

security areaDB directory

communication and information centre

• diverse information• software tools• therapy handbook• material database

partners

sp 1 sp3 sp4 sp5 sp9

security area

DB DB DB DB DBDBDB

participants

patients and relatives, hospitals, clinics and oncology specialistspathologists and radiotherapists

imagesjudgement

84

How to test a philosophical theory empirically

85

The Tests

Uniform 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 dictionary

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

86

The GoalsUniform top-level ontology for testing in medical domain

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

87

Measures of SuccessUniform 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

88

The End