Ontology - ICMCC

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Ontology - ICMCC

Ten Theses on Biomedical

Ontologies and Terminology

Systems

Stefan Schulz, Holger Stenzhorn

Freiburg University Hospital

Department of Medical Informatics

Medical Language and Ontology Group (MediLOG)

ICMCC

the international council on medical & care compunetics


Semantic Interoperability

Health

Care

Consumers

Enables

understanding

between human

and computational

agents

Public

Health

Biomedical

Research

Common language: Ontologies and Terminology Systems


Literature on Biomedical

Terminologies and Ontologies

2500

2000

1500

1000

Terminolog*

Ontolog*

500

0

1990

1992

1994

1996

1998

2000

2002

2004

2006


Definitions

bla bla bla

• Terminology System

• Set of terms representing the system of

concepts of a particular subject field.

(ISO 1087)

Ontology / Formal Ontologies

Ontology is the study of what there is.

• Formal ontologies are theories that attempt to

give precise mathematical formulations of the

properties and relations of certain entities.

(Stanford Encyclopedia of Philosophy)


Purpose of this talk

• Make clear the differences between

Terminology Systems and Ontologies

• Propose ten theses to characterize the

nature, the rationale, and the limitations of

(clinical, biomedical) ontologies

• Initiate discussion about

• Preference for terminologies or (formal)

ontologies in practical applications

• How to “ontologize” existing terminologies

• Co-existence between terminologies and

ontologies


I. Terminology

systems

provide

semantic

reference


Semantic Reference

Entities of

Language

(Terms)

Shared /

Meanings /

Entities of

Thought

(Concepts)

„benign neoplasm of heart“

„gutartige Neubildung des Herzmuskels”

“neoplasia cardíaca benigna”


Example: UMLS (mrconso table)

Shared /

Meanings /

Entities of

Thought

Entities of

Language

(Terms)

C0153957|ENG|P|L0180790|PF|S1084242|Y|A1141630||||MTH|PN|U001287|benign neoplasm of heart|0|N||

C0153957|ENG|P|L0180790|VC|S0245316|N|A0270815||||ICD9CM|PT| 212.7|Benign neoplasm of heart|0|N||

C0153957|ENG|P|L0180790|VC|S0245316|N|A0270817||||RCD|SY|B727.| Benign neoplasm of heart|3|N||

C0153957|ENG|P|L0180790|VO|S1446737|Y|A1406658||||SNMI|PT| D3-F0100|Benign neoplasm of heart, NOS|3|N||

C0153957|ENG|S|L0524277|PF|S0599118|N|A0654589||||RCDAE|PT|B727.|Benign tumor of heart|3|N||

C0153957|ENG|S|L0524277|VO|S0599510|N|A0654975||||RCD|PT|B727.| Benign tumour of heart|3|N||

C0153957|ENG|S|L0018787|PF|S0047194|Y|A0066366||||ICD10|PS|D15.1|Heart|3|Y||

C0153957|ENG|S|L0018787|VO|S0900815|Y|A0957792||||MTH|MM|U003158|Heart |0|Y||

C0153957|ENG|S|L1371329|PF|S1624801|N|A1583056|||10004245|MDR|LT|10004245|Benign cardiac neoplasm|3|N||

C0153957|GER|P|L1258174|PF|S1500120|Y|A1450314||||DMDICD10|PT| D15.1|Gutartige Neubildung: Herz|1|N||

C0153957|SPA|P|L2354284|PF|S2790139|N|A2809706||||MDRSPA|LT| 10004245|Neoplasia cardiaca benigna|3|N||

Unified Medical Language System, Bethesda, MD: National Library of Medicine, 2007:

http://umlsinfo.nlm.nih.gov/


Example: UMLS (mrrel table)

Shared /

Meanings /

Entities of

Thought

Shared /

Meanings /

Entities of

Thought

C0153957|A0066366|AUI|PAR|C0348423|A0876682|AUI |

|R06101405||ICD10|ICD10|||N||

C0153957|A0066366|AUI|RQ |C0153957|A0270815|AUI |default_mapped_ from|R03575929||NCISEER|NCISEER|||N||

C0153957|A0066366|AUI|SY |C0153957|A0270815|AUI |uniquely_mapped_ to |R03581228||NCISEER|NCISEER|||N||

C0153957|A0270815|AUI|RQ |C0810249|A1739601|AUI |classifies | R00860638||CCS|CCS|||N||

C0153957|A0270815|AUI|SIB|C0347243|A0654158|AUI |

|R06390094 || ICD9CM|ICD9CM|

C0153957|A0270815|CODE|RN|C0685118|A3807697|SCUI |mapped_to | R15864842||SNOMEDCT|SNOMEDCT||Y|N||

C0153957|A1406658|AUI|RL |C0153957|A0270815|AUI |mapped_from | R04145423||SNMI|SNMI|||N||

C0153957|A1406658|AUI|RO |C0018787|A0357988|AUI |location_of | R04309461||SNMI|SNMI|||N||

C0153957|A2891769|SCUI|CHD|C0151241|A2890143|SCUI|isa |R19841220|47189027|SNOMEDCT|SNOMEDCT

Semantic relations


Example: UMLS

Shared /

Meanings /

Entities of

Thought

Shared /

Meanings /

Entities of

Thought

C0153957|A0066366|AUI|PAR|C0348423|A0876682|AUI |

|R06101405||ICD10|ICD10|||N||

C0153957|A0066366|AUI|RQ |C0153957|A0270815|AUI |default_mapped_ from|R03575929||NCISEER|NCISEER|||N||

C0153957|A0066366|AUI|SY |C0153957|A0270815|AUI |uniquely_mapped_ to |R03581228||NCISEER|NCISEER|||N||

C0153957|A0270815|AUI|RQ |C0810249|A1739601|AUI |classifies | R00860638||CCS|CCS|||N||

C0153957|A0270815|AUI|SIB|C0347243|A0654158|AUI |

|R06390094 || ICD9CM|ICD9CM|

C0153957|A0270815|CODE|RN|C0685118|A3807697|SCUI |mapped_to | R15864842||SNOMEDCT|SNOMEDCT||Y|N||

C0153957|A1406658|AUI|RL |C0153957|A0270815|AUI |mapped_from | R04145423||SNMI|SNMI|||N||

C0153957|A1406658|AUI|RO |C0018787|A0357988|AUI |location_of | R04309461||SNMI|SNMI|||N||

C0153957|A2891769|SCUI|CHD|C0151241|A2890143|SCUI|isa |R19841220|47189027|SNOMEDCT|SNOMEDCT

INFORMAL

Semantic relations


II. Ontologies are

taxonomies of

semantic types that

support the

organization of

domain entities


Organizing Entities

Entity Types

The type

“benign

neoplasm of

heart”

Entities of

Language

The string

„benign neoplasm of heart“

Entities of

the World

My benign

neoplasm of

heart


Organizing Entities

represents

abstract

Entity Types

Universals, classes,

(Concepts)

The type

“benign

neoplasm of

heart”

Entities of

Language

Terms, names

The string

„benign neoplasm of heart“

represents

concrete

Entities of

the World

Particulars,

instances

Instance_of

The benign

neoplasm of my

heart


Organizing Entities

(the complication

of my)

benign heart tumor

(die

Komplikation

meines)

Gutartigen

Herztumors

represents


Organizing Entities

represents

(the)

benign heart tumor

(is congenital)

Terms, names

(die

Komplikation

meines)

Gutartigen

Herztumors


Organizing Entities

Entities of

Language

…are stored in dictionaries


Organizing Entities

Database systems store references to…

Entities of

the World


Organizing Entities

Entity Types

… are organized in ontologies


• Taxonomy: relates types and subtypes:

• is_a (Tumor of Heart, Tumor) equivalent to:

• ∀x: instance_of(x, Tumor of Heart) ⇒ instance_of(x,

Tumor)

• Relations*:

• instance_of relates instances with types, all others relate

instances (e.g. part_of) or are derived from them (e.g.

is_a)

• Definitions: formally describe what is always

true for all instances of a type

• ∀x: instance_of(x, Tumor of Heart) ⇒

∃y: instance_of(y, Heart) ∧ has_location(x,y)

* Smith B, Ceusters W, Kohler J, Kumar A, Lomax J, Mungall CJ, Neuhaus F, Rector A, Rosse C (2005) Relations in Biomedical

Ontologies. Genome Biology, 6(5)

Hierarchical framework for entity

types


Type / Subtype Hierarchy

Benign

Tumor

Tumor

of Heart

Is_a Is_a Is_a

Benign

Tumor

of Heart

Malignant

Tumor

of Heart


III. Ontologies

represent

“universal truths*”

*Assertions that are uncontroversially accepted and very rarely subject to change in a

domain, regardless of the philosophical question whether universal truths exist or whether we can

know them


Continuum of knowledge

Universally accepted

assertions

Consolidated but contextdependent

facts

Hypotheses, beliefs,

statistical associations

Domain Knowledge


Ontology !

Universally accepted

assertions

Consolidated but contextdependent

facts

Hypotheses, beliefs,

statistical associations

Domain Knowledge


Ontologies: Limitations

• Represent only shared, uncontroversial meaning

of a domain vocabulary

• Makes universal (not probabilistic) statements

about instances of a type:

• All Xs are Ys

∀x: instance_of(x, X) ⇒ instance_of(x, Y)

• For all Xs there is some Y

∀x: instance_of(x, X) ⇒∃y:…

• Properties of types are properties of all entities

that instantiate these types (strict inheritance)


IV. Ontology types

extend to classes of

world entities


Hierarchies, Types and

Classes


Hierarchies, Types and

Classes

Ontology

Type 1

Subtype

1.1

Is_a Is_a Is_a

Subtype

1.2

Subtype

1.3

World


Hierarchies, Types and

Classes

Ontology

Type 1

Subtype

1.1

Is_a Is_a Is_a

Subtype

1.2

Subtype

1.3

World

Class 1

Subclass 1.1 Subclass 1.2 Subclass 1.3


V. Ontologies organize

individual entities, not

concepts


… no direct reference to entities

of language or thought

Ontology

Type 1

Subtype

1.1

Is_a Is_a Is_a

Subtype

1.2

Subtype

1.3

World

Class 1

Subclass 1.1 Subclass 1.2 Subclass 1.3


Misconceptions in Ontologies

Ontology

SNOMED-

Concept

Is_a Is_a Is_a

Process

Object

Qualities

World


Misconceptions in Ontologies

Ontology

SNOMED-

Concept

Is_a Is_a Is_a

World

Process

?

Instance

Of

Object

Qualities

My left

hand


Don’t mix Ontology with

Epistemology

O: Bodenreider, B: Smith, and A: Burgun. The ontology-epistemology divide: A case study in

medical terminology. In Achille C. Varzi and Laure Vieu, editors, Formal Ontology in

Information Systems. Proceedings of the 3rd International Conference - FOIS 2004, pages

185–195. Amsterdam etc.: IOS Press, 2004 .

Ontology

Infectious

disease

Is_a Is_a Is_a

Bacterial

infection

Viral

infection

Infection of

unknown origin

World


VI. Ontologies

represent what is,

information models

represent what we

know about


Difference

Ontologies / Information Models

Ontology

“what is”

Is_a

Gender

Quality

Is_a

Male

Gender

Information Model

“what do we know”

(about instances),

circumstances of

observations

The Ontology

provides the

vocabulary for the

information

model

Gender: Female

Male

Unknown

Female

Gender

A. Rector, R. Qamar, T. Marley. Binding Ontologies & Coding Systems to Electronic Health Records and Messages. In: Bodenreider

O, editor. Formal Biomedical Knowledge Representation (KR-MED 2006) CEUR; 2006. p. 11-19.


VII. Practical

requirements

may justify

controlled

deviations from

the “true path”


Ontology in practice:

compromises required

• Example: Risk factors:

• A is a risk factor for B: Being risk factor for B is

a role A plays under certain circumstances in

certain populations.

• It means that there is a statistical dependency

of the incidence of B from on the presence of

A in a population

• It is not an ontological property of A: not every

instance of A is associated to some B


@neurist: Ontology in practice:

Risk factor workaround

Contextual

knowledge

particular in context

Entity

Particular

Ontology

proper

Risk factor

State

Risk factor for aneurysm rupture

Disease State

Hypertensive Disease

S. Hanser, M. Boeker, K. Kumpf, P. Bijlenga, S. Schulz. Design of an Ontology on Cerebral Aneurysms: Representing the Conceptual Space of the

@neurIST Project. Medinfo 2007 Congress, 20-24 August 2007, Brisbane, Australia. Accepted for publication.


Beyond the expressiveness of

Formal Ontologies

• Probabilistic knowledge:

• Presence of jaundice in 95% cases of hepatitis

• Default assertions:

• Adult humans have 32 teeth

• Population-based assertions

• Hayfever is a common seasonal disorder in the NL

• Dispositions

• Gleevec® cures CML

• Aspirin® irritates the Gastric Mucosa

Beyond the expressiveness of ontology

languages for formal reasoning (e.g. OWL-DL)


Logical Foundations of Ontologies


VIII. Ontologies need to

be linked to vocabularies


Ontologies and Vocabularies

• Vocabularies: Lists of terms with defined

meaning in a domain

• Human-readable Labels are commonly

used for unambiguously characterizing

classes in an ontology

• Labels should not be mistaken for domain

terms:

• Domain terms may be ambiguous

• Synonyms are common


• Glycerin Kinase

• Glycerokinase

• GK

•Glyzerinkinase


IX. Ontology users need

not to see the whole

“engine”


The user perspective: Ontology as

logic based terminology service

Ontology

Terminology Service

User

individual term

controlled term and ID

Ontology

Interface

blood das Blut

blood alcohol [med.] adj. der Blutalkohol [Abk.: BAK]

blood-shot adj. blutunterlaufen

blood-soaked adj. blutgetränkt

bloodbath also: blood bath das Blutbad

bloodletting also: blood-letting [med.] der Aderlass

Verben und Verbzusammensetzungen

to curdle the blood das Blut in den Adern erstarren lassen

to curdle one's blood das Blut in den Adern erstarren lassen

to draw blood eine blutende Wunde verursachen

to give blood Blut spenden

to run in the blood im Blut liegen

to shed blood Blut vergießen

to spill blood Blut vergießen

to supply with blood durchbluten

to take a blood sample from so. jmdm. Blut abnehmen

to take a blood sample from so. jmdm. Blut entnehmen

Wendungen und Ausdrücke

in cold blood kaltblütig

in cold blood ohne Gefühlsregung

of royal blood aus königlichem Stamm

related by blood blutsverwandt

stained with blood blutbefleckt

one's own flesh and blood jmds. eigen Fleisch und Blut

So.'s blood runs cold. Es läuft jmdm. eiskalt den Rücken hinunter.

Blood is thicker than water. Blut ist dicker als Wasser.

Blood is thicker than water. Das Hemd ist näher als der Rock.

Zusammengesetzte Einträge

bad blood böses Blut

bad blood der Groll

bad blood die Meinungsverschiedenheiten Pl.

blood alcohol [med.] die Blutalkoholkonzentration [Abk.: BAK]

blood and thunder Mord und Totschlag

blood arrest [med.]

term

semantically related terms

Ontology

Term repository

(Vocabulary)

User: constructs information

models and

database schema,

e.g. for the acquisition

of patient data


X. Ontology in

practice should

support

tailored logicbased

terminology

services


Vocabularies

(lists of terms)

Ontology

(hierarchy of types)

Information

Models

Patient

Data

blood das Blut

blood alcohol [med.] adj. der Blutalkohol [Abk.: BAK]

blood-shot adj. blutunterlaufen

blood-soaked adj. blutgetränkt

bloodbath also: blood bath das Blutbad

bloodletting also: blood-letting [med.] der Aderlass

Verben und Verbzusammensetzungen

to curdle the blood das Blut in den Adern erstarren lassen

to curdle one's blood das Blut in den Adern erstarren lassen

to draw blood eine blutende Wunde verursachen

to give blood Blut spenden

to run in the blood im Blut liegen

to shed blood Blut vergießen

to spill blood Blut vergießen

to supply with blood durchbluten

to take a blood sample from so. jmdm. Blut abnehmen

to take a blood sample from so. jmdm. Blut entnehmen

Wendungen und Ausdrücke

in cold blood kaltblütig

in cold blood ohne Gefühlsregung

of royal blood aus königlichem Stamm

related by blood blutsverwandt

stained with blood blutbefleckt

one's own flesh and blood jmds. eigen Fleisch und Blut

So.'s blood runs cold. Es läuft jmdm. eiskalt den Rücken hinunter.

Blood is thicker than water. Blut ist dicker als Wasser.

Blood is thicker than water. Das Hemd ist näher als der Rock.

Zusammengesetzte Einträge

bad blood böses Blut

bad blood der Groll

bad blood die Meinungsverschiedenheiten Pl.

blood alcohol [med.] die Blutalkoholkonzentration [Abk.: BAK]

blood and thunder Mord und Totschlag

blood arrest [med.]


Vocabularies

(lists of terms)

Terminology

Systems

Ontology

(hierarchy of types)

Information

Models

Patient

Data

blood das Blut

blood alcohol [med.] adj. der Blutalkohol [Abk.: BAK]

blood-shot adj. blutunterlaufen

blood-soaked adj. blutgetränkt

bloodbath also: blood bath das Blutbad

bloodletting also: blood-letting [med.] der Aderlass

Verben und Verbzusammensetzungen

to curdle the blood das Blut in den Adern erstarren lassen

to curdle one's blood das Blut in den Adern erstarren lassen

to draw blood eine blutende Wunde verursachen

to give blood Blut spenden

to run in the blood im Blut liegen

to shed blood Blut vergießen

to spill blood Blut vergießen

to supply with blood durchbluten

to take a blood sample from so. jmdm. Blut abnehmen

to take a blood sample from so. jmdm. Blut entnehmen

Wendungen und Ausdrücke

in cold blood kaltblütig

in cold blood ohne Gefühlsregung

of royal blood aus königlichem Stamm

related by blood blutsverwandt

stained with blood blutbefleckt

one's own flesh and blood jmds. eigen Fleisch und Blut

So.'s blood runs cold. Es läuft jmdm. eiskalt den Rücken hinunter.

Blood is thicker than water. Blut ist dicker als Wasser.

Blood is thicker than water. Das Hemd ist näher als der Rock.

Zusammengesetzte Einträge

bad blood böses Blut

bad blood der Groll

bad blood die Meinungsverschiedenheiten Pl.

blood alcohol [med.] die Blutalkoholkonzentration [Abk.: BAK]

blood and thunder Mord und Totschlag

blood arrest [med.]


Open questions: Bringing Ontologies

and Terminology Systems Together

• Which use cases require (formal)

ontologies

• In which cases informal terminology

systems are sufficient?

• Which cases require both ?

• Can existing terminologies be

ontologized?

• Can terminologies and ontologies coexist

?


Ten principles

I. Terminology systems provide semantic reference

II. Ontologies are hierarchies of semantic types that support the

organization of domain entities

III. Ontologies represent universal truths

IV. Ontology types extend to classes of world entities

V. Ontologies organize individual entities, not concepts

VI. Ontologies represent what is, information models represent

what we know about

VI. Practical requirements may justify controlled deviations from

the “true path”

VII. Ontologies need to be linked to vocabularies

VII. Ontology users need not to see the whole “engine”

IX. Ontology should provide tailored terminology services


Thank you for your

attention !

stschulz@uni-freiburg.de


Discussion


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


ONTOLOGY: Unresolved

Terminological Confusion…

• Artifacts for ordering domain entities, relating

word meanings or providing semantic reference

◦ Vocabularies

◦ Terminologies

◦ Thesauri

◦ Concept Systems

◦ Classifications

◦ Ontologies


ONTOLOGY: Unresolved

Terminological Confusion…

• Artifacts for ordering domain entities, relating

word meanings or providing semantic reference

◦ Vocabularies

◦ Terminologies

◦ Thesauri

◦ Concept Systems

◦ Classifications

◦ Ontologies


ONTOLOGY: Unresolved

Terminological Confusion…

• Artifacts for ordering domain entities, relating

word meanings or providing semantic reference

◦ Vocabularies

◦ Terminologies

◦ Thesauri

◦ Concept Systems

◦ Classifications

◦Ontologies


ONTOLOGY: Unresolved

Terminological Confusion…

• Different scientific traditions:

Biology, Medicine, Philosophy, Logic,

Linguistics, Library and Information

Science, Computer Science, Cognitive

Science

• Different philosophical schools of

thinking: Platonism, Aristotelian

Realism, Conceptualism, Relativism,

Idealism, Postmodernism,

Constructivism, Nominalism, Tropism,…


Ontologies / Terminological Systems

come in different flavors

Nodes and Links

(In)formal Definitions

domain or region of DNA [GENIA]: A substructure of

DNA molecule which is supposed to have a particular

function, such as a gene, e.g., c-jun gene, promoter

region, Sp1 site, CA repeat. This class also includes a

base sequence that has a particular function.

ExtractionOfForeignBodyFromStomachByIncision ≡

RemovalOfForeignBodyFromDigestiveSystem ∏

RemovalOfForeignBodyFromStomach ∏

IncisionOfStomach ∏

∃ has-part.(∃ Method.RemovalAction ∏

∃ DirectMorphology.ForeignBody) ∏

∃ has-part.(∃ Method.IncisionAction ∏

∃ ProcedureSite.stomachStructure)


What do the nodes in Ontologies /

Terminological Systems stand for?

names

descriptors

entities

terms

categories

classes

types

sorts

universals

sets

synsets

properties

concepts

descriptors


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Purposes of Ontologies: General

• Semantic Interoperability

• Terminology control

• Knowledge extraction

• Knowledge management

• Natural Language Processing

• Document retrieval

• Formal reasoning about knowledge

structures


Purposes of Ontologies: Medicine

• Support of clinical coding (diagnoses,

procedures):

• Accounting

• Health Statistics

• Support of Biomedical Science:

• Interoperability between heterogeneous

databases

• Indexing of biomedical literature


Purposes of Ontologies: Biology

• Data and information retrieval and analysis

• Semantic Annotation of Genes, Proteins in

terms of localization, pathways, functions..

• Intelligent text mining of literature

abstracts: "Bibliomics"


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Mapping the space of Ontology

• instead of providing a definition…


Purposes of Ontologies and

Terminology Systems: General

• Terminology control

• Knowledge extraction

• Knowledge management

• Natural Language Processing

• Document retrieval

• Formal reasoning about knowledge

structures

• Semantic Interoperability


Purposes of Ontologies and

Terminology Systems: Medicine

• Support of clinical coding (diagnoses,

procedures):

• Accounting

• Health Statistics

• Support of Biomedical Science:

• Interoperability between heterogeneous

databases

• Indexing of biomedical literature


Purposes of Ontologies and

Terminology Systems: Biology

• Data and information retrieval and analysis

• Semantic Annotation of Genes, Proteins in

terms of localization, pathways, functions..

• Intelligent text mining of literature

abstracts: "Bibliomics"


ONTOLOGY: Unresolved

Terminological Confusion…

• Artifacts for ordering domain entities, relating

word meanings or providing semantic reference

◦ Vocabularies

◦ Terminologies

◦ Thesauri

◦ Concept Systems

◦ Classifications

◦ Ontologies


ONTOLOGY: Unresolved

Terminological Confusion…

• Artifacts for ordering domain entities, relating

word meanings or providing semantic reference

◦ Vocabularies

◦ Terminologies

◦ Thesauri

◦ Concept Systems

◦ Classifications

◦ Ontologies


ONTOLOGY: Unresolved

Terminological Confusion…

• Artifacts for ordering domain entities, relating

word meanings or providing semantic reference

◦ Vocabularies

◦ Terminologies

◦ Thesauri

◦ Concept Systems

◦ Classifications

◦Ontologies


ONTOLOGY: Unresolved

Terminological Confusion…

• Different scientific traditions:

Biology, Medicine, Philosophy, Logic,

Linguistics, Library and Information

Science, Computer Science, Cognitive

Science

• Different philosophical schools of

thinking: Platonism, Aristotelian

Realism, Conceptualism, Relativism,

Idealism, Postmodernism,

Constructivism, Nominalism, Tropism,…


Ontologies / Terminological Systems

come in different flavors

Nodes and Links

(In)formal Definitions

domain or region of DNA [GENIA]: A substructure of

DNA molecule which is supposed to have a particular

function, such as a gene, e.g., c-jun gene, promoter

region, Sp1 site, CA repeat. This class also includes a

base sequence that has a particular function.

ExtractionOfForeignBodyFromStomachByIncision ≡

RemovalOfForeignBodyFromDigestiveSystem ∏

RemovalOfForeignBodyFromStomach ∏

IncisionOfStomach ∏

∃ has-part.(∃ Method.RemovalAction ∏

∃ DirectMorphology.ForeignBody) ∏

∃ has-part.(∃ Method.IncisionAction ∏

∃ ProcedureSite.stomachStructure)


What do the nodes in Ontologies /

Terminological Systems stand for?

names

descriptors

entities

terms

categories

classes

types

sorts

universals

sets

synsets

properties

concepts

descriptors


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Purposes of Ontologies: General

• Semantic Interoperability

• Terminology control

• Knowledge extraction

• Knowledge management

• Natural Language Processing

• Document retrieval

• Formal reasoning about knowledge

structures


Purposes of Ontologies: Medicine

• Support of clinical coding (diagnoses,

procedures):

• Accounting

• Health Statistics

• Support of Biomedical Science:

• Interoperability between heterogeneous

databases

• Indexing of biomedical literature


Purposes of Ontologies: Biology

• Data and information retrieval and analysis

• Semantic Annotation of Genes, Proteins in

terms of localization, pathways, functions..

• Intelligent text mining of literature

abstracts: "Bibliomics"


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Mapping the space of Ontology

• instead of providing a definition…


Mapping the space of Ontology

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

How is it represented?

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus Taxonomy Glossary Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Mapping the space of Ontology

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies


Common Principles

• (1) There is a mind-independent reality of

objects in the world

• (2) There is (human) knowledge or belief

• Humans use language to exchange

information about (1) and (2)

• Semantically interoperable information

systems should be able to deal with

language, knowledge and reality


Intelligent

Minds

triangle

square

circle

trilateral

rectangle

square

circle

triangle

square

circle

Natural

Language

Mind

Independent

Reality

triangle

square

circle

triangle

square

circle

triangle

square

circle

Individual Entities (Instances, Particulars)


Intelligent

Minds

triangle

square

circle

trilateral

rectangle

square

circle

triangle

square

circle

Natural

Language

Mind

Independent

Reality

triangle

square

circle

triangle

square

circle

triangle

square

circle

Classes

Individual Entities (Instances, Particulars)


Examples from Terminological Systems

Viral Hepatitis

Viral Hepatitis, diagnosed by clinical

examination

Prevented pregnancy

reference

to reality

X

X

reference to

knowledge

Diabetes, not elsewhere classified X X

Brain transplant

Absent toe

Absence of toe

Relative of possible smoker X X

Health Problems related to Mars mission

Yin deficit ? X

X

X

X

X

X

X


Schools of

Thinking

Realist

Conceptualist Nominalist

Categories Universals

Concepts Names

Mind

Independent

Reality

Individual Entities (Instances, Particulars)


Mapping the space of Ontology:

Realist perspective

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

• Universals (types, kinds) are

invariants in reality, e.g. cell,

molecule, eye, inflammation,


• All universals refer to nonempty

(at some moment)

classes of (individual)

entities in the world

Representation

restricted to real

world entities

How is it represented?

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies


Schools of

Thinking

Realist

Conceptualist Nominalist

Categories Universals

Concepts

Names

Mind

Independent

Reality

Individual Entities (Instances, Particulars)


Mapping the space of Ontology

Conceptualist perspective

Representation

of arbitrary

propositions

Representation

of term

meanings

What is represented?

• Concepts do not necessarily imply the

extension to classes in reality

• (“retinal transplant”, “yin deficiency”,

“missing digit”, “prevented pregnancy”)

• Concepts as mind constructs may be

oriented to prototypes, their extension

exhibits large inter-individual variation

• Concepts can be related by imprecise

conceptual relations such as “is broader

as”

Representation

restricted to real

world entities

How is it represented?

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies


Schools of

Thinking

Realist Conceptualist Nominalist

Categories Universals Concepts Names

trilateral

rectangle

square

circle

triangle

square

circle

Reference

to Reality

Mind

Independent

Reality

Individual Entities (Instances, Particulars)


Mapping the space of Ontology

Nominalist perspective

Representation

of arbitrary

propositions

Representation

of term

meanings

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

What is represented?

• Names are are created in an ad hoc

fashion from linguistic predicates.

• Examples:

• “People in SR 1048 at 7pm today”

• “Nontraffic accident involving other off-road

motor vehicle ” (ICD9-CM: E821 )

• Tuberculosis of lung, bacteriological and histological

examination not done (ICD-10: A16.1)

• “Follow-up inpatient consultation for an established patient which

requires at least two of these three key components: a detailed

interval history; a detailed examination; medical decision making

of high complexity. Counseling and/or coordination of care with

other providers or agencies are provided consistent with the

nature of the problem(s) and the patient's and/or family's needs.

Usually, the patient is unstable or has developed a significant

complication or a significant new problem. Physicians typically

spend 30 minutes at the bedside and on the patient's hospital

floor or unit.”

(Current Procedural Terminology Code: HCPT06 )

How is it represented?


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Mapping the space of Ontology

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

How is it represented?

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus Taxonomy Glossary Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Catalogs

Representation

of arbitrary

propositions

What is represented?

Hierarchical Ordering of Semantic Nodes

Representation

of term

meanings

Representation

restricted to real

world entities

How is it represented?

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus Taxonomy Glossary Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Catalogs

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

• Catalog:

a set of terms

without constraints

(formal or informal)

to How characterize is it represented? their

meaning.

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus

Taxonomy

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Glossary

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

• Glossary:

catalogue with

glosses How in is natural it represented?

language.

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus

Taxonomy

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Taxonomy

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

• Taxonomy:

terms (and glosses) are

organized into a

subsumption (subclass)

hierarchy with Property

inheritance

How is it represented?

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus

Taxonomy

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Thesaurus

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

• Thesaurus:

taxonomy coupled with

additional semantic

relations (part-of,

How is it represented?

similar to, etc.).

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus

Taxonomy

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Conceptual Schemas

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

• Conceptual Schema:

Set of terms, attributes and

relations (or frames and

slots) with explicit

descriptions (definitions)

Conceptual

Schema

Thesaurus

Taxonomy

How is it represented?

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology

Axiomatic Theories

Representation

of arbitrary

propositions

What is represented?

Representation

of term

meanings

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

• Axiomatic Theory:

Formal system with a clear

semantics that captures

the meaning of the

adopted vocabulary via

logical formulas.

Strict

Ontologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus

Taxonomy

How is it represented?

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology:

Notions of Ontology

Representation

of arbitrary

propositions

Representation

of term

meanings

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Perspective of Philosophical Ontology

Conceptual

Schema

Thesaurus

Taxonomy

Glossary

Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology:

Notions of Ontology

Representation

of arbitrary

propositions

Representation

of term

meanings

AI

Perspective

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus Taxonomy Glossary Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology:

Notions of Ontology

Representation

of arbitrary

propositions

Representation

of term

meanings

Linguistic /

Cognitive Perspective

Representation

restricted to real

world entities

Gunnar Klein & Barry Smith:

ontology.buffalo.edu/concepts/

ConceptsandOntologies

Axiomatic

Theory

Conceptual

Schema

Thesaurus Taxonomy Glossary Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Mapping the space of Ontology:

Biomedical Vocabularies

Representation

of arbitrary

propositions

Representation

of term

meanings

Representation

restricted to real

world entities

SNOMED CT

WordNet

GO

ICD

MeSH

Axiomatic

Theory

Conceptual

Schema

Thesaurus Taxonomy Glossary Catalog

from Borgo et al. http://www.loa-cnr.it/Tutorials/ESSLLI1.pdf


Content

• A cruise through the O-Space

• The "O-word": Terminological Clarification

• Purposes of Ontologies

• Mapping the O-Space

• What is represented

• How is it represented

• Practice of Good Ontology


Don’t mix up universals (Concepts,

Classes) with individuals (Instances)

• subclass-of (Motor Neuron, Neuron)

(FMA, OpenGALEN)

• is-a (Motor Neuron, Neuron)

• instance_of (Motor Neuron, Neuron) (FlyBase)

But:

• instance_of (my Hand, Hand)

• instance_of (this amount of insulin, Insulin)

• instance_of (Germany, Country)

• not: instance of (Heart, Organ)

• not: instance of (Insulin, Protein)

instance_of

Class

Membership

is-a = subclass-of:

Taxonomic

Subsumption


Keep in mind the meaning of

taxonomic subsumption (is-a)

is-a (A, B) = def

∀ x: instance_of(x, A) → instance_of (x, B)

No subclassing without inheritance!

is-a (Geographic Area, Spatial Concept) (UMLS-SN)

is-a (Spatial Concept, Idea of Concept) (UMLS-SN)

→ instance_of (Freiburg, Idea or Concept) ??

is-a (Protein Family or Group, Protein, ) (GENIA) ??


Don’t use superclasses to express

roles

• is-a (Fish, Animal)

• is-a (Fish, Food) ??

• is-a (Acetylsalicylic Acid, Salicylate)

• is-a (Acetylsalicylic Acid, Analgetic Drug) ??

Be aware of the “rigidity” of classes


Partition the ontology by

principled upper level categories

Example: DOLCE’s Upper Ontology

Endurant (Continuant)

Physical

Amount of matter

Physical object

Feature

Non-Physical

Mental object

Social object


Perdurant (Occurrent)

Static

State

Process

Dynamic

Achievement

Accomplishment

Quality

Physical Qualities

Spatial location


Temporal Qualities

Temporal location


Abstract Qualities


Abstract

Quality region

Time region

Space region

Color region

Source: S. Borgo ISTC-CNR


Endurants vs. Perdurants

• Endurants (Continuants):

• All proper parts are present whenever they are present (wholly

presence, no temporal parts)

• Exist in time

• Can genuinely change in time

• May have non-essential parts

• Need a time-indexed parthood relation

• Perdurants (Occurrents):

• Only some proper parts are present whenever the perdurant is

present (partial presence,temporal parts )

• Happen in time

• Do not change in time

• All parts are essential

• Do not need a time-indexed parthood relation


Be aware of ambiguities

• “Institution” (NCIT)

may refer to

1. (abstract) institutional rules

2. (concrete) things instituted

3. act of instituting sth.

• “Tumor”

1. evolution of a tumor as a disease process

2. having a tumor as a pathological state

3. tumor as a physical object

• “Gene”

1. a (physical) sequence of nucleotides on a DNA chain

2. a collection of (1)

3. A piece of information conveyed by (1)


Use semantically precise

Basic Relations

Barry Smith, Werner Ceusters, Bert Klagges, Jacob Köhler,

Anand Kumar, Jane Lomax, Chris Mungall, Fabian Neuhaus,

Alan L Rector and Cornelius Rosse. Relations in biomedical ontologies.

Genome Biology, 6(5), 2005.


Nontaxonomic Relations between

Classes are ambiguous !

• has-part(Cell, Axon)

(Gene Ontology)

• Do cells without

axons exist ?

• Do axons without

cells exist ?

• has-part(Neuron, Axon)

(FMA)

• Does every neuron

has an axon?


Nontaxonomic Relations between

Classes are ambiguous !

A, B are classes, inst-of = class membership

rel: relation between instances Rel: relation between classes

Rel (A, B) = def

∃x: inst-of (x, A) ∧ inst-of (y, B) ∧ rel (x, y) OR

∀ x: inst-of(x, A) →∃y: inst-of (y, B) ∧ rel (x, y) OR

∀ x: inst-of(x, A) → ∃y: inst-of (y, B) ∧ rel (x, y) AND

∀ y: inst-of(y, B) → ∃x: inst-of (x, A) ∧ rel (x, y)


Nontaxonomic Relations between

Classes are ambiguous !

A, B are classes, inst-of = class membership

rel: relation between instances Rel: relation between classes

Rel (A, B) = def

∃x: inst-of (x, A) ∧ inst-of (y, B) ∧ rel (x, y) OR

∀ x: inst-of(x, A) →∃y: inst-of (y, B) ∧ rel (x, y) OR

∀ x: inst-of(x, A) → ∃y: inst-of (y, B) ∧ rel (x, y) AND

∀ y: inst-of(y, B) → ∃x: inst-of (x, A) ∧ rel (x, y)


Example: Part-of and Has-Part

between Classes


MediLOG: Ontology activities

• Three EU projects

• SemanticMining: Semantic Interoperability and

Data Mining in Biomedicine

• @neurist:

Integrated decision support system to assess

the risk of aneurysm rupture in patients and to

optimize their treatments.

• BootSTREP: Integration of biological fact

databases and terminological repositories to

implement a text analysis system which

continuously increases their coverage by

analyzing biological documents.


MediLOG: Ontology activities

• Three EU projects

• SemanticMining: Semantic Interoperability and

Data Mining in Biomedicine

• @neurist:

Integrated decision support system to assess

the risk of aneurysm rupture in patients and to

optimize their treatments.

• BootSTREP: Integration of biological fact

databases and terminological repositories to

implement a text analysis system which

continuously increases their coverage by

analyzing biological documents.


MediLOG: Ontology activities

• Three EU projects

• SemanticMining: Semantic Interoperability and

Data Mining in Biomedicine

• @neurist:

Integrated decision support system to assess

the risk of aneurysm rupture in patients and to

optimize their treatments.

• BootSTREP: Integration of biological fact

databases and terminological repositories to

implement a text analysis system which

continuously increases their coverage by

analyzing biological documents.


Thank you for your attention


Conclusions

• Many ontology builders and users have a implicitly

promiscuous attitude both towards philosophical

categories and formal properties

• Different application contexts require different styles

of ontologies / terminological systems. There is not

the one purist approach

Ontology builders should avoid pitfalls by adhering

to best practice guidelines

• Every terminology system should have its defined

place in the “ontological space” and have a clear

commitment of what it represents and how

• This is an important requirement for reference

terminologies such as SNOMED


A cruise through the archipelago of

Biomedical Terminologies and Ontologies

BRENDA

ChEBI

GO

MA

FMA

ICD

MedRa

FBcv

MeSH

GALEN

NCI

GENIA

SNOMED

TA

CLGRO

FAO

WordNet


MeSH: Medical Subject Headings

MeSH

Medical Subject Headings

http://www.nlm.nih.gov/mesh/


MeSH: Medical Subject Headings

GO

Gene Ontology

Gene Ontology COnsortium: Creating the Gene Ontology resource:

Design and implementation Genome Research, Vol. 11, pp. 1425-1433,

2001, http://www.geneontology.org


MeSH: Medical Subject Headings

ICD

International Classification

of Diseases

http://www.who.int/classifications/apps/icd/icd10online/


MeSH: Medical Subject Headings

Word NET

Fellbaum, C. WordNet: An Electronic Lexical Database. Cambridge, MA: MIT Press, 1998

http://wordnet.princeton.edu/


MeSH: Medical Subject Headings

SNOMED

Clinical Terms

http://www.ihtsdo.org/


SNOMED CT:

Description Logics Representation:

RemovalOfForeignBodyFromTheStomachByIncision

∃ has-part.(∃ hasProcedureSite. StomachStructure

∃ hasMethod. IncisionAction)

∃ has-part.(∃ hasProcedureSite. StomachStructure

∃ hasDirectMorphology. ForeignBody

∃ hasMethod. RemovalAction)

Schulz S, Hanser S, Hahn U, Rogers J: The Semantics of Procedures

and Diseases in SNOMED CT. Method Inform Med, 2006; 45 (4) : 354-358

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