Representingthe UMLS Semantic Network

using OWL

Vipul Kashyap1 and Alex Borgida2

1 LHCNBC, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 208942 Department of Computer Science, Rutgers University, New Brunswick, NJ 08903

Seminar Prinzipien des Ontological Engineering Leipzig, 15.01.2004

Kristin Lippoldt

Email: kristin.lippoldt@imise.uni-leipzig.de

Outline

• The UMLS Semantic Network (SN)

• Representation of SN using OWL

• Multiple interpretations of „link“

• Evaluation of the interpretation variants

• Methodology for choosing the „right“ representation variant (first steps)

The UMLS Semantic Network

• nodes = semantic types• links = semantic relationships• two high level is-a hierarchies

Entity, Event

• is-a hierarchie of relationshipsphysically_related_to, spatially_related_to, temporally_related_to, functionally_related_to, conceptually_related_to

functionally_related_to

affects

manages

is-a

is-a

The UMLS Semantic Network (excerpt)

OrganismAttribute

AnatomicalStructure

EmbryonicStructure

AnatomicalAbnormality

CongenitalAbnormality

AcquiredAbnormality

Fully FormedAnatomical

Structure

Finding

Laboratory orTest Result

Sign orSymptom

BodySubstance

Body System

part of

part of

part of part of

part of

Body Part, Organ orOrgan Component

Tissue Cell CellComponent

Gene orGenome

Injury orPoisoning

property of

evaluation of

Body Spaceor Junction

conceptualpart of

Body Locationor Region

conceptualpart of

produces,contains

disrupts

disrupts

process of

conceptualpart of

evaluation of

isa linksnon-isa relations

conceptualpart of

BiologicFunction

PhysiologicFunction

Organ orTissue

Function

CellFunction

MolecularFunction

OrganismFunction

GeneticFunction

MentalProcess

PathologicFunction

Cell orMolecular

Dysfunction

Experimentalmodel

of Disease

Disease orSyndrome

Mental orBehavioral

Dysfunction

NeoplasticProcess

location of

adjacent to

location of

co-occurs with

Organism

Alga

Fungus Virus Rickettsiaor

Chlamydia

Bacterium Animal

Invertebrate Vertebrate

Amphibian Bird Fish

PlantArchaeon

ReptileMammal

Human

OrganismAttribute

AnatomicalStructure

EmbryonicStructure

AnatomicalAbnormality

CongenitalAbnormality

AcquiredAbnormality

Fully FormedAnatomical

Structure

Finding

Laboratory orTest Result

Sign orSymptom

Laboratory orTest Result

Sign orSymptom

BodySubstance

Body System

part of

part of

part of part of

part of

Body Part, Organ orOrgan Component

Tissue Cell CellComponent

Gene orGenome

Injury orPoisoningInjury orPoisoning

property of

evaluation of

Body Spaceor Junction

conceptualpart of

Body Locationor Region

conceptualpart of

produces,contains

disrupts

disrupts

process of

conceptualpart of

evaluation of

isa linksnon-isa relationsisa linksnon-isa relations

conceptualpart of

BiologicFunction

PhysiologicFunction

Organ orTissue

Function

CellFunction

MolecularFunction

OrganismFunction

GeneticFunction

MentalProcess

PathologicFunction

Cell orMolecular

Dysfunction

Experimentalmodel

of Disease

Disease orSyndrome

Mental orBehavioral

Dysfunction

NeoplasticProcess

Mental orBehavioral

Dysfunction

NeoplasticProcess

location of

adjacent to

location of

co-occurs with

Organism

Alga

Fungus Virus Rickettsiaor

Chlamydia

Bacterium Animal

Invertebrate Vertebrate

Amphibian Bird Fish

PlantArchaeon

ReptileMammal

Human

Organism

Alga

Fungus Virus Rickettsiaor

Chlamydia

Bacterium Animal

Invertebrate Vertebrate

Amphibian Bird Fish

PlantArchaeon

ReptileMammal

Human

Mammal

Human

OWL

• Web Ontology Language• Based on DAML+OIL• Description of classes, properties (e.g. relations

between classes (e.g. disjointness), cardinality (e.g. "exactly one"))

• Sublanguages:– OWL Lite (lower formal complexity than OWL DL, only

cardinality values of 0 or 1)– OWL DL (maximum expressiveness, computational

completeness )– OWL Full (maximum expressiveness, syntactic freedom of

RDF with no computational guarantees)

Description Logic - OWL

Bacterium ODER Virus

<owl:Class>

<owl:unionOf rdf:parseType=“Collection”>

<owl:Class rdf:about=“#Bacterium”/>

<owl:Class rdf:about=“#Virus”/>

</owl:unionOf>

</owl:Class>

Representation of SN using OWL

• Semantic Types OWL classes– Fungus Organism– Virus Organism

• Semantic Relationships OWL properties– part_of physically_related_to– affects functionally_related_to

• Properties of Semantic Network Relationships– Asymmetric relationships

• has_part ≡ part_of

– Symmetric relationships• adjacent_to ≡ adjacent_to

Semantics of a „link“ in the UMLS SN

Two operators and :

(causes) = { x Bacteria (y)(y Infection causes(x,y)) }DL notation: (causes) ≡ causes.T

(causes) = { y Infection (x)(x Bacteria causes(x,y)) }DL notation: (causes) ≡ causes.T

Bacteria Infectioncauses

Interpretation 1: / equals

• axioms: causes.T ≡ Bacteria, causes.T ≡ Infection

• All Bacteria have to “cause” and all Infections have to“be-caused” (no others can participate in “causes”)

b1 i1b2 i2

b3 i3b4

Interpretation 2: / subsumed

• axioms: causes.T Bacteria, causes.T Infection

• Not all bacteria need to “cause” not all infections have to “be-caused” (However no others can participate)

i1b2 i2

b3 i3b4

Interpretation 3: / subsumes

• axioms: Bacteria causes.T, Infection causes.T

• All bacterias have to “cause” and all infections have to “be-caused”, but – A bacteria can cause a “non-infection” as well!– A “non-bacteria” can cause an infection as well!

i1b2 i2

b3 i3b4

x1

y1

Interpretation 4: All/Some

• axiom: Bacteria causes.Infection

• All bacteria must “cause” some infection, but– A bacteria can cause a “non-infection” as well!– A “non-bacteria” can cause an infection as well!

i1b2 i2

b3 i3b4

x1

y1

Interpretation 5: All/Only

• axiom: Bacteria causes.Infection

• All bacteria, if they “cause”, can cause only infections, but– Not all bacteria have to participate in the “causes”

relationship– A non-bacteria can still cause an infection!

i1b2 i2

b3 i3b4

y1

Interpretation 6: All/Each

• axiom: Bacteria causes.Infection

• Similar to a cross product, but– A bacteria can still cause a non-infection!

i1b2 i2

b3 i3b4

x1

Interpretation 7: Some/Some

• axiom: 1 (Bacteria causes.Infection)

• There is at least one bacteria that “causes” at least one infection, but– A bacteria can still cause a non-infection!– A non-bacteria can still cause an infection!

i1b2 i2

b3 i3b4

x1

y1

Interpretation 8: Some/Each

• axiom: 1 (Bacteria causes.Infection)

• There is at least one bacteria that “causes” all infections, but– A bacteria can still cause a non-infection!– A non-bacteria can still cause an infection!

i1b2 i2

b3 i3b4

x1

y1

Summary of Interpretations

1) equals: causes.T ≡ Bacteria, causes.T ≡ Infection

2) subsumed: causes.T Bacteria, causes.T Infection

3) subsumes: Bacteria causes.T, Infection causes.T

4) all/some: Bacteria causes.Infection

5) all/only: Bacteria causes.Infection

6) all/each: Bacteria causes.Infection

7) some/some: 1 (Bacteria causes.Infection)

8) some/all: 1 (Bacteria causes.Infection)

and Inheritance

inheritance P(A,B) C A P(C,B)

inheritance P(A,B) D B P(A,D)

Example: process_of(BiologicFunction,Organism)C = PhysiologicFunctionD = Animal

1) equals: no support of inheritance , A ≡ C

2) subsumed: no support of inheritance A

C process_of.T

and Inheritance

3) subsumes: supports both

4) all/some: supports inheritance,but not inheritance

5) all/only: supports inheritance,but not inheritance

A

C

process_of.T

B

D

process_of-.T

A

C

process_of.B

B

D

process_of-.D

process_of.B

A

C

and Inheritance

6) all/each: supports both

7) some/some: no support of inheritance

8) some/all: doesn’t supports inheritance, but inheritance

A

C

process_of. B

process_of. D

Blocking of Inheritance

Example:Process_of(BiologicFunction,Organism)Process_of(MentalProcess,Plant)

Modifying axioms:

subsumes: P(A,B)C1 A and D1 BA C1 (P) and B D1

(P)

Ergebnis

Interpretation Encoding /Inheritance

Inheritance Blocking PolymorphicRelations

/ equals (P) A (P) B No/No N/A No

/ subsumed (P) A(P) B No/No N/A Missed model

/ subsumes A (P) B (P) Yes/Yes

Exceptions + compensation

Unintended model

all / some A P.B Yes/No Exception in axiom ok

all / only A P.B Yes/No Exception in axiom Modification

some / some 1(A P.B) No/No N/A ok

some / all 1(AP.B) No/Yes Exception in axiom ok

all / each A P.BYes/Yes

Exceptions + compensation

ok

Methodologie für die Kodierung von Wissen im Semantic Web

• Wahl der Kodierung– Unterstützung von Inferenz– Unterstützung der intendierten Anwendung– Nachvollziehbares Domänenmodell– Repräsentation in der Ontologiesprache

Unterstützung von Inferenzen

• Welche Kodierung unterstützt Inferenz?– All/each und subsumes

• Unterstützt die Kodierung nicht-intendierte Inferenzen?– Some/some unterstützt Aufwärts-Vererbung von Links

• Kann etwas aus der Abwesenheit eines Links geschlussfolgert werden?– A P. B verbietet nicht, dass A in Relation zu B

steht

Unterstützung der intendierten Anwendung

• Ist es wichtig Inkonsistenzen zu erkennen?

• Was sind Inkonsistenzen?

• Wird die Kodierung diese Inkonsistenzen erkennen?

Nachvollziehbarkeit des Domänenmodells

• Konzepte sind Kollektionen von Instanzen– Causes(Bacteria,Infection)

• Was ist die intuitive Kodierung?– All/some and all/only wird von medizinischen

Ontologien genutzt– All/each und some/some wurden abgelehnt

• Gibt es alternative Interpretationen?– Aber: all/each erfüllt alle UMLS SN Anforderungen

Repräsentation in der Ontologiesprache

• Grenzen von OWL– Negation und Disjunktion von Rollen– Kardinalität von Konzepten

• Kann man weniger „teure“ Konstrukte verwenden?– Ressourcen fließen in die Komplexität der DL

Operatoren

Conclusions and Future Work

• Experiences in representing a real world “ontology”, the UMLS Semantic Network

– Has been used very successfully– Requirements: / inheritance, inheritance blocking, polymorphic relationships

• Presented multiple interpretations and encodings and evaluated their support for the UMLS Semantic Network requirements

– Ontology developers and encoders on the Semantic Web might encounter similar requirements and possible encodings

• Identified criteria for choosing between the various encodings– First steps towards a methodology which might be useful to ontology developers

• Ongoing and Future Work– Semantic Vocabulary Interoperation Project

• http://cgsb2.nlm.nih.gov/~kashyap/projects/SVIP – Use of OWL, RDF for improvement in Medical Information Retrieval