1. Representing the UMLS Semantic Network using OWL
Vipul Kashyap1 and Alex Borgida2
1 LHCNBC, National Library of Medicine, 8600 Rockville Pike, Bethesda, MD 20894
2 Department of Computer Science, Rutgers University, New Brunswick, NJ 08903
Seminar Prinzipien des Ontological Engineering Leipzig,
Kristin Lippoldt

2. 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)

3. The UMLS Semantic Network
nodes = semantic types
links = semantic relationships
two high level is-a hierarchies Entity, Event
is-a hierarchie of relationships physically_related_to, spatially_related_to, temporally_related_to, functionally_related_to, conceptually_related_to
functionally_related_to
affects
is-a
manages
is-a

4. The UMLS Semantic Network (excerpt)

5. 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)
SN semantic type = DL primitive concept = OWL class
SN relationship = DL primitive role = OWL object property

6. 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>
SN semantic type = DL primitive concept = OWL class
SN relationship = DL primitive role = OWL object property

7. 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
- Pilz ist Unterklasse von Organismus
mit OWL properties kann die Relationshierarchie umgesetzt werden
adjacent = angrenzend

8. Semantics of a „link“ in the UMLS SN
Bacteria
causes
Infection
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
(causes) ≡ causes.T
 alle verursachenden Top-Konzepte (linke Seite)
(causes) ≡ causes.T
 alle verursachten Top-Konzepte (rechte Seite)

9. 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 i1 b2 i2 b3 i3 b4

10. 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) i1 b2 i2
die Entitäten, die verursachen ist eine Subklasse der Klasse der Bacteria
die Entitäten, die verursacht werden ist eine Subklasse der Klasse der Infection b3 i3 b4

11. 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! y1 i1
Bacteria ist eine Subklasse der Entitäten, die verursachen
Infection ist eine Subklasse der Entitäten, die verursacht werden b2 i2 b3 i3 b4 x1

12. 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! y1 i1
Bacteria ist eine Subklasse der Entitäten, die verursachen
Infection ist eine Subklasse der Entitäten, die verursacht werden b2 i2 b3 i3 b4 x1

13. 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! y1 i1
A non-bacteria can still cause a non-infection! b2 i2 b3 i3 b4

14. Interpretation 6: All/Each
axiom: Bacteria  causes.Infection
Similar to a cross product, but
A bacteria can still cause a non-infection! i1
Bacteria   Infection.causes
jedes Bakterium verursacht alle Instanzen der Klasse Infektion b2 i2 b3 i3 b4 x1

15. 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! y1 i1
Durchschnitt b2 i2 b3 i3 b4 x1

16. 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! y1 i1
Schnittmenge zwischen Bacteria und allen Dingen, die Infektionen verursachen b2 i2 b3 i3 b4 x1

17. Summary of Interpretations
equals: causes.T ≡ Bacteria, causes.T ≡ Infection
subsumed: causes.T  Bacteria, causes.T  Infection
subsumes: Bacteria  causes.T, Infection  causes.T
all/some: Bacteria  causes.Infection
all/only: Bacteria  causes.Infection
all/each: Bacteria  causes.Infection
some/some:  1 (Bacteria  causes.Infection)
some/all:  1 (Bacteria  causes.Infection)

18.  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 = PhysiologicFunction D = Animal
equals: no support of inheritance , A ≡ C
subsumed: no support of inheritance
alle verursachenden Entitäten sind äquivalent zu A und ebenfalls zu C, also muss A äquivalent zu C sein
C ist subconcept von A, alle verursachenden Entitäten sind subconcept von A, daraus folgt aber nicht, dass alle verursachenden Entitäten ebenfalls subconcept von C sind A C
process_of.T

19.  and  Inheritance process_of.B subsumes: supports both
process_of.T
process_of-.T
subsumes: supports both
all/some: supports  inheritance, but not  inheritance
all/only: supports  inheritance, but not  inheritance A B C D
process_of.B
process_of-.D A B
4) C = Staphylokokken, aber D = Streptokokkeninfektion
5 ist genauso wie 4 C D
process_of.B A C

20.  and  Inheritance process_of. D process_of. B
all/each: supports both
some/some: no support of inheritance
some/all: doesn’t supports  inheritance, but  inheritance
process_of. B A C

21. Blocking of Inheritance
Example:
Process_of(BiologicFunction,Organism)
Process_of(MentalProcess,Plant)
Modifying axioms:
subsumes: P(A,B) C1  A and D1  B
A  C1  (P) and B  D1  (P)

22. Polymorphic Relations
Ergebnis
Interpretation
Encoding
/ Inheritance
Inheritance Blocking
Polymorphic Relations
/ equals
(P)  A
(P)  B
No/No
N/A No
/ subsumed
(P)  A
(P)  B
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
Modification
some / some
1(A  P.B)
some / all
1(AP.B)
No/Yes
all / each
A  P.B

23. 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

24. 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

25. Unterstützung der intendierten Anwendung
Ist es wichtig Inkonsistenzen zu erkennen?
Was sind Inkonsistenzen?
Wird die Kodierung diese Inkonsistenzen erkennen?

26. 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

27. 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

28. 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
Use of OWL, RDF for improvement in Medical Information Retrieval